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4adb7faf12999b59bae7422c640ca04ac9896581 | # Dataset Card for "traininglogoset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samp3209/traininglogoset | [
"region:us"
]
| 2023-01-22T01:28:14+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 530151628.952, "num_examples": 9888}], "download_size": 498332084, "dataset_size": 530151628.952}} | 2023-01-22T01:28:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "traininglogoset"
More Information needed | [
"# Dataset Card for \"traininglogoset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"traininglogoset\"\n\nMore Information needed"
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| [
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|
a03fc3bb20267743f3734ba115acb38331b42167 | # Dataset Card for "528by528logos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samp3209/528by528logos | [
"region:us"
]
| 2023-01-22T01:54:48+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1204173958.858, "num_examples": 8817}], "download_size": 1262219319, "dataset_size": 1204173958.858}} | 2023-01-22T01:56:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "528by528logos"
More Information needed | [
"# Dataset Card for \"528by528logos\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"528by528logos\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"528by528logos\"\n\nMore Information needed"
]
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|
476e2a83fdeb660d996214d2af787aa13296581b | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-sentiment-latest
* Dataset: tweet_eval
* Config: sentiment
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@alhug](https://huggingface.co/alhug) for evaluating this model. | autoevaluate/autoeval-eval-tweet_eval-sentiment-5ae1bf-3003786426 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-01-22T02:16:54+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "cardiffnlp/twitter-roberta-base-sentiment-latest", "metrics": [], "dataset_name": "tweet_eval", "dataset_config": "sentiment", "dataset_split": "validation", "col_mapping": {"text": "text", "target": "label"}}} | 2023-01-22T02:17:33+00:00 | []
| []
| TAGS
#autotrain #evaluation #region-us
| # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-sentiment-latest
* Dataset: tweet_eval
* Config: sentiment
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @alhug for evaluating this model. | [
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-sentiment-latest\n* Dataset: tweet_eval\n* Config: sentiment\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @alhug for evaluating this model."
]
| [
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-sentiment-latest\n* Dataset: tweet_eval\n* Config: sentiment\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @alhug for evaluating this model."
]
| [
13,
98,
15
]
| [
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: cardiffnlp/twitter-roberta-base-sentiment-latest\n* Dataset: tweet_eval\n* Config: sentiment\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @alhug for evaluating this model."
]
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|
8889c4e941110119099d51cd1b45196a609797fb | # Dataset Card for "pii-pile-chunk3-200000-250000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-200000-250000-tagged | [
"region:us"
]
| 2023-01-22T04:08:01+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 508200278, "num_examples": 49999}], "download_size": 194434096, "dataset_size": 508200278}} | 2023-01-22T04:08:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pii-pile-chunk3-200000-250000-tagged"
More Information needed | [
"# Dataset Card for \"pii-pile-chunk3-200000-250000-tagged\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pii-pile-chunk3-200000-250000-tagged\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pii-pile-chunk3-200000-250000-tagged\"\n\nMore Information needed"
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|
cc12b40d966293a4f4db2dd0882231ba14469f11 | # Introduction
I mannually choose 342 images from top-100 in weekly toplist of 2022.
I have to mention that some of painters may not be consent to the use of their art works in AI training.
| ecccho/pixiv-image-toplist-aesthetics | [
"region:us"
]
| 2023-01-22T05:03:08+00:00 | {} | 2023-01-22T05:32:51+00:00 | []
| []
| TAGS
#region-us
| # Introduction
I mannually choose 342 images from top-100 in weekly toplist of 2022.
I have to mention that some of painters may not be consent to the use of their art works in AI training.
| [
"# Introduction\nI mannually choose 342 images from top-100 in weekly toplist of 2022. \nI have to mention that some of painters may not be consent to the use of their art works in AI training."
]
| [
"TAGS\n#region-us \n",
"# Introduction\nI mannually choose 342 images from top-100 in weekly toplist of 2022. \nI have to mention that some of painters may not be consent to the use of their art works in AI training."
]
| [
6,
46
]
| [
"passage: TAGS\n#region-us \n# Introduction\nI mannually choose 342 images from top-100 in weekly toplist of 2022. \nI have to mention that some of painters may not be consent to the use of their art works in AI training."
]
| [
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|
899815f1c41b1578541c28fad40d03c9137eeb08 | # Dataset Card for "fleurs_2_sec_chunks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AMead10/fleurs_2_sec_chunks | [
"region:us"
]
| 2023-01-22T06:54:20+00:00 | {"dataset_info": {"features": [{"name": "audio", "sequence": "float64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3407519773.999594, "num_examples": 13310}, {"name": "test", "num_bytes": 378641754.0004057, "num_examples": 1479}], "download_size": 2183139381, "dataset_size": 3786161528.0}} | 2023-01-30T23:14:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fleurs_2_sec_chunks"
More Information needed | [
"# Dataset Card for \"fleurs_2_sec_chunks\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fleurs_2_sec_chunks\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fleurs_2_sec_chunks\"\n\nMore Information needed"
]
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|
4ddad9920947d6139a57d5f81da94c615bc5a17a | Набор действий происходящих с человеком | solkogan/people_actions | [
"language:ru",
"region:us"
]
| 2023-01-22T09:23:04+00:00 | {"language": ["ru"]} | 2023-01-22T09:26:30+00:00 | []
| [
"ru"
]
| TAGS
#language-Russian #region-us
| Набор действий происходящих с человеком | []
| [
"TAGS\n#language-Russian #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#language-Russian #region-us \n"
]
| [
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|
a890b7bcd63e675e1969da650cd8d7d0d7e61c4c | # Dataset Card for "pii-pile-chunk3-250000-300000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-250000-300000-tagged | [
"region:us"
]
| 2023-01-22T10:21:25+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 526454655, "num_examples": 49999}], "download_size": 201949320, "dataset_size": 526454655}} | 2023-01-22T10:21:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pii-pile-chunk3-250000-300000-tagged"
More Information needed | [
"# Dataset Card for \"pii-pile-chunk3-250000-300000-tagged\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pii-pile-chunk3-250000-300000-tagged\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pii-pile-chunk3-250000-300000-tagged\"\n\nMore Information needed"
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b9ad429b3893eab30321b0aaf319c25a9363d3b3 | # Dataset Card for "patched_1000_test_p_100_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_100_m1_predictions | [
"region:us"
]
| 2023-01-22T15:09:09+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 519712989, "num_examples": 1319717}], "download_size": 50674557, "dataset_size": 519712989}} | 2023-01-22T15:12:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_1000_test_p_100_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_1000_test_p_100_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_1000_test_p_100_m1_predictions\"\n\nMore Information needed"
]
| [
6,
27
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_1000_test_p_100_m1_predictions\"\n\nMore Information needed"
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|
2bf231971572aeb500a72b8df7ad393272c11b56 | # Overview
Converted from these datasets:
> https://huggingface.co/datasets/jordiclive/scored_summarization_datasets -> labeled "data" \
> https://huggingface.co/datasets/jordiclive/wikipedia-summary-dataset -> labeled "wiki" \
wiki dataset is split between "description" (cleaner, more popular articles) and "no description" (less clean, less popular)
Consist of parquet files with cols: `instruction, response, source`
full_to_summary (fts) consist of the following prompts infront of the instruction \
the full text is the instruction in these files
```
full_to_summary = [
"Summarize the following text: {}",
"Make a summary of the following text: {}",
"Provide a summary of the following text: {}",
"Change the following text into a summary: {}",
"Create a summary of the following text: {}",
"Give a brief overview of the following text: {}",
"Condense the following text into a summary: {}",
"Provide a condensed version of the following text: {}",
"Make a brief summary of the following text: {}",
"Create a condensed overview of the following text: {}",
]
```
summary_to_full (stf) consist of the following prompts infront of the instruction \
the summary is the instruction in these files
```
summary_to_full = [
"Write the original text for the following summary: {}",
"Write the full text for the following summary: {}",
"Provide the inputted source that provided the following summary: {}",
"Revert the following summary back into the original text: {}",
"Write a text that could've provided the following summary: {}",
"Write the original text that generated the following summary: {}",
"Provide the full text for the following summary: {}",
"Create the inputted source that provided the following summary: {}",
"Write the original source that provided the following summary: {}",
"Convert the following summary back into the original text: {}",
"Provide a text that could have been the input for the following summary: {}",
]
``` | MyloBishop/reverse_summarization_dataset | [
"region:us"
]
| 2023-01-22T15:15:30+00:00 | {} | 2023-01-22T17:37:24+00:00 | []
| []
| TAGS
#region-us
| # Overview
Converted from these datasets:
> URL -> labeled "data" \
> URL -> labeled "wiki" \
wiki dataset is split between "description" (cleaner, more popular articles) and "no description" (less clean, less popular)
Consist of parquet files with cols: 'instruction, response, source'
full_to_summary (fts) consist of the following prompts infront of the instruction \
the full text is the instruction in these files
summary_to_full (stf) consist of the following prompts infront of the instruction \
the summary is the instruction in these files
| [
"# Overview\n\nConverted from these datasets:\n> URL -> labeled \"data\" \\\n> URL -> labeled \"wiki\" \\\n\nwiki dataset is split between \"description\" (cleaner, more popular articles) and \"no description\" (less clean, less popular)\n\nConsist of parquet files with cols: 'instruction, response, source'\n\nfull_to_summary (fts) consist of the following prompts infront of the instruction \\\nthe full text is the instruction in these files\n\n\nsummary_to_full (stf) consist of the following prompts infront of the instruction \\\nthe summary is the instruction in these files"
]
| [
"TAGS\n#region-us \n",
"# Overview\n\nConverted from these datasets:\n> URL -> labeled \"data\" \\\n> URL -> labeled \"wiki\" \\\n\nwiki dataset is split between \"description\" (cleaner, more popular articles) and \"no description\" (less clean, less popular)\n\nConsist of parquet files with cols: 'instruction, response, source'\n\nfull_to_summary (fts) consist of the following prompts infront of the instruction \\\nthe full text is the instruction in these files\n\n\nsummary_to_full (stf) consist of the following prompts infront of the instruction \\\nthe summary is the instruction in these files"
]
| [
6,
146
]
| [
"passage: TAGS\n#region-us \n# Overview\n\nConverted from these datasets:\n> URL -> labeled \"data\" \\\n> URL -> labeled \"wiki\" \\\n\nwiki dataset is split between \"description\" (cleaner, more popular articles) and \"no description\" (less clean, less popular)\n\nConsist of parquet files with cols: 'instruction, response, source'\n\nfull_to_summary (fts) consist of the following prompts infront of the instruction \\\nthe full text is the instruction in these files\n\n\nsummary_to_full (stf) consist of the following prompts infront of the instruction \\\nthe summary is the instruction in these files"
]
| [
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|
9eb902cae7245e34dd6f7e8bb6439524f2e89d72 | # Dataset Card for "wvDatasetFinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | basilis/wvDatasetFinal | [
"region:us"
]
| 2023-01-22T15:42:01+00:00 | {"dataset_info": {"features": [{"name": "final_text", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 6722209183, "num_examples": 97928}], "download_size": 1660170870, "dataset_size": 6722209183}} | 2023-01-22T15:46:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wvDatasetFinal"
More Information needed | [
"# Dataset Card for \"wvDatasetFinal\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wvDatasetFinal\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wvDatasetFinal\"\n\nMore Information needed"
]
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7a7e92553a233e0b89afb93e8d8292a1a39ba490 | # Dataset Card for "patched_1000_test_p_40_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_40_m1_predictions | [
"region:us"
]
| 2023-01-22T15:44:31+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 643791182, "num_examples": 1663294}], "download_size": 60859409, "dataset_size": 643791182}} | 2023-01-22T15:46:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_1000_test_p_40_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_1000_test_p_40_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_1000_test_p_40_m1_predictions\"\n\nMore Information needed"
]
| [
6,
27
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_1000_test_p_40_m1_predictions\"\n\nMore Information needed"
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|
6c1379cb6e1554f34c878040af700aa798bf9f62 | # Dataset Card for "relbert/scientific_and_creative_analogy"
## Dataset Description
- **Repository:** [https://github.com/taczin/SCAN_analogies](https://github.com/taczin/SCAN_analogies)
- **Paper:** [https://arxiv.org/abs/2211.15268](https://arxiv.org/abs/2211.15268)
- **Dataset:** Relation Mapping
### Dataset Summary
A dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the [paper](https://www.jair.org/index.php/jair/article/view/10583)).
Relation mapping `M` is the set of bijective map in between two sets of terms (`A` and `B`):
```
[set `A`]: ("solar system", "sun", "planet", "mass", "attracts", "revolves", "gravity")
[set `B`]: ("atom", "nucleus", "electron", "charge", "attracts", "revolves", "electromagnetism")
[Relation Mapping `M`]
* "solar system" -> "atom"
* "sun" -> "nucleus"
* "planet" -> "electron"
* "mass" -> "charge"
* "attracts" -> "attracts"
* "revolves" -> "revolves"
* "gravity" -> "electromagnetism"
```
***[Relation Mapping Problem](https://www.jair.org/index.php/jair/article/view/10583)*** is the task to identify the mapping `M` given the sets of terms `A` and `B`.
## Dataset Structure
### Data Instances
An example looks as follows.
```
{
"id": "0",
"reference": ["buying an item", "accepting a belief"],
"source": ["buying an item", "buyer", "merchandise", "buying", "selling", "returning", "valuable", "worthless"],
"target": ["accepting a belief", "believer", "belief", "accepting", "advocating", "rejecting", "true", "false"],
"target_random": ["rejecting", "true", "false", "accepting a belief", "believer", "advocating", "belief", "accepting"],
"type": "metaphor"
}
```
- `source`: A list of terms, which is the source of the relation mapping from.
- `target_random`: A list of terms, where we want to find a mapping from `source` to.
- `target`: A correctly ordered `target_random` that aligns with the `source`.
Given `source` and `target_random`, the task is to predict the correct order of `target_random` so that it matches `target`.
In average 7 terms are in the set, so the total number of possible order is 5040.
### Data Splits
| name |test|
|---------|----:|
|relation_mapping| 45 |
### Citation Information
```
@article{czinczoll2022scientific,
title={Scientific and Creative Analogies in Pretrained Language Models},
author={Czinczoll, Tamara and Yannakoudakis, Helen and Mishra, Pushkar and Shutova, Ekaterina},
journal={arXiv preprint arXiv:2211.15268},
year={2022}
}
``` | relbert/scientific_and_creative_analogy | [
"multilinguality:monolingual",
"size_categories:1<n<1K",
"language:en",
"license:other",
"arxiv:2211.15268",
"region:us"
]
| 2023-01-22T16:29:04+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1<n<1K"], "pretty_name": "Relation Mapping"} | 2023-01-22T16:49:01+00:00 | [
"2211.15268"
]
| [
"en"
]
| TAGS
#multilinguality-monolingual #size_categories-1<n<1K #language-English #license-other #arxiv-2211.15268 #region-us
| Dataset Card for "relbert/scientific\_and\_creative\_analogy"
=============================================================
Dataset Description
-------------------
* Repository: URL
* Paper: URL
* Dataset: Relation Mapping
### Dataset Summary
A dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the paper).
Relation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):
*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.
Dataset Structure
-----------------
### Data Instances
An example looks as follows.
* 'source': A list of terms, which is the source of the relation mapping from.
* 'target\_random': A list of terms, where we want to find a mapping from 'source' to.
* 'target': A correctly ordered 'target\_random' that aligns with the 'source'.
Given 'source' and 'target\_random', the task is to predict the correct order of 'target\_random' so that it matches 'target'.
In average 7 terms are in the set, so the total number of possible order is 5040.
### Data Splits
| [
"### Dataset Summary\n\n\nA dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the paper).\n\n\nRelation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):\n\n\n*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example looks as follows.\n\n\n* 'source': A list of terms, which is the source of the relation mapping from.\n* 'target\\_random': A list of terms, where we want to find a mapping from 'source' to.\n* 'target': A correctly ordered 'target\\_random' that aligns with the 'source'.\n\n\nGiven 'source' and 'target\\_random', the task is to predict the correct order of 'target\\_random' so that it matches 'target'.\nIn average 7 terms are in the set, so the total number of possible order is 5040.",
"### Data Splits"
]
| [
"TAGS\n#multilinguality-monolingual #size_categories-1<n<1K #language-English #license-other #arxiv-2211.15268 #region-us \n",
"### Dataset Summary\n\n\nA dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the paper).\n\n\nRelation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):\n\n\n*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example looks as follows.\n\n\n* 'source': A list of terms, which is the source of the relation mapping from.\n* 'target\\_random': A list of terms, where we want to find a mapping from 'source' to.\n* 'target': A correctly ordered 'target\\_random' that aligns with the 'source'.\n\n\nGiven 'source' and 'target\\_random', the task is to predict the correct order of 'target\\_random' so that it matches 'target'.\nIn average 7 terms are in the set, so the total number of possible order is 5040.",
"### Data Splits"
]
| [
43,
103,
154,
5
]
| [
"passage: TAGS\n#multilinguality-monolingual #size_categories-1<n<1K #language-English #license-other #arxiv-2211.15268 #region-us \n### Dataset Summary\n\n\nA dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the paper).\n\n\nRelation mapping 'M' is the set of bijective map in between two sets of terms ('A' and 'B'):\n\n\n*Relation Mapping Problem* is the task to identify the mapping 'M' given the sets of terms 'A' and 'B'.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example looks as follows.\n\n\n* 'source': A list of terms, which is the source of the relation mapping from.\n* 'target\\_random': A list of terms, where we want to find a mapping from 'source' to.\n* 'target': A correctly ordered 'target\\_random' that aligns with the 'source'.\n\n\nGiven 'source' and 'target\\_random', the task is to predict the correct order of 'target\\_random' so that it matches 'target'.\nIn average 7 terms are in the set, so the total number of possible order is 5040.### Data Splits"
]
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|
2127d1c183b87fcae8176346473cb8103deaf872 | # Dataset Card for "Hatefulmemes_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Hatefulmemes_train | [
"region:us"
]
| 2023-01-22T17:25:12+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "not-hateful", "1": "hateful"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3066249406.0, "num_examples": 8500}], "download_size": 3059695187, "dataset_size": 3066249406.0}} | 2023-05-07T19:54:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Hatefulmemes_train"
More Information needed | [
"# Dataset Card for \"Hatefulmemes_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Hatefulmemes_train\"\n\nMore Information needed"
]
| [
6,
18
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_train\"\n\nMore Information needed"
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|
e4adbc1a9793e94f14e9a422f56c672b3e4b7f44 | # Dataset Card for "Hatefulmemes_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Hatefulmemes_test | [
"region:us"
]
| 2023-01-22T17:27:29+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "not-hateful", "1": "hateful"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "blip_caption_topk_50_Salesforce_blip_image_captioning_large_multiple", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_all_patches", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "blip_caption_beam_5_Salesforce_blip_image_captioning_large", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai_wordnet", "sequence": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_hf", "dtype": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_hf_a meme of", "dtype": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_max_length_30_hf", "dtype": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_max_length_200_hf", "dtype": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_max_length_200_hf_a meme of", "dtype": "string"}, {"name": "blip_caption_False_beams_5_Salesforce_blip_image_captioning_large_max_length_30_hf", "dtype": "string"}, {"name": "blip_caption_beam_False_5_source", "dtype": "string"}, {"name": "blip_caption_False_beams_5_base_capfilt_large_max_length_30_source_a pitcure of ", "dtype": "string"}, {"name": "blip_caption_False_beams_5_base_capfilt_large_max_length_100_source_a pitcure of ", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_laion.pt", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 421626763.0, "num_examples": 1000}], "download_size": 387589337, "dataset_size": 421626763.0}} | 2023-06-17T01:26:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Hatefulmemes_test"
More Information needed | [
"# Dataset Card for \"Hatefulmemes_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Hatefulmemes_test\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test\"\n\nMore Information needed"
]
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|
3c5c0f5c6b5b14e1dd29b76055d73db7e9a110d3 | # Dataset Card for "pii-pile-chunk3-300000-350000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-300000-350000-tagged | [
"region:us"
]
| 2023-01-22T17:57:02+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 510432044, "num_examples": 50000}], "download_size": 194469001, "dataset_size": 510432044}} | 2023-01-22T17:57:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pii-pile-chunk3-300000-350000-tagged"
More Information needed | [
"# Dataset Card for \"pii-pile-chunk3-300000-350000-tagged\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pii-pile-chunk3-300000-350000-tagged\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pii-pile-chunk3-300000-350000-tagged\"\n\nMore Information needed"
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|
17b54df016af0073e3bc05ee9bf56352dd3b4713 | # Dataset Card for "patched_1000_test_p_150_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_150_m1_predictions | [
"region:us"
]
| 2023-01-22T17:57:24+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 415290126, "num_examples": 1035692}], "download_size": 41645841, "dataset_size": 415290126}} | 2023-01-22T17:57:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_1000_test_p_150_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_1000_test_p_150_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_1000_test_p_150_m1_predictions\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_1000_test_p_150_m1_predictions\"\n\nMore Information needed"
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|
afc1b5fdb5125962aeb896eef182fce3cdd31475 |
# Dataset Card for spaeti_store
## Dataset Description
The dataset consists of 10 pictures of one späti (German convenience store) from different angles.
The data is unlabeled.
The dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the [organization's page](https://huggingface.co/dreambooth-hackathon) for more info.
| malysheva42/spaeti_store | [
"task_categories:text-to-image",
"task_categories:image-segmentation",
"task_categories:image-classification",
"task_categories:image-to-image",
"size_categories:n<1K",
"license:openrail",
"region:us"
]
| 2023-01-22T19:20:22+00:00 | {"license": "openrail", "size_categories": ["n<1K"], "task_categories": ["text-to-image", "image-segmentation", "image-classification", "image-to-image"], "pretty_name": "Photos of one sp\u00e4ti (a German convenience store)"} | 2023-02-07T17:34:51+00:00 | []
| []
| TAGS
#task_categories-text-to-image #task_categories-image-segmentation #task_categories-image-classification #task_categories-image-to-image #size_categories-n<1K #license-openrail #region-us
|
# Dataset Card for spaeti_store
## Dataset Description
The dataset consists of 10 pictures of one späti (German convenience store) from different angles.
The data is unlabeled.
The dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the organization's page for more info.
| [
"# Dataset Card for spaeti_store",
"## Dataset Description\nThe dataset consists of 10 pictures of one späti (German convenience store) from different angles. \nThe data is unlabeled.\nThe dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the organization's page for more info."
]
| [
"TAGS\n#task_categories-text-to-image #task_categories-image-segmentation #task_categories-image-classification #task_categories-image-to-image #size_categories-n<1K #license-openrail #region-us \n",
"# Dataset Card for spaeti_store",
"## Dataset Description\nThe dataset consists of 10 pictures of one späti (German convenience store) from different angles. \nThe data is unlabeled.\nThe dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the organization's page for more info."
]
| [
69,
9,
76
]
| [
"passage: TAGS\n#task_categories-text-to-image #task_categories-image-segmentation #task_categories-image-classification #task_categories-image-to-image #size_categories-n<1K #license-openrail #region-us \n# Dataset Card for spaeti_store## Dataset Description\nThe dataset consists of 10 pictures of one späti (German convenience store) from different angles. \nThe data is unlabeled.\nThe dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the organization's page for more info."
]
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|
5fff4e2ac3514a4bb861b046c6206159ac6e9d63 | # Dataset Card for "Hatefulmemes_train_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Hatefulmemes_train_embeddings | [
"region:us"
]
| 2023-01-22T19:25:54+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 3080005786.0, "num_examples": 8500}], "download_size": 3087127731, "dataset_size": 3080005786.0}} | 2023-01-22T19:28:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Hatefulmemes_train_embeddings"
More Information needed | [
"# Dataset Card for \"Hatefulmemes_train_embeddings\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Hatefulmemes_train_embeddings\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_train_embeddings\"\n\nMore Information needed"
]
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|
50adeea882bdf0bddf535b6c4245005efade3d57 | # Dataset Card for "Hatefulmemes_test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Hatefulmemes_test_embeddings | [
"region:us"
]
| 2023-01-22T19:41:51+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 364453207.0, "num_examples": 1000}], "download_size": 365091102, "dataset_size": 364453207.0}} | 2023-01-22T19:42:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Hatefulmemes_test_embeddings"
More Information needed | [
"# Dataset Card for \"Hatefulmemes_test_embeddings\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Hatefulmemes_test_embeddings\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Hatefulmemes_test_embeddings\"\n\nMore Information needed"
]
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|
1f8922b2a37c65ec38e79d7a4704c1b9b6f21f7e |
# Dataset Card for Swiss Legislation
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Swiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.
### Supported Tasks and Leaderboards
### Languages
The total number of texts in the dataset is 35,698. The dataset is saved in _lexfind_v2.jsonl_ format.
Switzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts.
36K & 18K & 11K & 6K & 534 & 207
| Language | Subset | Number of Documents |
|------------|------------|----------------------|
| German | **de** | 18K |
| French | **fr** | 11K |
| Italian | **it** | 6K |
| Romanch | **rm** | 534 |
| English | **en** | 207 |
## Dataset Structure
### Data Fields
Each entry in the dataset is a dictionary with the following keys:
- `canton`: the canton of origin of the legislation
- example: "ag"
- `language`: the language of the legislation
- example: "de"
- `uuid`: a unique identifier for the legislation
- example: "ec312f57-05fe-4552-ba50-8c9c269e0f3b"
- `title`: the title of the legislation
- example: "Gesetz über die Geoinformation im Kanton Aargau"
- `short`: a short description of the legislation
- example: "Kantonales Geoinformationsgesetz"
- `abbreviation`: an abbreviation for the legislation
- example: "KGeoIG"
- `sr_number`: a reference number for the legislation
- example: "740.100"
- `is_active`: whether the legislation is currently in force
- example: true
- `version_active_since`: the date since when the legislation's current version is active
- example: "2021-09-01"
- `family_active_since`: the date since when the legislation's current version's family is active
- example: "2011-05-24"
- `version_inactive_since`: the date since when the legislation's current version is inactive
- example: null
- `version_found_at`: the date the legislation's current version was found
- example: "2021-09-01"
- `pdf_url`: a link to the legislation's pdf
- example: "https://www.lexfind.ch/tol/1557/de"
- `html_url`: a link to the legislation's html
- example: "https://gesetzessammlungen.ag.ch/app/de/texts_of_law/740.100")_
- `pdf_content`: the legislation's pdf content
- example: "740.100 - Gesetz über..."
- `html_content`: the legislation's html content
- example: ""
- `changes`: a list of changes made to the legislation
- example: []
- `history`: a list of the legislation's history
- example: []
- `quotes`: a list of quotes from the legislation
- example: []
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
1. 'ch': Switzerland (Federal) - 15840
2. 'fr': Fribourg - 1633
3. 'be': Bern - 1344
4. 'vs': Valais - 1328
5. 'gr': Graubünden - 1205
6. 'ne': Neuchâtel - 1115
7. 'zh': Zurich - 974
8. 'bs': Basel-Stadt - 899
9. 'bl': Basel-Landschaft - 863
10. 'vd': Vaud - 870
11. 'ge': Geneva - 837
12. 'sg': St. Gallen - 764
13. 'ju': Jura - 804
14. 'zg': Zug - 632
15. 'ti': Ticino - 627
16. 'lu': Lucerne - 584
17. 'so': Solothurn - 547
18. 'ow': Obwalden - 513
19. 'ik': Interkantonal - 510
20. 'sh': Schaffhausen - 469
21. 'gl': Glarus - 467
22. 'tg': Thurgau - 453
23. 'sz': Schwyz - 423
24. 'ai': Appenzell Innerrhoden - 416
25. 'ag': Aargau - 483
26. 'ar': Appenzell Ausserrhoden - 330
27. 'nw': Nidwalden - 401
28. 'ur': Uri - 367
29.
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| rcds/swiss_legislation | [
"task_categories:text-classification",
"task_categories:translation",
"size_categories:100K<n<1M",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
]
| 2023-01-22T20:02:28+00:00 | {"language": ["de", "fr", "it"], "license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "translation"], "pretty_name": "Swiss Legislation"} | 2023-07-20T06:36:07+00:00 | [
"2306.09237"
]
| [
"de",
"fr",
"it"
]
| TAGS
#task_categories-text-classification #task_categories-translation #size_categories-100K<n<1M #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us
| Dataset Card for Swiss Legislation
==================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
Swiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.
### Supported Tasks and Leaderboards
### Languages
The total number of texts in the dataset is 35,698. The dataset is saved in *lexfind\_v2.jsonl* format.
Switzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts.
36K & 18K & 11K & 6K & 534 & 207
Language: German, Subset: de, Number of Documents: 18K
Language: French, Subset: fr, Number of Documents: 11K
Language: Italian, Subset: it, Number of Documents: 6K
Language: Romanch, Subset: rm, Number of Documents: 534
Language: English, Subset: en, Number of Documents: 207
Dataset Structure
-----------------
### Data Fields
Each entry in the dataset is a dictionary with the following keys:
* 'canton': the canton of origin of the legislation
+ example: "ag"
* 'language': the language of the legislation
+ example: "de"
* 'uuid': a unique identifier for the legislation
+ example: "ec312f57-05fe-4552-ba50-8c9c269e0f3b"
* 'title': the title of the legislation
+ example: "Gesetz über die Geoinformation im Kanton Aargau"
* 'short': a short description of the legislation
+ example: "Kantonales Geoinformationsgesetz"
* 'abbreviation': an abbreviation for the legislation
+ example: "KGeoIG"
* 'sr\_number': a reference number for the legislation
+ example: "740.100"
* 'is\_active': whether the legislation is currently in force
+ example: true
* 'version\_active\_since': the date since when the legislation's current version is active
+ example: "2021-09-01"
* 'family\_active\_since': the date since when the legislation's current version's family is active
+ example: "2011-05-24"
* 'version\_inactive\_since': the date since when the legislation's current version is inactive
+ example: null
* 'version\_found\_at': the date the legislation's current version was found
+ example: "2021-09-01"
* 'pdf\_url': a link to the legislation's pdf
+ example: "URL
* 'html\_url': a link to the legislation's html
+ example: "URL
* 'pdf\_content': the legislation's pdf content
+ example: "740.100 - Gesetz über..."
* 'html\_content': the legislation's html content
+ example: ""
* 'changes': a list of changes made to the legislation
+ example: []
* 'history': a list of the legislation's history
+ example: []
* 'quotes': a list of quotes from the legislation
+ example: []
### Data Instances
### Data Fields
### Data Splits
1. 'ch': Switzerland (Federal) - 15840
2. 'fr': Fribourg - 1633
3. 'be': Bern - 1344
4. 'vs': Valais - 1328
5. 'gr': Graubünden - 1205
6. 'ne': Neuchâtel - 1115
7. 'zh': Zurich - 974
8. 'bs': Basel-Stadt - 899
9. 'bl': Basel-Landschaft - 863
10. 'vd': Vaud - 870
11. 'ge': Geneva - 837
12. 'sg': St. Gallen - 764
13. 'ju': Jura - 804
14. 'zg': Zug - 632
15. 'ti': Ticino - 627
16. 'lu': Lucerne - 584
17. 'so': Solothurn - 547
18. 'ow': Obwalden - 513
19. 'ik': Interkantonal - 510
20. 'sh': Schaffhausen - 469
21. 'gl': Glarus - 467
22. 'tg': Thurgau - 453
23. 'sz': Schwyz - 423
24. 'ai': Appenzell Innerrhoden - 416
25. 'ag': Aargau - 483
26. 'ar': Appenzell Ausserrhoden - 330
27. 'nw': Nidwalden - 401
28. 'ur': Uri - 367
29.
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (URL).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (URL
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: URL
Please cite our ArXiv-Preprint
| [
"### Dataset Summary\n\n\nSwiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThe total number of texts in the dataset is 35,698. The dataset is saved in *lexfind\\_v2.jsonl* format.\nSwitzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts.\n36K & 18K & 11K & 6K & 534 & 207\n\n\nLanguage: German, Subset: de, Number of Documents: 18K\nLanguage: French, Subset: fr, Number of Documents: 11K\nLanguage: Italian, Subset: it, Number of Documents: 6K\nLanguage: Romanch, Subset: rm, Number of Documents: 534\nLanguage: English, Subset: en, Number of Documents: 207\n\n\nDataset Structure\n-----------------",
"### Data Fields\n\n\nEach entry in the dataset is a dictionary with the following keys:\n\n\n* 'canton': the canton of origin of the legislation\n\t+ example: \"ag\"\n* 'language': the language of the legislation\n\t+ example: \"de\"\n* 'uuid': a unique identifier for the legislation\n\t+ example: \"ec312f57-05fe-4552-ba50-8c9c269e0f3b\"\n* 'title': the title of the legislation\n\t+ example: \"Gesetz über die Geoinformation im Kanton Aargau\"\n* 'short': a short description of the legislation\n\t+ example: \"Kantonales Geoinformationsgesetz\"\n* 'abbreviation': an abbreviation for the legislation\n\t+ example: \"KGeoIG\"\n* 'sr\\_number': a reference number for the legislation\n\t+ example: \"740.100\"\n* 'is\\_active': whether the legislation is currently in force\n\t+ example: true\n* 'version\\_active\\_since': the date since when the legislation's current version is active\n\t+ example: \"2021-09-01\"\n* 'family\\_active\\_since': the date since when the legislation's current version's family is active\n\t+ example: \"2011-05-24\"\n* 'version\\_inactive\\_since': the date since when the legislation's current version is inactive\n\t+ example: null\n* 'version\\_found\\_at': the date the legislation's current version was found\n\t+ example: \"2021-09-01\"\n* 'pdf\\_url': a link to the legislation's pdf\n\t+ example: \"URL\n* 'html\\_url': a link to the legislation's html\n\t+ example: \"URL\n* 'pdf\\_content': the legislation's pdf content\n\t+ example: \"740.100 - Gesetz über...\"\n* 'html\\_content': the legislation's html content\n\t+ example: \"\"\n* 'changes': a list of changes made to the legislation\n\t+ example: []\n* 'history': a list of the legislation's history\n\t+ example: []\n* 'quotes': a list of quotes from the legislation\n\t+ example: []",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\n1. 'ch': Switzerland (Federal) - 15840\n2. 'fr': Fribourg - 1633\n3. 'be': Bern - 1344\n4. 'vs': Valais - 1328\n5. 'gr': Graubünden - 1205\n6. 'ne': Neuchâtel - 1115\n7. 'zh': Zurich - 974\n8. 'bs': Basel-Stadt - 899\n9. 'bl': Basel-Landschaft - 863\n10. 'vd': Vaud - 870\n11. 'ge': Geneva - 837\n12. 'sg': St. Gallen - 764\n13. 'ju': Jura - 804\n14. 'zg': Zug - 632\n15. 'ti': Ticino - 627\n16. 'lu': Lucerne - 584\n17. 'so': Solothurn - 547\n18. 'ow': Obwalden - 513\n19. 'ik': Interkantonal - 510\n20. 'sh': Schaffhausen - 469\n21. 'gl': Glarus - 467\n22. 'tg': Thurgau - 453\n23. 'sz': Schwyz - 423\n24. 'ai': Appenzell Innerrhoden - 416\n25. 'ag': Aargau - 483\n26. 'ar': Appenzell Ausserrhoden - 330\n27. 'nw': Nidwalden - 401\n28. 'ur': Uri - 367\n29. \n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?\n\n\nMetadata is published by the Swiss Federal Supreme Court (URL).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-translation #size_categories-100K<n<1M #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us \n",
"### Dataset Summary\n\n\nSwiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThe total number of texts in the dataset is 35,698. The dataset is saved in *lexfind\\_v2.jsonl* format.\nSwitzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts.\n36K & 18K & 11K & 6K & 534 & 207\n\n\nLanguage: German, Subset: de, Number of Documents: 18K\nLanguage: French, Subset: fr, Number of Documents: 11K\nLanguage: Italian, Subset: it, Number of Documents: 6K\nLanguage: Romanch, Subset: rm, Number of Documents: 534\nLanguage: English, Subset: en, Number of Documents: 207\n\n\nDataset Structure\n-----------------",
"### Data Fields\n\n\nEach entry in the dataset is a dictionary with the following keys:\n\n\n* 'canton': the canton of origin of the legislation\n\t+ example: \"ag\"\n* 'language': the language of the legislation\n\t+ example: \"de\"\n* 'uuid': a unique identifier for the legislation\n\t+ example: \"ec312f57-05fe-4552-ba50-8c9c269e0f3b\"\n* 'title': the title of the legislation\n\t+ example: \"Gesetz über die Geoinformation im Kanton Aargau\"\n* 'short': a short description of the legislation\n\t+ example: \"Kantonales Geoinformationsgesetz\"\n* 'abbreviation': an abbreviation for the legislation\n\t+ example: \"KGeoIG\"\n* 'sr\\_number': a reference number for the legislation\n\t+ example: \"740.100\"\n* 'is\\_active': whether the legislation is currently in force\n\t+ example: true\n* 'version\\_active\\_since': the date since when the legislation's current version is active\n\t+ example: \"2021-09-01\"\n* 'family\\_active\\_since': the date since when the legislation's current version's family is active\n\t+ example: \"2011-05-24\"\n* 'version\\_inactive\\_since': the date since when the legislation's current version is inactive\n\t+ example: null\n* 'version\\_found\\_at': the date the legislation's current version was found\n\t+ example: \"2021-09-01\"\n* 'pdf\\_url': a link to the legislation's pdf\n\t+ example: \"URL\n* 'html\\_url': a link to the legislation's html\n\t+ example: \"URL\n* 'pdf\\_content': the legislation's pdf content\n\t+ example: \"740.100 - Gesetz über...\"\n* 'html\\_content': the legislation's html content\n\t+ example: \"\"\n* 'changes': a list of changes made to the legislation\n\t+ example: []\n* 'history': a list of the legislation's history\n\t+ example: []\n* 'quotes': a list of quotes from the legislation\n\t+ example: []",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\n1. 'ch': Switzerland (Federal) - 15840\n2. 'fr': Fribourg - 1633\n3. 'be': Bern - 1344\n4. 'vs': Valais - 1328\n5. 'gr': Graubünden - 1205\n6. 'ne': Neuchâtel - 1115\n7. 'zh': Zurich - 974\n8. 'bs': Basel-Stadt - 899\n9. 'bl': Basel-Landschaft - 863\n10. 'vd': Vaud - 870\n11. 'ge': Geneva - 837\n12. 'sg': St. Gallen - 764\n13. 'ju': Jura - 804\n14. 'zg': Zug - 632\n15. 'ti': Ticino - 627\n16. 'lu': Lucerne - 584\n17. 'so': Solothurn - 547\n18. 'ow': Obwalden - 513\n19. 'ik': Interkantonal - 510\n20. 'sh': Schaffhausen - 469\n21. 'gl': Glarus - 467\n22. 'tg': Thurgau - 453\n23. 'sz': Schwyz - 423\n24. 'ai': Appenzell Innerrhoden - 416\n25. 'ag': Aargau - 483\n26. 'ar': Appenzell Ausserrhoden - 330\n27. 'nw': Nidwalden - 401\n28. 'ur': Uri - 367\n29. \n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?\n\n\nMetadata is published by the Swiss Federal Supreme Court (URL).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint"
]
| [
73,
40,
10,
175,
505,
6,
5,
320,
7,
4,
50,
30,
5,
5,
22,
55,
7,
8,
14,
6,
110
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-translation #size_categories-100K<n<1M #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #region-us \n### Dataset Summary\n\n\nSwiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.### Supported Tasks and Leaderboards### Languages\n\n\nThe total number of texts in the dataset is 35,698. The dataset is saved in *lexfind\\_v2.jsonl* format.\nSwitzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts.\n36K & 18K & 11K & 6K & 534 & 207\n\n\nLanguage: German, Subset: de, Number of Documents: 18K\nLanguage: French, Subset: fr, Number of Documents: 11K\nLanguage: Italian, Subset: it, Number of Documents: 6K\nLanguage: Romanch, Subset: rm, Number of Documents: 534\nLanguage: English, Subset: en, Number of Documents: 207\n\n\nDataset Structure\n-----------------",
"passage: ### Data Fields\n\n\nEach entry in the dataset is a dictionary with the following keys:\n\n\n* 'canton': the canton of origin of the legislation\n\t+ example: \"ag\"\n* 'language': the language of the legislation\n\t+ example: \"de\"\n* 'uuid': a unique identifier for the legislation\n\t+ example: \"ec312f57-05fe-4552-ba50-8c9c269e0f3b\"\n* 'title': the title of the legislation\n\t+ example: \"Gesetz über die Geoinformation im Kanton Aargau\"\n* 'short': a short description of the legislation\n\t+ example: \"Kantonales Geoinformationsgesetz\"\n* 'abbreviation': an abbreviation for the legislation\n\t+ example: \"KGeoIG\"\n* 'sr\\_number': a reference number for the legislation\n\t+ example: \"740.100\"\n* 'is\\_active': whether the legislation is currently in force\n\t+ example: true\n* 'version\\_active\\_since': the date since when the legislation's current version is active\n\t+ example: \"2021-09-01\"\n* 'family\\_active\\_since': the date since when the legislation's current version's family is active\n\t+ example: \"2011-05-24\"\n* 'version\\_inactive\\_since': the date since when the legislation's current version is inactive\n\t+ example: null\n* 'version\\_found\\_at': the date the legislation's current version was found\n\t+ example: \"2021-09-01\"\n* 'pdf\\_url': a link to the legislation's pdf\n\t+ example: \"URL\n* 'html\\_url': a link to the legislation's html\n\t+ example: \"URL\n* 'pdf\\_content': the legislation's pdf content\n\t+ example: \"740.100 - Gesetz über...\"\n* 'html\\_content': the legislation's html content\n\t+ example: \"\"\n* 'changes': a list of changes made to the legislation\n\t+ example: []\n* 'history': a list of the legislation's history\n\t+ example: []\n* 'quotes': a list of quotes from the legislation\n\t+ example: []### Data Instances### Data Fields### Data Splits\n\n\n1. 'ch': Switzerland (Federal) - 15840\n2. 'fr': Fribourg - 1633\n3. 'be': Bern - 1344\n4. 'vs': Valais - 1328\n5. 'gr': Graubünden - 1205\n6. 'ne': Neuchâtel - 1115\n7. 'zh': Zurich - 974\n8. 'bs': Basel-Stadt - 899\n9. 'bl': Basel-Landschaft - 863\n10. 'vd': Vaud - 870\n11. 'ge': Geneva - 837\n12. 'sg': St. Gallen - 764\n13. 'ju': Jura - 804\n14. 'zg': Zug - 632\n15. 'ti': Ticino - 627\n16. 'lu': Lucerne - 584\n17. 'so': Solothurn - 547\n18. 'ow': Obwalden - 513\n19. 'ik': Interkantonal - 510\n20. 'sh': Schaffhausen - 469\n21. 'gl': Glarus - 467\n22. 'tg': Thurgau - 453\n23. 'sz': Schwyz - 423\n24. 'ai': Appenzell Innerrhoden - 416\n25. 'ag': Aargau - 483\n26. 'ar': Appenzell Ausserrhoden - 330\n27. 'nw': Nidwalden - 401\n28. 'ur': Uri - 367\n29. \n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.#### Who are the source language producers?\n\n\nThe decisions are written by the judges and clerks in the language of the proceedings.### Annotations#### Annotation process#### Who are the annotators?\n\n\nMetadata is published by the Swiss Federal Supreme Court (URL)."
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|
2dc7f435cb51768eaa6275d5242bfa777bfc5a06 |
# PLANE Out-of-Distribution Sets
PLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference.
The current dataset contains five sampled splits, used in the supervised experiments of [Bertolini et al., 22](https://aclanthology.org/2022.coling-1.359/).
## Data Structure
The `dataset` is organised around five `Train/test_split#`, each containing a training and test set of circa 60K and 2K.
### Features
Each entrance has 6 features: `seq, label, Adj_Class, Adj, Nn, Hy`
- `seq`:test sequense
- `label`: ground truth (1:entialment, 0:no-entailment)
- `Adj_Class`: the class of the sequence adjectives
- `Adj`: the adjective of the sequence (I: intersective, S: subsective, O: intensional)
- `N`n: the noun
- `Hy`: the noun's hypericum
Each sample in `seq` can take one of three forms (or inference types, in paper):
- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)
- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)
- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)
Please note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail.
### Trained Model
You can find a tuned BERT-base model (tuned and validated using the 2nd split) [here](https://huggingface.co/lorenzoscottb/bert-base-cased-PLANE-ood-2?text=A+fake+smile+is+a+smile).
### Cite
If you use PLANE for your work, please cite the main COLING 2022 paper.
```
@inproceedings{bertolini-etal-2022-testing,
title = "Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment",
author = "Bertolini, Lorenzo and
Weeds, Julie and
Weir, David",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.359",
pages = "4084--4100",
}
``` | lorenzoscottb/PLANE-ood | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-2.0",
"region:us"
]
| 2023-01-22T21:22:03+00:00 | {"language": ["en"], "license": "cc-by-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"]} | 2023-01-25T09:51:09+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-cc-by-2.0 #region-us
|
# PLANE Out-of-Distribution Sets
PLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference.
The current dataset contains five sampled splits, used in the supervised experiments of Bertolini et al., 22.
## Data Structure
The 'dataset' is organised around five 'Train/test_split#', each containing a training and test set of circa 60K and 2K.
### Features
Each entrance has 6 features: 'seq, label, Adj_Class, Adj, Nn, Hy'
- 'seq':test sequense
- 'label': ground truth (1:entialment, 0:no-entailment)
- 'Adj_Class': the class of the sequence adjectives
- 'Adj': the adjective of the sequence (I: intersective, S: subsective, O: intensional)
- 'N'n: the noun
- 'Hy': the noun's hypericum
Each sample in 'seq' can take one of three forms (or inference types, in paper):
- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)
- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)
- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)
Please note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail.
### Trained Model
You can find a tuned BERT-base model (tuned and validated using the 2nd split) here.
### Cite
If you use PLANE for your work, please cite the main COLING 2022 paper.
| [
"# PLANE Out-of-Distribution Sets\n\nPLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference. \nThe current dataset contains five sampled splits, used in the supervised experiments of Bertolini et al., 22.",
"## Data Structure\n\nThe 'dataset' is organised around five 'Train/test_split#', each containing a training and test set of circa 60K and 2K.",
"### Features\n\nEach entrance has 6 features: 'seq, label, Adj_Class, Adj, Nn, Hy'\n- 'seq':test sequense\n- 'label': ground truth (1:entialment, 0:no-entailment)\n- 'Adj_Class': the class of the sequence adjectives\n- 'Adj': the adjective of the sequence (I: intersective, S: subsective, O: intensional)\n- 'N'n: the noun\n- 'Hy': the noun's hypericum\n\nEach sample in 'seq' can take one of three forms (or inference types, in paper):\n\n- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)\n- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)\n- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)\n\nPlease note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail.",
"### Trained Model\n\nYou can find a tuned BERT-base model (tuned and validated using the 2nd split) here.",
"### Cite\n\nIf you use PLANE for your work, please cite the main COLING 2022 paper."
]
| [
"TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-cc-by-2.0 #region-us \n",
"# PLANE Out-of-Distribution Sets\n\nPLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference. \nThe current dataset contains five sampled splits, used in the supervised experiments of Bertolini et al., 22.",
"## Data Structure\n\nThe 'dataset' is organised around five 'Train/test_split#', each containing a training and test set of circa 60K and 2K.",
"### Features\n\nEach entrance has 6 features: 'seq, label, Adj_Class, Adj, Nn, Hy'\n- 'seq':test sequense\n- 'label': ground truth (1:entialment, 0:no-entailment)\n- 'Adj_Class': the class of the sequence adjectives\n- 'Adj': the adjective of the sequence (I: intersective, S: subsective, O: intensional)\n- 'N'n: the noun\n- 'Hy': the noun's hypericum\n\nEach sample in 'seq' can take one of three forms (or inference types, in paper):\n\n- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)\n- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)\n- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)\n\nPlease note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail.",
"### Trained Model\n\nYou can find a tuned BERT-base model (tuned and validated using the 2nd split) here.",
"### Cite\n\nIf you use PLANE for your work, please cite the main COLING 2022 paper."
]
| [
42,
73,
42,
301,
30,
22
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #license-cc-by-2.0 #region-us \n# PLANE Out-of-Distribution Sets\n\nPLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference. \nThe current dataset contains five sampled splits, used in the supervised experiments of Bertolini et al., 22.## Data Structure\n\nThe 'dataset' is organised around five 'Train/test_split#', each containing a training and test set of circa 60K and 2K.### Features\n\nEach entrance has 6 features: 'seq, label, Adj_Class, Adj, Nn, Hy'\n- 'seq':test sequense\n- 'label': ground truth (1:entialment, 0:no-entailment)\n- 'Adj_Class': the class of the sequence adjectives\n- 'Adj': the adjective of the sequence (I: intersective, S: subsective, O: intensional)\n- 'N'n: the noun\n- 'Hy': the noun's hypericum\n\nEach sample in 'seq' can take one of three forms (or inference types, in paper):\n\n- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)\n- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)\n- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)\n\nPlease note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail.### Trained Model\n\nYou can find a tuned BERT-base model (tuned and validated using the 2nd split) here."
]
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|
eaab5e695a1c8084eea9abebf7323ca9d6eea727 | # Dataset Card for "bookcorpus_compact_256_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_256_test | [
"region:us"
]
| 2023-01-22T21:43:56+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20727824, "num_examples": 6160}], "download_size": 10867768, "dataset_size": 20727824}} | 2023-01-22T23:43:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_256_test"
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_256_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_256_test\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_256_test\"\n\nMore Information needed"
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| [
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|
5e9d7b7fc073553b624f050b6bb1a05b1073960f | # Dataset Card for "bookcorpus_compact_512_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_512_test | [
"region:us"
]
| 2023-01-22T21:51:07+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39735149, "num_examples": 6160}], "download_size": 20545672, "dataset_size": 39735149}} | 2023-01-23T00:07:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_512_test"
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_512_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_512_test\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_512_test\"\n\nMore Information needed"
]
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|
407683a9e51c59c4a6ed148177f36df5017034ef |
# Ivypanda essays
## Dataset Description
- **Homepage:** https://laion.ai/
### Dataset Summary
This dataset contains essays from [ivypanda](https://ivypanda.com/essays/).
## Dataset Structure
### Data Fields
`TEXT`: The text of the essay.<br/>
`SOURCE`: A permalink to the ivypanda essay page
| qwedsacf/ivypanda-essays | [
"region:us"
]
| 2023-01-23T00:37:04+00:00 | {} | 2023-02-03T21:05:11+00:00 | []
| []
| TAGS
#region-us
|
# Ivypanda essays
## Dataset Description
- Homepage: URL
### Dataset Summary
This dataset contains essays from ivypanda.
## Dataset Structure
### Data Fields
'TEXT': The text of the essay.<br/>
'SOURCE': A permalink to the ivypanda essay page
| [
"# Ivypanda essays",
"## Dataset Description\n\n- Homepage: URL",
"### Dataset Summary\n\nThis dataset contains essays from ivypanda.",
"## Dataset Structure",
"### Data Fields\n\n'TEXT': The text of the essay.<br/>\n'SOURCE': A permalink to the ivypanda essay page"
]
| [
"TAGS\n#region-us \n",
"# Ivypanda essays",
"## Dataset Description\n\n- Homepage: URL",
"### Dataset Summary\n\nThis dataset contains essays from ivypanda.",
"## Dataset Structure",
"### Data Fields\n\n'TEXT': The text of the essay.<br/>\n'SOURCE': A permalink to the ivypanda essay page"
]
| [
6,
6,
8,
18,
6,
34
]
| [
"passage: TAGS\n#region-us \n# Ivypanda essays## Dataset Description\n\n- Homepage: URL### Dataset Summary\n\nThis dataset contains essays from ivypanda.## Dataset Structure### Data Fields\n\n'TEXT': The text of the essay.<br/>\n'SOURCE': A permalink to the ivypanda essay page"
]
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|
e417799ec581048674164e3e4b595ff8560d32a3 | # Dataset Card for "bookcorpus_compact_256_test_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_256_test_meta | [
"region:us"
]
| 2023-01-23T01:04:22+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 214680900, "num_examples": 6160}], "download_size": 47705450, "dataset_size": 214680900}} | 2023-01-23T01:04:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_256_test_meta"
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_256_test_meta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_256_test_meta\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_256_test_meta\"\n\nMore Information needed"
]
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]
|
4d631c6bbd0ec86f851fd15932a6eb589f3dfa24 | # Dataset Card for "bookcorpus_compact_512_test_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_512_test_meta | [
"region:us"
]
| 2023-01-23T04:03:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 414104933, "num_examples": 6160}], "download_size": 91522110, "dataset_size": 414104933}} | 2023-01-23T04:04:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_512_test_meta"
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_512_test_meta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_512_test_meta\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_512_test_meta\"\n\nMore Information needed"
]
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|
c5bb3cfee315427148cd8dbf86d2d244a4b6e195 | # Dataset Card for "mnist_dijkstra_v0.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/mnist_dijkstra_v0.0 | [
"region:us"
]
| 2023-01-23T05:31:23+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "sequence", "sequence": "int64"}, {"name": "occurence", "dtype": "int64"}, {"name": "split", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 84223889, "num_examples": 68614}], "download_size": 12695868, "dataset_size": 84223889}} | 2023-02-04T17:06:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mnist_dijkstra_v0.0"
More Information needed | [
"# Dataset Card for \"mnist_dijkstra_v0.0\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mnist_dijkstra_v0.0\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mnist_dijkstra_v0.0\"\n\nMore Information needed"
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|
837cbf43f126704bda094ab7649c5bcbef8c5320 | # Dataset Card for SLF5K
## Dataset Description
- **Repository: https://github.com/JeremyAlain/imitation_learning_from_language_feedback**
- **Paper: Training Language Models with Language Feedback at Scale**
- **Point of Contact: [email protected] and [email protected]**
### Dataset Summary
The Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used
for the task of abstraction summarization. Each sample consists
of a Reddit title and post, a model-generated ([FeedME](https://beta.openai.com/docs/model-index-for-researchers)) summary, and human-written language feedback on that summary.
Additionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post.
Lastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human.
The dataset can be used to train language models with language feedback on abstractive summarization. It can also be
used to train a reward model on binary preferences.
The Reddit posts were taken from the datasets provided by [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf), who used the initial Reddit post dataset
[TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf).
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on
human-written summaries, or leverage language feedback or binary human preferences.
The model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries.
Previous work has used [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) scores, but in [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf) they
show that ROUGE is not an ideal metric.
### Languages
English
## Dataset Structure
### Data Instances
Each instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are
Here is an example instance:
```
{"id":"t3_3w7gyp",
"subreddit":"dogs",
"title":"Puppy playing at park - other owner aggressive towards him [help]",
"post":"Hi all, looking for some advice. I have a 6m old kelpie, buzz, who goes with me daily to a dog park, [...]",
"tldr_human_reference_summary":"other owner at park harsh with my dog for playing to rough with his. Have tried talking to him about it, hasn't helped.",
"summary_prompt":"Write an excellent summary of the given text.\n\nTitle: Puppy playing at park - other owner aggressive towards him [help]\n\nText: Hi all, looking for some advice. [...] that too.\n\nTL;DR:",
"generated_summary_for_comparison_A":"New dog at park is being aggressive to my pup, owner won't stop. What do I do?",
"generated_summary_for_comparison_B":"A new dog has been coming to the dog park and the first day the new dog came, the old dog (a kelpie) was all over him.",
"generated_summary_for_feedback":"A new dog has been coming to the dog park and the first day the owner hauled buzz off and whacked him. Today, the owner was staring daggers at me and lunging at buzz\/pulling his collar roughly.",
"comparison_preference":"Summary A",
"feedback":"The summary is concise but could include information about the poster knowing the dogs are just playing and will react if they become aggressive and wants to know how to handle things with Max's dad. ",
"feedback_class":"Coverage",
"has_additional_feedback":"No",
"ideal_human_summary":"The poster is frustrated with a new person at the dog park who is upset with him because their young dogs are playing roughly. The poster will step in if it gets aggressive and wants the new person to understand this. "}
```
There are some additional fields like `time_spent_in_seconds_ideal_human_summary`, `time_spent_in_seconds_feedback`,`time_spent_in_seconds_comparison` which only have values for the development dataset.
### Data Fields
- `id`: a unique string identifying the reddit post.
- `subreddit`: subreddit of the post.
- `title`: title of the reddit post.
- `post`: reddit post
- `tldr_human_reference_summary`: human reference summary automatically extracted from reddit (taken from the dataset of [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf))
- `summary_prompt`: the whole prompt used to generate summaries
- `generated_summary_for_comparison_A`: summary A used for binary human comparison (generated with FeedME)
- `generated_summary_for_comparison_B`: summary B used for binary human comparison (generated with FeedME)
- `generated_summary_for_feedback`: summary used to gather human language feedback ((generated with FeedME))
- `comparison_preference`: prefered Summary of human comparison, Values: "Summary A", "Summary B"
- `feedback`: human language feedback on `generated_summary_for_feedback`(most important feedback point)
- `feedback_class`: Class of language feedback, Values: "Coverage", "Accuracy", "Coherence", "other"
- `has_additional_feedback`: Whether this sample could use more feedback on an important point.
- `ideal_human_summary`: high-quality human-written summary for this sample. We instructed annotators to write an ideal summary.
- `time_spent_in_seconds_ideal_human_summary`: Annotation time for ideal human summary
- `time_spent_in_seconds_feedback`: Annotation time for language feedback
- `time_spent_in_seconds_comparison`: Annotation time for binary comparison
Note that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None.
### Data Splits
The SLF5K dataset has 4 splits: _train_, _development_, _validation_, and _test_. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 5000 |
| Development | 200 |
| Validation | 500 |
| Test | 698 |
The reason we introduce a development and validation dataset, is the following.
## Dataset Creation
### Curation Rationale
This dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data.
### Source Data
#### Initial Data Collection and Normalization
The initial TL;DR dataset was made public by Völkse et. al. in the paper [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf) (licensed under CC By 4.0).
Stiennon et. al. then use this TL;DR dataset for their work [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf).
They filter the TL;DR dataset for quality reasons and collect binary human preference labels.
Our datset is a subset from Stiennon et. al. Dataset, which can be downloaded [here](https://github.com/openai/summarize-from-feedback).
Our train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest.
#### Who are the source language producers?
The reddit posts are written by users of reddit.com.
### Annotations
#### Annotation process
We first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31
annotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process
we updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc.
The various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation
5000 samples for the train dataset.
#### Who are the annotators?
We used annotators through the annotation service [Surge AI](https://www.surgehq.ai/).
### Personal and Sensitive Information
The annotators were completely anonymized and no information about them can be found in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to align language models with human preferences by leveraging language feedback, on the task of summarization. Concretely, the goal is to
to develop models that produce summaries for reddit posts that are more in line with human preferences.
Note that this does not imply that the outputs will perfectly be aligned with human values, i.e. outputs can still be misaligned, offensive and contain harumful biases.
While outputs from a model trained on our dataset may reflect the language of the reddit posts, summaries, and human feedback, it should always be made clear that such an output
is automatically generated.
### Discussion of Biases
The TL;DR dataset consists of user-submitted posts to the website reddit.com. It can thus contain content that is offensive or reflects harmful social biases.
We thus recommend that models trained on the SLF5K dataset (which is based on the TL;DR) dataset be thoroughly studied for potential harmful behavior.
The human preferences and feedback represented in this dataset were collected through crowd-workers and may disproportionally represent the views, biases, and values
of the respective demographic of the annotators.
### Other Known Limitations
The "human-summaries" collected in the TL;DR dataset (and available in the SLF5K dataset under the field `tldr_human_reference_summary`, were automatically extracted from reddit.com.
They are often of poor quality and do not accurately reflect human summarization performance. In our paper, we show that our human written summaries (available in the SLF5K dataset under the field
`ideal_human_summary`) are of much higher quality.
## Additional Information
### Dataset Curators
The data is collected by Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez.
All authors are affiliated with New York University. Additionally, Jérémy Scheurer is affiliated with FAR AI. Jon Ander
is affiliated with the University of the Basque Country. Tomek Korbak is affiliated with FAR AI and the University of Sussesx.
Kyunghyun Cho is affiliated with Genentech and CIFAR LMB. Ethan Perez is affiliated with FAR AI and Anthropic.
### Licensing Information
The SLF5K dataset is released under the Apache 2.0 license.
### Citation Information
TBD | JeremyAlain/SLF5K | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"feedback",
"human feedback",
"language feedback",
"binary feedback",
"reward",
"reward model",
"gpt3",
"gpt-3",
"instructgpt",
"alignment",
"ai alignment",
"scale",
"imitation learning from language feedback",
"ilf",
"arxiv:2009.01325",
"region:us"
]
| 2023-01-23T08:44:34+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "SLF5K", "tags": ["feedback", "human feedback", "language feedback", "binary feedback", "reward", "reward model", "gpt3", "gpt-3", "instructgpt", "alignment", "ai alignment", "scale", "imitation learning from language feedback", "ilf"]} | 2023-01-24T14:21:35+00:00 | [
"2009.01325"
]
| [
"en"
]
| TAGS
#task_categories-summarization #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #feedback #human feedback #language feedback #binary feedback #reward #reward model #gpt3 #gpt-3 #instructgpt #alignment #ai alignment #scale #imitation learning from language feedback #ilf #arxiv-2009.01325 #region-us
| Dataset Card for SLF5K
======================
Dataset Description
-------------------
* Repository: URL
* Paper: Training Language Models with Language Feedback at Scale
* Point of Contact: jeremy.scheurer@URL and ethan@URL
### Dataset Summary
The Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used
for the task of abstraction summarization. Each sample consists
of a Reddit title and post, a model-generated (FeedME) summary, and human-written language feedback on that summary.
Additionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post.
Lastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human.
The dataset can be used to train language models with language feedback on abstractive summarization. It can also be
used to train a reward model on binary preferences.
The Reddit posts were taken from the datasets provided by Learning to Summarize from Human Feedbback, who used the initial Reddit post dataset
TL;DR: Mining Reddit to Learn Automatic Summarization.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on
human-written summaries, or leverage language feedback or binary human preferences.
The model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries.
Previous work has used ROUGE scores, but in Learning to Summarize from Human Feedbback they
show that ROUGE is not an ideal metric.
### Languages
English
Dataset Structure
-----------------
### Data Instances
Each instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are
Here is an example instance:
There are some additional fields like 'time\_spent\_in\_seconds\_ideal\_human\_summary', 'time\_spent\_in\_seconds\_feedback','time\_spent\_in\_seconds\_comparison' which only have values for the development dataset.
### Data Fields
* 'id': a unique string identifying the reddit post.
* 'subreddit': subreddit of the post.
* 'title': title of the reddit post.
* 'post': reddit post
* 'tldr\_human\_reference\_summary': human reference summary automatically extracted from reddit (taken from the dataset of TL;DR: Mining Reddit to Learn Automatic Summarization)
* 'summary\_prompt': the whole prompt used to generate summaries
* 'generated\_summary\_for\_comparison\_A': summary A used for binary human comparison (generated with FeedME)
* 'generated\_summary\_for\_comparison\_B': summary B used for binary human comparison (generated with FeedME)
* 'generated\_summary\_for\_feedback': summary used to gather human language feedback ((generated with FeedME))
* 'comparison\_preference': prefered Summary of human comparison, Values: "Summary A", "Summary B"
* 'feedback': human language feedback on 'generated\_summary\_for\_feedback'(most important feedback point)
* 'feedback\_class': Class of language feedback, Values: "Coverage", "Accuracy", "Coherence", "other"
* 'has\_additional\_feedback': Whether this sample could use more feedback on an important point.
* 'ideal\_human\_summary': high-quality human-written summary for this sample. We instructed annotators to write an ideal summary.
* 'time\_spent\_in\_seconds\_ideal\_human\_summary': Annotation time for ideal human summary
* 'time\_spent\_in\_seconds\_feedback': Annotation time for language feedback
* 'time\_spent\_in\_seconds\_comparison': Annotation time for binary comparison
Note that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None.
### Data Splits
The SLF5K dataset has 4 splits: *train*, *development*, *validation*, and *test*. Below are the statistics of the dataset.
The reason we introduce a development and validation dataset, is the following.
Dataset Creation
----------------
### Curation Rationale
This dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data.
### Source Data
#### Initial Data Collection and Normalization
The initial TL;DR dataset was made public by Völkse et. al. in the paper TL;DR: Mining Reddit to Learn Automatic Summarization (licensed under CC By 4.0).
Stiennon et. al. then use this TL;DR dataset for their work Learning to Summarize from Human Feedbback.
They filter the TL;DR dataset for quality reasons and collect binary human preference labels.
Our datset is a subset from Stiennon et. al. Dataset, which can be downloaded here.
Our train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest.
#### Who are the source language producers?
The reddit posts are written by users of URL.
### Annotations
#### Annotation process
We first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31
annotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process
we updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc.
The various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation
5000 samples for the train dataset.
#### Who are the annotators?
We used annotators through the annotation service Surge AI.
### Personal and Sensitive Information
The annotators were completely anonymized and no information about them can be found in the dataset.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
The purpose of this dataset is to align language models with human preferences by leveraging language feedback, on the task of summarization. Concretely, the goal is to
to develop models that produce summaries for reddit posts that are more in line with human preferences.
Note that this does not imply that the outputs will perfectly be aligned with human values, i.e. outputs can still be misaligned, offensive and contain harumful biases.
While outputs from a model trained on our dataset may reflect the language of the reddit posts, summaries, and human feedback, it should always be made clear that such an output
is automatically generated.
### Discussion of Biases
The TL;DR dataset consists of user-submitted posts to the website URL. It can thus contain content that is offensive or reflects harmful social biases.
We thus recommend that models trained on the SLF5K dataset (which is based on the TL;DR) dataset be thoroughly studied for potential harmful behavior.
The human preferences and feedback represented in this dataset were collected through crowd-workers and may disproportionally represent the views, biases, and values
of the respective demographic of the annotators.
### Other Known Limitations
The "human-summaries" collected in the TL;DR dataset (and available in the SLF5K dataset under the field 'tldr\_human\_reference\_summary', were automatically extracted from URL.
They are often of poor quality and do not accurately reflect human summarization performance. In our paper, we show that our human written summaries (available in the SLF5K dataset under the field
'ideal\_human\_summary') are of much higher quality.
Additional Information
----------------------
### Dataset Curators
The data is collected by Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez.
All authors are affiliated with New York University. Additionally, Jérémy Scheurer is affiliated with FAR AI. Jon Ander
is affiliated with the University of the Basque Country. Tomek Korbak is affiliated with FAR AI and the University of Sussesx.
Kyunghyun Cho is affiliated with Genentech and CIFAR LMB. Ethan Perez is affiliated with FAR AI and Anthropic.
### Licensing Information
The SLF5K dataset is released under the Apache 2.0 license.
TBD
| [
"### Dataset Summary\n\n\nThe Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used\nfor the task of abstraction summarization. Each sample consists\nof a Reddit title and post, a model-generated (FeedME) summary, and human-written language feedback on that summary.\nAdditionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post.\nLastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human.\nThe dataset can be used to train language models with language feedback on abstractive summarization. It can also be\nused to train a reward model on binary preferences.\n\n\nThe Reddit posts were taken from the datasets provided by Learning to Summarize from Human Feedbback, who used the initial Reddit post dataset\nTL;DR: Mining Reddit to Learn Automatic Summarization.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on\nhuman-written summaries, or leverage language feedback or binary human preferences.\nThe model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries.\nPrevious work has used ROUGE scores, but in Learning to Summarize from Human Feedbback they\nshow that ROUGE is not an ideal metric.",
"### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nEach instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are\nHere is an example instance:\n\n\nThere are some additional fields like 'time\\_spent\\_in\\_seconds\\_ideal\\_human\\_summary', 'time\\_spent\\_in\\_seconds\\_feedback','time\\_spent\\_in\\_seconds\\_comparison' which only have values for the development dataset.",
"### Data Fields\n\n\n* 'id': a unique string identifying the reddit post.\n* 'subreddit': subreddit of the post.\n* 'title': title of the reddit post.\n* 'post': reddit post\n* 'tldr\\_human\\_reference\\_summary': human reference summary automatically extracted from reddit (taken from the dataset of TL;DR: Mining Reddit to Learn Automatic Summarization)\n* 'summary\\_prompt': the whole prompt used to generate summaries\n* 'generated\\_summary\\_for\\_comparison\\_A': summary A used for binary human comparison (generated with FeedME)\n* 'generated\\_summary\\_for\\_comparison\\_B': summary B used for binary human comparison (generated with FeedME)\n* 'generated\\_summary\\_for\\_feedback': summary used to gather human language feedback ((generated with FeedME))\n* 'comparison\\_preference': prefered Summary of human comparison, Values: \"Summary A\", \"Summary B\"\n* 'feedback': human language feedback on 'generated\\_summary\\_for\\_feedback'(most important feedback point)\n* 'feedback\\_class': Class of language feedback, Values: \"Coverage\", \"Accuracy\", \"Coherence\", \"other\"\n* 'has\\_additional\\_feedback': Whether this sample could use more feedback on an important point.\n* 'ideal\\_human\\_summary': high-quality human-written summary for this sample. We instructed annotators to write an ideal summary.\n* 'time\\_spent\\_in\\_seconds\\_ideal\\_human\\_summary': Annotation time for ideal human summary\n* 'time\\_spent\\_in\\_seconds\\_feedback': Annotation time for language feedback\n* 'time\\_spent\\_in\\_seconds\\_comparison': Annotation time for binary comparison\n\n\nNote that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None.",
"### Data Splits\n\n\nThe SLF5K dataset has 4 splits: *train*, *development*, *validation*, and *test*. Below are the statistics of the dataset.\n\n\n\nThe reason we introduce a development and validation dataset, is the following.\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThis dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe initial TL;DR dataset was made public by Völkse et. al. in the paper TL;DR: Mining Reddit to Learn Automatic Summarization (licensed under CC By 4.0).\nStiennon et. al. then use this TL;DR dataset for their work Learning to Summarize from Human Feedbback.\nThey filter the TL;DR dataset for quality reasons and collect binary human preference labels.\n\n\nOur datset is a subset from Stiennon et. al. Dataset, which can be downloaded here.\nOur train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest.",
"#### Who are the source language producers?\n\n\nThe reddit posts are written by users of URL.",
"### Annotations",
"#### Annotation process\n\n\nWe first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31\nannotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process\nwe updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc.\n\n\nThe various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation\n5000 samples for the train dataset.",
"#### Who are the annotators?\n\n\nWe used annotators through the annotation service Surge AI.",
"### Personal and Sensitive Information\n\n\nThe annotators were completely anonymized and no information about them can be found in the dataset.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe purpose of this dataset is to align language models with human preferences by leveraging language feedback, on the task of summarization. Concretely, the goal is to\nto develop models that produce summaries for reddit posts that are more in line with human preferences.\nNote that this does not imply that the outputs will perfectly be aligned with human values, i.e. outputs can still be misaligned, offensive and contain harumful biases.\n\n\nWhile outputs from a model trained on our dataset may reflect the language of the reddit posts, summaries, and human feedback, it should always be made clear that such an output\nis automatically generated.",
"### Discussion of Biases\n\n\nThe TL;DR dataset consists of user-submitted posts to the website URL. It can thus contain content that is offensive or reflects harmful social biases.\nWe thus recommend that models trained on the SLF5K dataset (which is based on the TL;DR) dataset be thoroughly studied for potential harmful behavior.\n\n\nThe human preferences and feedback represented in this dataset were collected through crowd-workers and may disproportionally represent the views, biases, and values\nof the respective demographic of the annotators.",
"### Other Known Limitations\n\n\nThe \"human-summaries\" collected in the TL;DR dataset (and available in the SLF5K dataset under the field 'tldr\\_human\\_reference\\_summary', were automatically extracted from URL.\nThey are often of poor quality and do not accurately reflect human summarization performance. In our paper, we show that our human written summaries (available in the SLF5K dataset under the field\n'ideal\\_human\\_summary') are of much higher quality.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe data is collected by Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez.\nAll authors are affiliated with New York University. Additionally, Jérémy Scheurer is affiliated with FAR AI. Jon Ander\nis affiliated with the University of the Basque Country. Tomek Korbak is affiliated with FAR AI and the University of Sussesx.\nKyunghyun Cho is affiliated with Genentech and CIFAR LMB. Ethan Perez is affiliated with FAR AI and Anthropic.",
"### Licensing Information\n\n\nThe SLF5K dataset is released under the Apache 2.0 license.\n\n\nTBD"
]
| [
"TAGS\n#task_categories-summarization #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #feedback #human feedback #language feedback #binary feedback #reward #reward model #gpt3 #gpt-3 #instructgpt #alignment #ai alignment #scale #imitation learning from language feedback #ilf #arxiv-2009.01325 #region-us \n",
"### Dataset Summary\n\n\nThe Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used\nfor the task of abstraction summarization. Each sample consists\nof a Reddit title and post, a model-generated (FeedME) summary, and human-written language feedback on that summary.\nAdditionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post.\nLastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human.\nThe dataset can be used to train language models with language feedback on abstractive summarization. It can also be\nused to train a reward model on binary preferences.\n\n\nThe Reddit posts were taken from the datasets provided by Learning to Summarize from Human Feedbback, who used the initial Reddit post dataset\nTL;DR: Mining Reddit to Learn Automatic Summarization.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on\nhuman-written summaries, or leverage language feedback or binary human preferences.\nThe model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries.\nPrevious work has used ROUGE scores, but in Learning to Summarize from Human Feedbback they\nshow that ROUGE is not an ideal metric.",
"### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nEach instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are\nHere is an example instance:\n\n\nThere are some additional fields like 'time\\_spent\\_in\\_seconds\\_ideal\\_human\\_summary', 'time\\_spent\\_in\\_seconds\\_feedback','time\\_spent\\_in\\_seconds\\_comparison' which only have values for the development dataset.",
"### Data Fields\n\n\n* 'id': a unique string identifying the reddit post.\n* 'subreddit': subreddit of the post.\n* 'title': title of the reddit post.\n* 'post': reddit post\n* 'tldr\\_human\\_reference\\_summary': human reference summary automatically extracted from reddit (taken from the dataset of TL;DR: Mining Reddit to Learn Automatic Summarization)\n* 'summary\\_prompt': the whole prompt used to generate summaries\n* 'generated\\_summary\\_for\\_comparison\\_A': summary A used for binary human comparison (generated with FeedME)\n* 'generated\\_summary\\_for\\_comparison\\_B': summary B used for binary human comparison (generated with FeedME)\n* 'generated\\_summary\\_for\\_feedback': summary used to gather human language feedback ((generated with FeedME))\n* 'comparison\\_preference': prefered Summary of human comparison, Values: \"Summary A\", \"Summary B\"\n* 'feedback': human language feedback on 'generated\\_summary\\_for\\_feedback'(most important feedback point)\n* 'feedback\\_class': Class of language feedback, Values: \"Coverage\", \"Accuracy\", \"Coherence\", \"other\"\n* 'has\\_additional\\_feedback': Whether this sample could use more feedback on an important point.\n* 'ideal\\_human\\_summary': high-quality human-written summary for this sample. We instructed annotators to write an ideal summary.\n* 'time\\_spent\\_in\\_seconds\\_ideal\\_human\\_summary': Annotation time for ideal human summary\n* 'time\\_spent\\_in\\_seconds\\_feedback': Annotation time for language feedback\n* 'time\\_spent\\_in\\_seconds\\_comparison': Annotation time for binary comparison\n\n\nNote that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None.",
"### Data Splits\n\n\nThe SLF5K dataset has 4 splits: *train*, *development*, *validation*, and *test*. Below are the statistics of the dataset.\n\n\n\nThe reason we introduce a development and validation dataset, is the following.\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThis dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe initial TL;DR dataset was made public by Völkse et. al. in the paper TL;DR: Mining Reddit to Learn Automatic Summarization (licensed under CC By 4.0).\nStiennon et. al. then use this TL;DR dataset for their work Learning to Summarize from Human Feedbback.\nThey filter the TL;DR dataset for quality reasons and collect binary human preference labels.\n\n\nOur datset is a subset from Stiennon et. al. Dataset, which can be downloaded here.\nOur train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest.",
"#### Who are the source language producers?\n\n\nThe reddit posts are written by users of URL.",
"### Annotations",
"#### Annotation process\n\n\nWe first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31\nannotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process\nwe updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc.\n\n\nThe various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation\n5000 samples for the train dataset.",
"#### Who are the annotators?\n\n\nWe used annotators through the annotation service Surge AI.",
"### Personal and Sensitive Information\n\n\nThe annotators were completely anonymized and no information about them can be found in the dataset.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe purpose of this dataset is to align language models with human preferences by leveraging language feedback, on the task of summarization. Concretely, the goal is to\nto develop models that produce summaries for reddit posts that are more in line with human preferences.\nNote that this does not imply that the outputs will perfectly be aligned with human values, i.e. outputs can still be misaligned, offensive and contain harumful biases.\n\n\nWhile outputs from a model trained on our dataset may reflect the language of the reddit posts, summaries, and human feedback, it should always be made clear that such an output\nis automatically generated.",
"### Discussion of Biases\n\n\nThe TL;DR dataset consists of user-submitted posts to the website URL. It can thus contain content that is offensive or reflects harmful social biases.\nWe thus recommend that models trained on the SLF5K dataset (which is based on the TL;DR) dataset be thoroughly studied for potential harmful behavior.\n\n\nThe human preferences and feedback represented in this dataset were collected through crowd-workers and may disproportionally represent the views, biases, and values\nof the respective demographic of the annotators.",
"### Other Known Limitations\n\n\nThe \"human-summaries\" collected in the TL;DR dataset (and available in the SLF5K dataset under the field 'tldr\\_human\\_reference\\_summary', were automatically extracted from URL.\nThey are often of poor quality and do not accurately reflect human summarization performance. In our paper, we show that our human written summaries (available in the SLF5K dataset under the field\n'ideal\\_human\\_summary') are of much higher quality.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe data is collected by Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez.\nAll authors are affiliated with New York University. Additionally, Jérémy Scheurer is affiliated with FAR AI. Jon Ander\nis affiliated with the University of the Basque Country. Tomek Korbak is affiliated with FAR AI and the University of Sussesx.\nKyunghyun Cho is affiliated with Genentech and CIFAR LMB. Ethan Perez is affiliated with FAR AI and Anthropic.",
"### Licensing Information\n\n\nThe SLF5K dataset is released under the Apache 2.0 license.\n\n\nTBD"
]
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24
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| [
"passage: TAGS\n#task_categories-summarization #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #feedback #human feedback #language feedback #binary feedback #reward #reward model #gpt3 #gpt-3 #instructgpt #alignment #ai alignment #scale #imitation learning from language feedback #ilf #arxiv-2009.01325 #region-us \n### Dataset Summary\n\n\nThe Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used\nfor the task of abstraction summarization. Each sample consists\nof a Reddit title and post, a model-generated (FeedME) summary, and human-written language feedback on that summary.\nAdditionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post.\nLastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human.\nThe dataset can be used to train language models with language feedback on abstractive summarization. It can also be\nused to train a reward model on binary preferences.\n\n\nThe Reddit posts were taken from the datasets provided by Learning to Summarize from Human Feedbback, who used the initial Reddit post dataset\nTL;DR: Mining Reddit to Learn Automatic Summarization.### Supported Tasks and Leaderboards\n\n\nThe dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on\nhuman-written summaries, or leverage language feedback or binary human preferences.\nThe model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries.\nPrevious work has used ROUGE scores, but in Learning to Summarize from Human Feedbback they\nshow that ROUGE is not an ideal metric.### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------",
"passage: ### Data Instances\n\n\nEach instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are\nHere is an example instance:\n\n\nThere are some additional fields like 'time\\_spent\\_in\\_seconds\\_ideal\\_human\\_summary', 'time\\_spent\\_in\\_seconds\\_feedback','time\\_spent\\_in\\_seconds\\_comparison' which only have values for the development dataset.### Data Fields\n\n\n* 'id': a unique string identifying the reddit post.\n* 'subreddit': subreddit of the post.\n* 'title': title of the reddit post.\n* 'post': reddit post\n* 'tldr\\_human\\_reference\\_summary': human reference summary automatically extracted from reddit (taken from the dataset of TL;DR: Mining Reddit to Learn Automatic Summarization)\n* 'summary\\_prompt': the whole prompt used to generate summaries\n* 'generated\\_summary\\_for\\_comparison\\_A': summary A used for binary human comparison (generated with FeedME)\n* 'generated\\_summary\\_for\\_comparison\\_B': summary B used for binary human comparison (generated with FeedME)\n* 'generated\\_summary\\_for\\_feedback': summary used to gather human language feedback ((generated with FeedME))\n* 'comparison\\_preference': prefered Summary of human comparison, Values: \"Summary A\", \"Summary B\"\n* 'feedback': human language feedback on 'generated\\_summary\\_for\\_feedback'(most important feedback point)\n* 'feedback\\_class': Class of language feedback, Values: \"Coverage\", \"Accuracy\", \"Coherence\", \"other\"\n* 'has\\_additional\\_feedback': Whether this sample could use more feedback on an important point.\n* 'ideal\\_human\\_summary': high-quality human-written summary for this sample. We instructed annotators to write an ideal summary.\n* 'time\\_spent\\_in\\_seconds\\_ideal\\_human\\_summary': Annotation time for ideal human summary\n* 'time\\_spent\\_in\\_seconds\\_feedback': Annotation time for language feedback\n* 'time\\_spent\\_in\\_seconds\\_comparison': Annotation time for binary comparison\n\n\nNote that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None.",
"passage: ### Data Splits\n\n\nThe SLF5K dataset has 4 splits: *train*, *development*, *validation*, and *test*. Below are the statistics of the dataset.\n\n\n\nThe reason we introduce a development and validation dataset, is the following.\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nThis dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data.### Source Data#### Initial Data Collection and Normalization\n\n\nThe initial TL;DR dataset was made public by Völkse et. al. in the paper TL;DR: Mining Reddit to Learn Automatic Summarization (licensed under CC By 4.0).\nStiennon et. al. then use this TL;DR dataset for their work Learning to Summarize from Human Feedbback.\nThey filter the TL;DR dataset for quality reasons and collect binary human preference labels.\n\n\nOur datset is a subset from Stiennon et. al. Dataset, which can be downloaded here.\nOur train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest.#### Who are the source language producers?\n\n\nThe reddit posts are written by users of URL.### Annotations#### Annotation process\n\n\nWe first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31\nannotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process\nwe updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc.\n\n\nThe various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation\n5000 samples for the train dataset.#### Who are the annotators?\n\n\nWe used annotators through the annotation service Surge AI.### Personal and Sensitive Information\n\n\nThe annotators were completely anonymized and no information about them can be found in the dataset.\n\n\nConsiderations for Using the Data\n---------------------------------"
]
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|
62b5618758a398f989de278272fe605ecf9878de | # Dataset Card for "pexel_friends"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_friends | [
"region:us"
]
| 2023-01-23T09:32:49+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2906655034.625, "num_examples": 7995}], "download_size": 490223516, "dataset_size": 2906655034.625}} | 2023-01-23T09:46:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pexel_friends"
More Information needed | [
"# Dataset Card for \"pexel_friends\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pexel_friends\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pexel_friends\"\n\nMore Information needed"
]
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|
22cf1bdc4e49b115220c782203f6504be1181774 | # CelebA Dataset
CelebA Dataset is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image.
## Usage
It is composed of 3 sets of images:
* Training
* Validation
* Test
## Example
The dataset returns each item as a dictionary with the following fields:
```
{
"image": image,
"bbox": [x1, y1, w, h],
"facial_landmarks": {
"lefteye": [x1, y1],
"righteye": [x2, y2],
"nose": [x3, y3],
"leftmouth": [x4, y4],
"rightmouth": [x5, y5]
}
}
```
## License
CelebA Dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). | hfaus/CelebA_bbox_and_facepoints | [
"size_categories:n<1K",
"region:us"
]
| 2023-01-23T11:04:45+00:00 | {"size_categories": ["n<1K"]} | 2023-01-28T09:34:39+00:00 | []
| []
| TAGS
#size_categories-n<1K #region-us
| # CelebA Dataset
CelebA Dataset is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image.
## Usage
It is composed of 3 sets of images:
* Training
* Validation
* Test
## Example
The dataset returns each item as a dictionary with the following fields:
## License
CelebA Dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). | [
"# CelebA Dataset\n\nCelebA Dataset is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image.",
"## Usage\n\nIt is composed of 3 sets of images:\n\n* Training\n* Validation\n* Test",
"## Example\n\nThe dataset returns each item as a dictionary with the following fields:",
"## License\n\nCelebA Dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)."
]
| [
"TAGS\n#size_categories-n<1K #region-us \n",
"# CelebA Dataset\n\nCelebA Dataset is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image.",
"## Usage\n\nIt is composed of 3 sets of images:\n\n* Training\n* Validation\n* Test",
"## Example\n\nThe dataset returns each item as a dictionary with the following fields:",
"## License\n\nCelebA Dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)."
]
| [
16,
109,
22,
21,
35
]
| [
"passage: TAGS\n#size_categories-n<1K #region-us \n# CelebA Dataset\n\nCelebA Dataset is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image.## Usage\n\nIt is composed of 3 sets of images:\n\n* Training\n* Validation\n* Test## Example\n\nThe dataset returns each item as a dictionary with the following fields:## License\n\nCelebA Dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)."
]
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|
8803964e7afd58380010264fe1e58dd3cd59f304 | # AutoTrain Dataset for project: big_tm4
## Dataset Description
This dataset has been automatically processed by AutoTrain for project big_tm4.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "I would like to request the count of vendors that are situated in Houston and have received a purchase order.",
"target": "select max(RETAILBUYER_VENDOR.vendor_id) from RETAILBUYER_POHEADER\ninner join\nRETAILBUYER_VENDOR \non\nRETAILBUYER_POHEADER.vendor_id = RETAILBUYER_VENDOR.vendor_id\nwhere RETAILBUYER_VENDOR.Vendor_City = 'Houston'"
},
{
"text": "List all vendors and their details for whom no PO has been issued",
"target": "select * from RETAILBUYER_VENDOR\nwhere vendor_id not in (select vendor_id from RETAILBUYER_POHEADER)"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 356 |
| valid | 90 |
| Aman6917/autotrain-data-big_tm4 | [
"task_categories:summarization",
"region:us"
]
| 2023-01-23T11:43:56+00:00 | {"task_categories": ["summarization"]} | 2023-01-23T12:45:31+00:00 | []
| []
| TAGS
#task_categories-summarization #region-us
| AutoTrain Dataset for project: big\_tm4
=======================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project big\_tm4.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-summarization #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
16,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-summarization #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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]
|
16b3ebb3b831dd0cbf832ea0c25c963a71beb5b6 |
# ⚕️ health-questions
TODO | heinrichreimer/health-questions | [
"size_categories:1M<n<10M",
"language:en",
"Health",
"Question Answering",
"region:us"
]
| 2023-01-23T11:47:06+00:00 | {"language": ["en"], "size_categories": ["1M<n<10M"], "tags": ["Health", "Question Answering"], "dataset_info": [{"config_name": "silver", "features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "health_related_label", "dtype": {"class_label": {"names": {"0": "not_health_related", "1": "health_related"}}}}, {"name": "medical_label", "dtype": {"class_label": {"names": {"0": "not_medical", "1": "medical"}}}}], "splits": [{"name": "train", "num_bytes": 750040934, "num_examples": 6835271}, {"name": "validation", "num_bytes": 187523993, "num_examples": 1708818}], "download_size": 0, "dataset_size": 937564927}, {"config_name": "golden", "features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "health_related_label", "dtype": {"class_label": {"names": {"0": "not_health_related", "1": "health_related"}}}}, {"name": "medical_label", "dtype": {"class_label": {"names": {"0": "not_medical", "1": "medical"}}}}], "splits": [{"name": "test", "num_bytes": 163495, "num_examples": 1489}, {"name": "train", "num_bytes": 489298, "num_examples": 4466}, {"name": "validation", "num_bytes": 163015, "num_examples": 1489}], "download_size": 0, "dataset_size": 815808}]} | 2023-01-23T16:42:37+00:00 | []
| [
"en"
]
| TAGS
#size_categories-1M<n<10M #language-English #Health #Question Answering #region-us
|
# ️ health-questions
TODO | [
"# ️ health-questions\n\nTODO"
]
| [
"TAGS\n#size_categories-1M<n<10M #language-English #Health #Question Answering #region-us \n",
"# ️ health-questions\n\nTODO"
]
| [
29,
9
]
| [
"passage: TAGS\n#size_categories-1M<n<10M #language-English #Health #Question Answering #region-us \n# ️ health-questions\n\nTODO"
]
| [
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|
1f56c0ab8f18531e43c5c772f1a6dc74dae83ac1 | The dataset containes test.csv and train.csv of skin crops 128x128.
| vgg/skin_data | [
"region:us"
]
| 2023-01-23T11:50:07+00:00 | {} | 2023-01-23T13:48:10+00:00 | []
| []
| TAGS
#region-us
| The dataset containes URL and URL of skin crops 128x128.
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
bb2433b96b49bc502271d4224a73a925b1df5539 | # Dataset Card for "AToMiC-Images-v0.2-medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justram/AToMiC-Images-v0.2-medium | [
"region:us"
]
| 2023-01-23T12:34:40+00:00 | {"dataset_info": {"features": [{"name": "image_url", "dtype": "string"}, {"name": "image_id", "dtype": "string"}, {"name": "language", "sequence": "string"}, {"name": "caption_reference_description", "sequence": "string"}, {"name": "caption_alt_text_description", "sequence": "string"}, {"name": "caption_attribution_description", "sequence": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 152304669.4521026, "num_examples": 9322}, {"name": "validation", "num_bytes": 266345282.27935815, "num_examples": 16302}, {"name": "train", "num_bytes": 61149034067.48147, "num_examples": 3742704}], "download_size": 32462289032, "dataset_size": 61567684019.21293}} | 2023-01-23T14:01:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AToMiC-Images-v0.2-medium"
More Information needed | [
"# Dataset Card for \"AToMiC-Images-v0.2-medium\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AToMiC-Images-v0.2-medium\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AToMiC-Images-v0.2-medium\"\n\nMore Information needed"
]
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|
950956d618f7adc50ee9e06d3bdd5eb73425be4a | # Dataset Card for "AToMiC-Texts-v0.2-medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justram/AToMiC-Texts-v0.2-medium | [
"region:us"
]
| 2023-01-23T12:37:49+00:00 | {"dataset_info": {"features": [{"name": "text_id", "dtype": "string"}, {"name": "page_url", "dtype": "string"}, {"name": "page_title", "dtype": "string"}, {"name": "section_title", "dtype": "string"}, {"name": "context_page_description", "dtype": "string"}, {"name": "context_section_description", "dtype": "string"}, {"name": "media", "sequence": "string"}, {"name": "hierachy", "sequence": "string"}, {"name": "category", "sequence": "string"}, {"name": "source_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5404754455.050775, "num_examples": 3002458}, {"name": "validation", "num_bytes": 30913287.798392836, "num_examples": 17173}, {"name": "test", "num_bytes": 17772485.321931664, "num_examples": 9873}], "download_size": 2719090777, "dataset_size": 5502126001.291424}} | 2023-01-23T15:49:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AToMiC-Texts-v0.2-medium"
More Information needed | [
"# Dataset Card for \"AToMiC-Texts-v0.2-medium\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AToMiC-Texts-v0.2-medium\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AToMiC-Texts-v0.2-medium\"\n\nMore Information needed"
]
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|
47963b65a897cb884f972bbac237d78d79ade32d | # Dataset Card for "pexel_friends_with_generated_captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_friends_with_generated_captions | [
"region:us"
]
| 2023-01-23T12:46:26+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "generated_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2907255881.625, "num_examples": 7995}], "download_size": 2902632694, "dataset_size": 2907255881.625}} | 2023-01-23T12:51:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pexel_friends_with_generated_captions"
More Information needed | [
"# Dataset Card for \"pexel_friends_with_generated_captions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pexel_friends_with_generated_captions\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pexel_friends_with_generated_captions\"\n\nMore Information needed"
]
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|
22a723953c86a961f25e370b8b61eb14e7615e8a | # Dataset Card for "bookcorpus_compact_1024_test_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_test_meta | [
"region:us"
]
| 2023-01-23T12:46:56+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 758527093, "num_examples": 6160}], "download_size": 169143634, "dataset_size": 758527093}} | 2023-01-23T12:48:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_1024_test_meta"
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_1024_test_meta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_1024_test_meta\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_1024_test_meta\"\n\nMore Information needed"
]
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|
b050b1a855784e4b00ccb94d72f5fc9eec4966b0 | # Dataset Card for "pexel_people"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_people | [
"region:us"
]
| 2023-01-23T13:10:10+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "generated_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5374411376.0, "num_examples": 15994}], "download_size": 3908548281, "dataset_size": 5374411376.0}} | 2023-01-23T14:01:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pexel_people"
More Information needed | [
"# Dataset Card for \"pexel_people\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pexel_people\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pexel_people\"\n\nMore Information needed"
]
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|
15af196dd1d605fd3e5e3025be6a0aa5e03726ec | # Dataset Card for "Emoji_Dataset-Openmoji-BLIP"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | soypablo/Emoji_Dataset-Openmoji-BLIP | [
"region:us"
]
| 2023-01-23T13:20:41+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 85108246.546, "num_examples": 4083}], "download_size": 101495440, "dataset_size": 85108246.546}} | 2023-01-25T10:00:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Emoji_Dataset-Openmoji-BLIP"
More Information needed | [
"# Dataset Card for \"Emoji_Dataset-Openmoji-BLIP\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Emoji_Dataset-Openmoji-BLIP\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Emoji_Dataset-Openmoji-BLIP\"\n\nMore Information needed"
]
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|
fd8beffb788df5f6673bc688e6dcbe3690a3acc6 | https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark
The companion datasets to the STS Benchmark comprise the rest of the English datasets used in the STS tasks organized by us in the context of SemEval between 2012 and 2017.
Authors collated two datasets, one with pairs of sentences related to machine translation evaluation. Another one with the rest of datasets, which can be used for domain adaptation studies.
```bib
@inproceedings{cer-etal-2017-semeval,
title = "{S}em{E}val-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
author = "Cer, Daniel and
Diab, Mona and
Agirre, Eneko and
Lopez-Gazpio, I{\~n}igo and
Specia, Lucia",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2001",
doi = "10.18653/v1/S17-2001",
pages = "1--14",
}
``` | tasksource/sts-companion | [
"task_categories:sentence-similarity",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"sts",
"region:us"
]
| 2023-01-23T13:34:56+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["sentence-similarity", "text-classification"], "tags": ["sts"]} | 2023-02-03T08:36:00+00:00 | []
| [
"en"
]
| TAGS
#task_categories-sentence-similarity #task_categories-text-classification #language-English #license-apache-2.0 #sts #region-us
| URL
The companion datasets to the STS Benchmark comprise the rest of the English datasets used in the STS tasks organized by us in the context of SemEval between 2012 and 2017.
Authors collated two datasets, one with pairs of sentences related to machine translation evaluation. Another one with the rest of datasets, which can be used for domain adaptation studies.
| []
| [
"TAGS\n#task_categories-sentence-similarity #task_categories-text-classification #language-English #license-apache-2.0 #sts #region-us \n"
]
| [
45
]
| [
"passage: TAGS\n#task_categories-sentence-similarity #task_categories-text-classification #language-English #license-apache-2.0 #sts #region-us \n"
]
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|
ddace36e243b1573b2e40ed07f82d62b09b4f9a5 | # Gronings transcribed speech
Demonstration dataset with Gronings transcribed speech based on the dataset released by [San et al. (2021)](https://github.com/fauxneticien/qbe-std_feats_eval).
For more information see the corresponding [ASRU 2021 paper](https://ieeexplore.ieee.org/abstract/document/9688301). | bartelds/gos-demo | [
"task_categories:automatic-speech-recognition",
"language:gos",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-23T14:17:51+00:00 | {"language": ["gos"], "license": "cc-by-4.0", "task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "development", "num_bytes": 6030729, "num_examples": 59}, {"name": "test", "num_bytes": 8229224, "num_examples": 71}, {"name": "train", "num_bytes": 29128904, "num_examples": 300}], "download_size": 43004020, "dataset_size": 43388857}} | 2023-01-23T14:35:26+00:00 | []
| [
"gos"
]
| TAGS
#task_categories-automatic-speech-recognition #language-Gronings #license-cc-by-4.0 #region-us
| # Gronings transcribed speech
Demonstration dataset with Gronings transcribed speech based on the dataset released by San et al. (2021).
For more information see the corresponding ASRU 2021 paper. | [
"# Gronings transcribed speech\n\nDemonstration dataset with Gronings transcribed speech based on the dataset released by San et al. (2021).\n\nFor more information see the corresponding ASRU 2021 paper."
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #language-Gronings #license-cc-by-4.0 #region-us \n",
"# Gronings transcribed speech\n\nDemonstration dataset with Gronings transcribed speech based on the dataset released by San et al. (2021).\n\nFor more information see the corresponding ASRU 2021 paper."
]
| [
36,
44
]
| [
"passage: TAGS\n#task_categories-automatic-speech-recognition #language-Gronings #license-cc-by-4.0 #region-us \n# Gronings transcribed speech\n\nDemonstration dataset with Gronings transcribed speech based on the dataset released by San et al. (2021).\n\nFor more information see the corresponding ASRU 2021 paper."
]
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|
de806fb1036e510f9f4de599bed0cd0029b5286c | # AutoTrain Dataset for project: thycomments
## Dataset Description
This dataset has been automatically processed by HuggingFace AutoTrain for project tktktk.
### Languages
Turkish and English
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "@TK_TR 21 dk beklemem gerekti\u011fi s\u00f6yleniyor, m\u00fc\u015fteri temsilcisi ba\u011flanm\u0131yorum . \u0130nternet sitesinden de i\u015flem yap\u0131lam\u0131yor nas\u0131l \u00e7\u00f6z\u00fcm bulaca\u011f\u0131m ?",
"target": 0
},
{
"text": "@yhyustun Sevgili Yahya Bey Allah Rizasi icin bari sen bir aciklama yaparsan sevinirim.Konu su:Danimarkadan Turkiyeye ucuslar sistemde yok gorunuyor tum Mart ayi icin.1 Mart icin ucusum vardi fakat birkac gun once cagri merkeziyle gorustum ucuslar satisa kapanmis ancak bizim bir haberimiz",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['negative', 'neutral', 'positive'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2398 |
| valid | 601 | | tolgadev/thycomments | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:tr",
"language:en",
"region:us"
]
| 2023-01-23T14:34:30+00:00 | {"language": ["tr", "en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-01-30T14:18:43+00:00 | []
| [
"tr",
"en"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Turkish #language-English #region-us
| AutoTrain Dataset for project: thycomments
==========================================
Dataset Description
-------------------
This dataset has been automatically processed by HuggingFace AutoTrain for project tktktk.
### Languages
Turkish and English
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nTurkish and English",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Turkish #language-English #region-us \n",
"### Languages\n\n\nTurkish and English",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
39,
8,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Turkish #language-English #region-us \n### Languages\n\n\nTurkish and English### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
68c40c1620b63e175f06045e908ead8b3132d30c |
# preprocessed version of rcds/wikipedia-persons-masked
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Contains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text
is masked with a <mask> token. The ground truth for every mask is provided.
Each row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk.
for each chunk the expected name for each mask is given.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering,
e.g. "Who is <mask>?"
### Languages
*english only*
## Dataset Structure
In /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/wikipedia-persons-masked', split='train', type='original', size='512')
```
### Data Fields
Columns are:
- texts: the text chunks
- masks: the names for each of the masks in the chunks
### Data Splits
There are no splits, only a default train.
## Dataset Creation
Created by using the tokenizer from allenai/longformer-base-4096 for the 4096 token per chunk version,
and the xml-roberta-large tokenizer for the 512 token version. Chunks are split to fit those token sizes,
with the splits ensuring no words are split in half.
Possible improvements: Last chunk of a page might be much shorter, could join part of the previous one to have more tokens
in the last chunk.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
TODO add citation
```
### Contributions
Thanks to [@skatinger](https://github.com/skatinger) for adding this dataset.
| rcds/wikipedia-for-mask-filling | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-23T15:14:48+00:00 | {"annotations_creators": ["other"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "wikipedia pages chunked for fill-mask"} | 2023-03-08T12:22:02+00:00 | []
| [
"en"
]
| TAGS
#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc-by-4.0 #region-us
|
# preprocessed version of rcds/wikipedia-persons-masked
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
Contains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text
is masked with a <mask> token. The ground truth for every mask is provided.
Each row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk.
for each chunk the expected name for each mask is given.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering,
e.g. "Who is <mask>?"
### Languages
*english only*
## Dataset Structure
In /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens.
Use the dataset like this:
### Data Fields
Columns are:
- texts: the text chunks
- masks: the names for each of the masks in the chunks
### Data Splits
There are no splits, only a default train.
## Dataset Creation
Created by using the tokenizer from allenai/longformer-base-4096 for the 4096 token per chunk version,
and the xml-roberta-large tokenizer for the 512 token version. Chunks are split to fit those token sizes,
with the splits ensuring no words are split in half.
Possible improvements: Last chunk of a page might be much shorter, could join part of the previous one to have more tokens
in the last chunk.
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @skatinger for adding this dataset.
| [
"# preprocessed version of rcds/wikipedia-persons-masked",
"## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository: \n- Paper: \n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nContains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text\nis masked with a <mask> token. The ground truth for every mask is provided.\nEach row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk.\nfor each chunk the expected name for each mask is given.",
"### Supported Tasks and Leaderboards\n\nThe dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering,\ne.g. \"Who is <mask>?\"",
"### Languages\n\n*english only*",
"## Dataset Structure\n\nIn /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens.\n\nUse the dataset like this:",
"### Data Fields\n\nColumns are:\n- texts: the text chunks\n- masks: the names for each of the masks in the chunks",
"### Data Splits\n\nThere are no splits, only a default train.",
"## Dataset Creation\n\nCreated by using the tokenizer from allenai/longformer-base-4096 for the 4096 token per chunk version,\nand the xml-roberta-large tokenizer for the 512 token version. Chunks are split to fit those token sizes,\nwith the splits ensuring no words are split in half.\nPossible improvements: Last chunk of a page might be much shorter, could join part of the previous one to have more tokens\nin the last chunk.",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @skatinger for adding this dataset."
]
| [
"TAGS\n#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n",
"# preprocessed version of rcds/wikipedia-persons-masked",
"## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository: \n- Paper: \n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nContains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text\nis masked with a <mask> token. The ground truth for every mask is provided.\nEach row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk.\nfor each chunk the expected name for each mask is given.",
"### Supported Tasks and Leaderboards\n\nThe dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering,\ne.g. \"Who is <mask>?\"",
"### Languages\n\n*english only*",
"## Dataset Structure\n\nIn /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens.\n\nUse the dataset like this:",
"### Data Fields\n\nColumns are:\n- texts: the text chunks\n- masks: the names for each of the masks in the chunks",
"### Data Splits\n\nThere are no splits, only a default train.",
"## Dataset Creation\n\nCreated by using the tokenizer from allenai/longformer-base-4096 for the 4096 token per chunk version,\nand the xml-roberta-large tokenizer for the 512 token version. Chunks are split to fit those token sizes,\nwith the splits ensuring no words are split in half.\nPossible improvements: Last chunk of a page might be much shorter, could join part of the previous one to have more tokens\nin the last chunk.",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @skatinger for adding this dataset."
]
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"passage: TAGS\n#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-English #license-cc-by-4.0 #region-us \n# preprocessed version of rcds/wikipedia-persons-masked## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository: \n- Paper: \n- Leaderboard:\n- Point of Contact:### Dataset Summary\n\nContains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text\nis masked with a <mask> token. The ground truth for every mask is provided.\nEach row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk.\nfor each chunk the expected name for each mask is given.### Supported Tasks and Leaderboards\n\nThe dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering,\ne.g. \"Who is <mask>?\"### Languages\n\n*english only*## Dataset Structure\n\nIn /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens.\n\nUse the dataset like this:### Data Fields\n\nColumns are:\n- texts: the text chunks\n- masks: the names for each of the masks in the chunks### Data Splits\n\nThere are no splits, only a default train."
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|
58c2e4e55263a5b613358a611b1e309ca0f3775f | # Dataset Card for "wvDataset2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | basilis/wvDataset2 | [
"region:us"
]
| 2023-01-23T16:54:08+00:00 | {"dataset_info": {"features": [{"name": "tokenized_text", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 6675666248, "num_examples": 97928}], "download_size": 1690147799, "dataset_size": 6675666248}} | 2023-01-23T16:58:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wvDataset2"
More Information needed | [
"# Dataset Card for \"wvDataset2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wvDataset2\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wvDataset2\"\n\nMore Information needed"
]
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|
a71b5376f7f3e38502e726a9c3838e88c643d258 | # Dataset Card for "atis_intents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/atis_intents | [
"region:us"
]
| 2023-01-23T18:19:03+00:00 | {"dataset_info": {"features": [{"name": "label text", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 448812, "num_examples": 4834}, {"name": "test", "num_bytes": 69352, "num_examples": 800}], "download_size": 157677, "dataset_size": 518164}} | 2023-01-23T18:25:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "atis_intents"
More Information needed | [
"# Dataset Card for \"atis_intents\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"atis_intents\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"atis_intents\"\n\nMore Information needed"
]
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|
350704cfca814d0e4292a7d3db40370beccc5d99 | # Dataset Card for "academia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juancopi81/academia | [
"task_categories:automatic-speech-recognition",
"whisper",
"whispering",
"medium",
"region:us"
]
| 2023-01-23T18:19:25+00:00 | {"task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "CHANNEL_NAME", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TITLE", "dtype": "string"}, {"name": "DESCRIPTION", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "SEGMENTS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4010418, "num_examples": 52}], "download_size": 273124, "dataset_size": 4010418}, "tags": ["whisper", "whispering", "medium"]} | 2023-01-24T18:28:53+00:00 | []
| []
| TAGS
#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us
| # Dataset Card for "academia"
More Information needed | [
"# Dataset Card for \"academia\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us \n",
"# Dataset Card for \"academia\"\n\nMore Information needed"
]
| [
34,
13
]
| [
"passage: TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us \n# Dataset Card for \"academia\"\n\nMore Information needed"
]
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|
175d8e24a9c9f01308a81eeda30d43d6c1aae67e | # Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | purplebear/dreambooth-hackathon-images | [
"region:us"
]
| 2023-01-23T19:54:39+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 54613224.0, "num_examples": 20}], "download_size": 54616715, "dataset_size": 54613224.0}} | 2023-01-23T20:07:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dreambooth-hackathon-images"
More Information needed | [
"# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed"
]
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|
e6d14657870251c07882a1a2ee772277d85a75bc | # Dataset Card for "utd_reddit.json"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rami/utd_reddit.json | [
"region:us"
]
| 2023-01-23T20:05:54+00:00 | {"dataset_info": {"features": [{"name": "j52edo", "struct": [{"name": "title", "dtype": "string"}, {"name": "selftext", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "permalink", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "comments", "struct": [{"name": "g7p723l", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7pmgai", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7q0gtr", "struct": [{"name": "body", "dtype": "string"}]}]}]}, {"name": "g7p6z8q", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7q37rw", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": 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| []
| TAGS
#region-us
| # Dataset Card for "utd_reddit.json"
More Information needed | [
"# Dataset Card for \"utd_reddit.json\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
"# Dataset Card for \"utd_reddit.json\"\n\nMore Information needed"
]
| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"utd_reddit.json\"\n\nMore Information needed"
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|
48e8ffadc4c1d81013615481968376fade4bb046 | # Dataset Card for "dataset-identities-v-1.4-colorfulness"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | SDbiaseval/dataset-identities-v-1.4-colorfulness | [
"region:us"
]
| 2023-01-23T20:11:47+00:00 | {"dataset_info": {"features": [{"name": "ethnicity", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "image_path", "dtype": "string"}, {"name": "colorfulness", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 65148, "num_examples": 480}], "download_size": 12121, "dataset_size": 65148}} | 2023-01-23T20:12:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset-identities-v-1.4-colorfulness"
More Information needed | [
"# Dataset Card for \"dataset-identities-v-1.4-colorfulness\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dataset-identities-v-1.4-colorfulness\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dataset-identities-v-1.4-colorfulness\"\n\nMore Information needed"
]
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|
bce52c9046c39581ed3309b7dd18934bbfa7b70a |
# Dataset Card for DivEMT Attributions
*For more details on DivEMT, see our [EMNLP 2022 Paper](https://arxiv.org/abs/2205.12215) and our [Github repository](https://github.com/gsarti/divemt)*
## Dataset Description
- **DivEMT Source:** [DivEMT Github](https://github.com/gsarti/divemt)
- **Inseq Source:** [Inseq Github](https://github.com/inseq-team/inseq)
- **DivEMT Paper:** [DivEMT Arxiv](https://arxiv.org/abs/2205.12215)
- **Inseq Paper:** [Inseq Arxiv](https://arxiv.org/abs/2302.13942)
- **Point of Contact:** [Gabriele Sarti](mailto:[email protected]) | inseq/divemt_attributions | [
"task_categories:translation",
"annotations_creators:machine-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"language:it",
"language:ar",
"language:nl",
"language:tr",
"language:uk",
"language:vi",
"license:apache-2.0",
"arxiv:2205.12215",
"arxiv:2302.13942",
"region:us"
]
| 2023-01-23T21:26:39+00:00 | {"annotations_creators": ["machine-generated"], "language": ["it", "ar", "nl", "tr", "uk", "vi"], "license": "apache-2.0", "multilinguality": ["translation"], "size_categories": ["1K<n<10K"], "task_categories": ["translation"], "pretty_name": "divemt_attributions"} | 2023-03-16T16:02:19+00:00 | [
"2205.12215",
"2302.13942"
]
| [
"it",
"ar",
"nl",
"tr",
"uk",
"vi"
]
| TAGS
#task_categories-translation #annotations_creators-machine-generated #multilinguality-translation #size_categories-1K<n<10K #language-Italian #language-Arabic #language-Dutch #language-Turkish #language-Ukrainian #language-Vietnamese #license-apache-2.0 #arxiv-2205.12215 #arxiv-2302.13942 #region-us
|
# Dataset Card for DivEMT Attributions
*For more details on DivEMT, see our EMNLP 2022 Paper and our Github repository*
## Dataset Description
- DivEMT Source: DivEMT Github
- Inseq Source: Inseq Github
- DivEMT Paper: DivEMT Arxiv
- Inseq Paper: Inseq Arxiv
- Point of Contact: Gabriele Sarti | [
"# Dataset Card for DivEMT Attributions\n\n*For more details on DivEMT, see our EMNLP 2022 Paper and our Github repository*",
"## Dataset Description\n- DivEMT Source: DivEMT Github\n- Inseq Source: Inseq Github\n- DivEMT Paper: DivEMT Arxiv\n- Inseq Paper: Inseq Arxiv\n- Point of Contact: Gabriele Sarti"
]
| [
"TAGS\n#task_categories-translation #annotations_creators-machine-generated #multilinguality-translation #size_categories-1K<n<10K #language-Italian #language-Arabic #language-Dutch #language-Turkish #language-Ukrainian #language-Vietnamese #license-apache-2.0 #arxiv-2205.12215 #arxiv-2302.13942 #region-us \n",
"# Dataset Card for DivEMT Attributions\n\n*For more details on DivEMT, see our EMNLP 2022 Paper and our Github repository*",
"## Dataset Description\n- DivEMT Source: DivEMT Github\n- Inseq Source: Inseq Github\n- DivEMT Paper: DivEMT Arxiv\n- Inseq Paper: Inseq Arxiv\n- Point of Contact: Gabriele Sarti"
]
| [
108,
35,
61
]
| [
"passage: TAGS\n#task_categories-translation #annotations_creators-machine-generated #multilinguality-translation #size_categories-1K<n<10K #language-Italian #language-Arabic #language-Dutch #language-Turkish #language-Ukrainian #language-Vietnamese #license-apache-2.0 #arxiv-2205.12215 #arxiv-2302.13942 #region-us \n# Dataset Card for DivEMT Attributions\n\n*For more details on DivEMT, see our EMNLP 2022 Paper and our Github repository*## Dataset Description\n- DivEMT Source: DivEMT Github\n- Inseq Source: Inseq Github\n- DivEMT Paper: DivEMT Arxiv\n- Inseq Paper: Inseq Arxiv\n- Point of Contact: Gabriele Sarti"
]
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]
|
b60506d1caf875867bcaa17e6467ddf303e28ac7 |
## Description
This dataset contains images of Senko-san which were drawn by Rimukoro. All images are cropped up to 512x512 and every image contains txt file with tags list which were extracted from one of booru site.
## Examples
 | NeuroSenko/senko-arts-by-rimukoro-512x512 | [
"license:mit",
"Senko",
"region:us"
]
| 2023-01-23T22:34:02+00:00 | {"license": "mit", "tags": ["Senko"]} | 2023-07-17T02:40:35+00:00 | []
| []
| TAGS
#license-mit #Senko #region-us
|
## Description
This dataset contains images of Senko-san which were drawn by Rimukoro. All images are cropped up to 512x512 and every image contains txt file with tags list which were extracted from one of booru site.
## Examples
!example
!example | [
"## Description\nThis dataset contains images of Senko-san which were drawn by Rimukoro. All images are cropped up to 512x512 and every image contains txt file with tags list which were extracted from one of booru site.",
"## Examples\n!example\n!example"
]
| [
"TAGS\n#license-mit #Senko #region-us \n",
"## Description\nThis dataset contains images of Senko-san which were drawn by Rimukoro. All images are cropped up to 512x512 and every image contains txt file with tags list which were extracted from one of booru site.",
"## Examples\n!example\n!example"
]
| [
14,
55,
11
]
| [
"passage: TAGS\n#license-mit #Senko #region-us \n## Description\nThis dataset contains images of Senko-san which were drawn by Rimukoro. All images are cropped up to 512x512 and every image contains txt file with tags list which were extracted from one of booru site.## Examples\n!example\n!example"
]
| [
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]
|
eebce6f114bd4b500d8bdcd1b214ceaa05cbe8df |
This dataset was created using [this tool](https://github.com/p1atdev/pinterest-wizard).
# concept_art.json
589 images about "Concept Art" from **Pinterest searches** 🤗.
# double_exposure.json
597 images about "Double Exposure" from **Pinterest searches** 🤗.
# vaporwave.json
599 images about "Vaporwave" from **Pinterest searches** 🤗.
# typography.json
633 images about "Typography" from **Pinterest searches** 🤗.
# portrait.json
573 images about "Portrait" from **Pinterest searches** 🤗.
# selfie.json
584 images about "自撮り 女の子" from **Pinterest searches** 🤗.
# Type
```ts
interface Pinterest {
url: string // pinterest page url
alt: string // description of the image (not so accurate everytime)
src: string // image url
tags: string[] // related tags
}
``` | p1atdev/pinterest | [
"license:cc0-1.0",
"region:us"
]
| 2023-01-23T23:27:08+00:00 | {"license": "cc0-1.0"} | 2023-01-30T04:06:07+00:00 | []
| []
| TAGS
#license-cc0-1.0 #region-us
|
This dataset was created using this tool.
# concept_art.json
589 images about "Concept Art" from Pinterest searches .
# double_exposure.json
597 images about "Double Exposure" from Pinterest searches .
# URL
599 images about "Vaporwave" from Pinterest searches .
# URL
633 images about "Typography" from Pinterest searches .
# URL
573 images about "Portrait" from Pinterest searches .
# URL
584 images about "自撮り 女の子" from Pinterest searches .
# Type
| [
"# concept_art.json\n\n589 images about \"Concept Art\" from Pinterest searches .",
"# double_exposure.json\n\n597 images about \"Double Exposure\" from Pinterest searches .",
"# URL\n\n599 images about \"Vaporwave\" from Pinterest searches .",
"# URL\n\n633 images about \"Typography\" from Pinterest searches .",
"# URL\n\n573 images about \"Portrait\" from Pinterest searches .",
"# URL\n\n584 images about \"自撮り 女の子\" from Pinterest searches .",
"# Type"
]
| [
"TAGS\n#license-cc0-1.0 #region-us \n",
"# concept_art.json\n\n589 images about \"Concept Art\" from Pinterest searches .",
"# double_exposure.json\n\n597 images about \"Double Exposure\" from Pinterest searches .",
"# URL\n\n599 images about \"Vaporwave\" from Pinterest searches .",
"# URL\n\n633 images about \"Typography\" from Pinterest searches .",
"# URL\n\n573 images about \"Portrait\" from Pinterest searches .",
"# URL\n\n584 images about \"自撮り 女の子\" from Pinterest searches .",
"# Type"
]
| [
14,
22,
25,
17,
17,
16,
18,
2
]
| [
"passage: TAGS\n#license-cc0-1.0 #region-us \n# concept_art.json\n\n589 images about \"Concept Art\" from Pinterest searches .# double_exposure.json\n\n597 images about \"Double Exposure\" from Pinterest searches .# URL\n\n599 images about \"Vaporwave\" from Pinterest searches .# URL\n\n633 images about \"Typography\" from Pinterest searches .# URL\n\n573 images about \"Portrait\" from Pinterest searches .# URL\n\n584 images about \"自撮り 女の子\" from Pinterest searches .# Type"
]
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|
00ebdd95d3c9d2c126ab659612271811f4a2baca | # Charged up 2023 data set from Team 88 TJ2
This data set contains labeled images of the two game objects for the 2023 FRC game "Charged Up".
Labels and folder structure follow the yolov5 format.
Data set split:
|Type|Size|
|---|---|
|Train|9352|
|Validation|934|
|Test|103|
| woz4tetra/charged_up_2023 | [
"frc",
"2023",
"charged up",
"team 88",
"tj2",
"region:us"
]
| 2023-01-24T02:03:34+00:00 | {"pretty_name": "Charged Up 2023 Cones and Cubes", "tags": ["frc", "2023", "charged up", "team 88", "tj2"]} | 2023-02-26T19:19:05+00:00 | []
| []
| TAGS
#frc #2023 #charged up #team 88 #tj2 #region-us
| Charged up 2023 data set from Team 88 TJ2
=========================================
This data set contains labeled images of the two game objects for the 2023 FRC game "Charged Up".
Labels and folder structure follow the yolov5 format.
Data set split:
| []
| [
"TAGS\n#frc #2023 #charged up #team 88 #tj2 #region-us \n"
]
| [
23
]
| [
"passage: TAGS\n#frc #2023 #charged up #team 88 #tj2 #region-us \n"
]
| [
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|
78012249b8dec7fc370bbc406457f9092b6e6e54 | # Dataset Card for "LegoCityAdventures"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ACosmicFractal/LegoCityAdventures | [
"region:us"
]
| 2023-01-24T05:38:01+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86324590.0, "num_examples": 653}], "download_size": 84571697, "dataset_size": 86324590.0}} | 2023-02-07T09:17:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "LegoCityAdventures"
More Information needed | [
"# Dataset Card for \"LegoCityAdventures\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"LegoCityAdventures\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"LegoCityAdventures\"\n\nMore Information needed"
]
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|
f81dbf9f801073710e75b002ef7593cd2230038e | # Dataset Card for "my-pokeman-dataset1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | xfh/my-pokeman-dataset1 | [
"region:us"
]
| 2023-01-24T07:21:16+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 836857.0, "num_examples": 20}], "download_size": 805844, "dataset_size": 836857.0}} | 2023-01-24T07:22:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "my-pokeman-dataset1"
More Information needed | [
"# Dataset Card for \"my-pokeman-dataset1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"my-pokeman-dataset1\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"my-pokeman-dataset1\"\n\nMore Information needed"
]
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|
5f9ed9a1790bafe23dc3f84710602b4bb3a137ef |
```
@inproceedings{yanaka-etal-2019-neural,
title = "Can Neural Networks Understand Monotonicity Reasoning?",
author = "Yanaka, Hitomi and
Mineshima, Koji and
Bekki, Daisuke and
Inui, Kentaro and
Sekine, Satoshi and
Abzianidze, Lasha and
Bos, Johan",
booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
year = "2019",
pages = "31--40",
}
```
| tasksource/monotonicity-entailment | [
"license:apache-2.0",
"region:us"
]
| 2023-01-24T08:29:45+00:00 | {"license": "apache-2.0"} | 2023-01-24T08:35:27+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| []
| [
"TAGS\n#license-apache-2.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
]
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|
|
a450ba4b1b6d2216c3674d3e576b2e85ce729add |
# Dataset Card for NusaX-Senti
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment)
- **Paper:** [EACL 2022](https://arxiv.org/abs/2205.15960)
- **Point of Contact:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment)
### Dataset Summary
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.
### Supported Tasks and Leaderboards
- Sentiment analysis for Indonesian languages
### Languages
- ace: acehnese,
- ban: balinese,
- bjn: banjarese,
- bug: buginese,
- eng: english,
- ind: indonesian,
- jav: javanese,
- mad: madurese,
- min: minangkabau,
- nij: ngaju,
- sun: sundanese,
- bbc: toba_batak,
## Dataset Creation
### Curation Rationale
There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.
### Source Data
#### Initial Data Collection and Normalization
NusaX-senti is a dataset for sentiment analysis in Indonesian that has been expertly translated by native speakers.
#### Who are the source language producers?
The data was produced by humans (native speakers).
### Annotations
#### Annotation process
NusaX-senti is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.
#### Who are the annotators?
Native speakers of both Indonesian and the corresponding languages.
Annotators were compensated based on the number of translated samples.
### Personal and Sensitive Information
Personal information is removed.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
NusaX is created from review text. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Licensing Information
CC-BY-SA 4.0.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please contact authors for any information on the dataset.
### Citation Information
```
@misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
Jey Han and Sennrich, Rico and Ruder, Sebastian},
year={2022},
eprint={2205.15960},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
| indonlp/NusaX-senti | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
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"language:nij",
"language:su",
"language:bbc",
"license:cc-by-sa-4.0",
"arxiv:2205.15960",
"region:us"
]
| 2023-01-24T09:28:21+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["ace", "ban", "bjn", "bug", "en", "id", "jv", "mad", "min", "nij", "su", "bbc"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "NusaX-senti", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "neutral", "2": "positive"}}}}]}} | 2023-01-24T17:02:06+00:00 | [
"2205.15960"
]
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| TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-English #language-Indonesian #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-Batak Toba #license-cc-by-sa-4.0 #arxiv-2205.15960 #region-us
|
# Dataset Card for NusaX-Senti
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Repository: GitHub
- Paper: EACL 2022
- Point of Contact: GitHub
### Dataset Summary
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.
### Supported Tasks and Leaderboards
- Sentiment analysis for Indonesian languages
### Languages
- ace: acehnese,
- ban: balinese,
- bjn: banjarese,
- bug: buginese,
- eng: english,
- ind: indonesian,
- jav: javanese,
- mad: madurese,
- min: minangkabau,
- nij: ngaju,
- sun: sundanese,
- bbc: toba_batak,
## Dataset Creation
### Curation Rationale
There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.
### Source Data
#### Initial Data Collection and Normalization
NusaX-senti is a dataset for sentiment analysis in Indonesian that has been expertly translated by native speakers.
#### Who are the source language producers?
The data was produced by humans (native speakers).
### Annotations
#### Annotation process
NusaX-senti is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.
#### Who are the annotators?
Native speakers of both Indonesian and the corresponding languages.
Annotators were compensated based on the number of translated samples.
### Personal and Sensitive Information
Personal information is removed.
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
NusaX is created from review text. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Licensing Information
CC-BY-SA 4.0.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please contact authors for any information on the dataset.
### Contributions
Thanks to @afaji for adding this dataset.
| [
"# Dataset Card for NusaX-Senti",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Repository: GitHub\n- Paper: EACL 2022\n- Point of Contact: GitHub",
"### Dataset Summary\n\nNusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.\nNusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.",
"### Supported Tasks and Leaderboards\n\n- Sentiment analysis for Indonesian languages",
"### Languages\n\n- ace: acehnese,\n- ban: balinese,\n- bjn: banjarese,\n- bug: buginese,\n- eng: english,\n- ind: indonesian,\n- jav: javanese,\n- mad: madurese,\n- min: minangkabau,\n- nij: ngaju,\n- sun: sundanese,\n- bbc: toba_batak,",
"## Dataset Creation",
"### Curation Rationale\n\nThere is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nNusaX-senti is a dataset for sentiment analysis in Indonesian that has been expertly translated by native speakers.",
"#### Who are the source language producers?\n\nThe data was produced by humans (native speakers).",
"### Annotations",
"#### Annotation process\n\nNusaX-senti is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.",
"#### Who are the annotators?\n\nNative speakers of both Indonesian and the corresponding languages.\nAnnotators were compensated based on the number of translated samples.",
"### Personal and Sensitive Information\n\nPersonal information is removed.",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases\n\nNusaX is created from review text. These data sources may contain some bias.",
"### Other Known Limitations\n\nNo other known limitations",
"## Additional Information",
"### Licensing Information\n\nCC-BY-SA 4.0.\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\nPlease contact authors for any information on the dataset.",
"### Contributions\n\nThanks to @afaji for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-English #language-Indonesian #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-Batak Toba #license-cc-by-sa-4.0 #arxiv-2205.15960 #region-us \n",
"# Dataset Card for NusaX-Senti",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Repository: GitHub\n- Paper: EACL 2022\n- Point of Contact: GitHub",
"### Dataset Summary\n\nNusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.\nNusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.",
"### Supported Tasks and Leaderboards\n\n- Sentiment analysis for Indonesian languages",
"### Languages\n\n- ace: acehnese,\n- ban: balinese,\n- bjn: banjarese,\n- bug: buginese,\n- eng: english,\n- ind: indonesian,\n- jav: javanese,\n- mad: madurese,\n- min: minangkabau,\n- nij: ngaju,\n- sun: sundanese,\n- bbc: toba_batak,",
"## Dataset Creation",
"### Curation Rationale\n\nThere is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nNusaX-senti is a dataset for sentiment analysis in Indonesian that has been expertly translated by native speakers.",
"#### Who are the source language producers?\n\nThe data was produced by humans (native speakers).",
"### Annotations",
"#### Annotation process\n\nNusaX-senti is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.",
"#### Who are the annotators?\n\nNative speakers of both Indonesian and the corresponding languages.\nAnnotators were compensated based on the number of translated samples.",
"### Personal and Sensitive Information\n\nPersonal information is removed.",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases\n\nNusaX is created from review text. These data sources may contain some bias.",
"### Other Known Limitations\n\nNo other known limitations",
"## Additional Information",
"### Licensing Information\n\nCC-BY-SA 4.0.\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\nPlease contact authors for any information on the dataset.",
"### Contributions\n\nThanks to @afaji for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-English #language-Indonesian #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-Batak Toba #license-cc-by-sa-4.0 #arxiv-2205.15960 #region-us \n# Dataset Card for NusaX-Senti## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Repository: GitHub\n- Paper: EACL 2022\n- Point of Contact: GitHub### Dataset Summary\n\nNusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.\nNusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.### Supported Tasks and Leaderboards\n\n- Sentiment analysis for Indonesian languages",
"passage: ### Languages\n\n- ace: acehnese,\n- ban: balinese,\n- bjn: banjarese,\n- bug: buginese,\n- eng: english,\n- ind: indonesian,\n- jav: javanese,\n- mad: madurese,\n- min: minangkabau,\n- nij: ngaju,\n- sun: sundanese,\n- bbc: toba_batak,## Dataset Creation### Curation Rationale\n\nThere is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.### Source Data#### Initial Data Collection and Normalization\n\nNusaX-senti is a dataset for sentiment analysis in Indonesian that has been expertly translated by native speakers.#### Who are the source language producers?\n\nThe data was produced by humans (native speakers).### Annotations#### Annotation process\n\nNusaX-senti is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.#### Who are the annotators?\n\nNative speakers of both Indonesian and the corresponding languages.\nAnnotators were compensated based on the number of translated samples.### Personal and Sensitive Information\n\nPersonal information is removed.## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases\n\nNusaX is created from review text. These data sources may contain some bias.### Other Known Limitations\n\nNo other known limitations## Additional Information### Licensing Information\n\nCC-BY-SA 4.0.\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\nPlease contact authors for any information on the dataset."
]
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|
e3ee8eaae30a7e5d7fd343e82013bcdaf17116ce | # California Burned Areas Dataset
**Working on adding more data**
## Dataset Description
- **Paper:** [Pre-Print](https://arxiv.org/abs/2401.11519) and [Version of Record](https://ieeexplore.ieee.org/document/10261881)
### Dataset Summary
This dataset contains images from Sentinel-2 satellites taken before and after a wildfire.
The ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images.
### Supported Tasks
The dataset is designed to do binary semantic segmentation of burned vs unburned areas.
## Dataset Structure
We opted to use HDF5 to grant better portability and lower file size than GeoTIFF.
### Dataset opening
Using the dataset library, you download only the pre-patched raw version for simplicity.
```python
from dataset import load_dataset
# There are two available configurations, "post-fire" and "pre-post-fire."
dataset = load_dataset("DarthReca/california_burned_areas", name="post-fire")
```
The dataset was compressed using `h5py` and BZip2 from `hdf5plugin`. **WARNING: `hdf5plugin` is necessary to extract data**.
### Data Instances
Each matrix has a shape of 5490x5490xC, where C is 12 for pre-fire and post-fire images, while it is 0 for binary masks.
Pre-patched version is provided with matrices of size 512x512xC, too. In this case, only mask with at least one positive pixel is present.
You can find two versions of the dataset: _raw_ (without any transformation) and _normalized_ (with data normalized in the range 0-255).
Our suggestion is to use the _raw_ version to have the possibility to apply any wanted pre-processing step.
### Data Fields
In each standard HDF5 file, you can find post-fire, pre-fire images, and binary masks. The file is structured in this way:
```bash
├── foldn
│ ├── uid0
│ │ ├── pre_fire
│ │ ├── post_fire
│ │ ├── mask
│ ├── uid1
│ ├── post_fire
│ ├── mask
│
├── foldm
├── uid2
│ ├── post_fire
│ ├── mask
├── uid3
├── pre_fire
├── post_fire
├── mask
...
```
where `foldn` and `foldm` are fold names and `uidn` is a unique identifier for the wildfire.
For the pre-patched version, the structure is:
```bash
root
|
|-- uid0_x: {post_fire, pre_fire, mask}
|
|-- uid0_y: {post_fire, pre_fire, mask}
|
|-- uid1_x: {post_fire, mask}
|
...
```
the fold name is stored as an attribute.
### Data Splits
There are 5 random splits whose names are: 0, 1, 2, 3, and 4.
### Source Data
Data are collected directly from Copernicus Open Access Hub through the API. The band files are aggregated into one single matrix.
## Additional Information
### Licensing Information
This work is under OpenRAIL license.
### Citation Information
If you plan to use this dataset in your work please give the credit to Sentinel-2 mission and the California Department of Forestry and Fire Protection and cite using this BibTex:
```
@ARTICLE{cabuar,
author={Cambrin, Daniele Rege and Colomba, Luca and Garza, Paolo},
journal={IEEE Geoscience and Remote Sensing Magazine},
title={CaBuAr: California burned areas dataset for delineation [Software and Data Sets]},
year={2023},
volume={11},
number={3},
pages={106-113},
doi={10.1109/MGRS.2023.3292467}
}
``` | DarthReca/california_burned_areas | [
"task_categories:image-segmentation",
"size_categories:n<1K",
"license:openrail",
"climate",
"arxiv:2401.11519",
"doi:10.57967/hf/0389",
"region:us"
]
| 2023-01-24T10:31:47+00:00 | {"license": "openrail", "size_categories": ["n<1K"], "task_categories": ["image-segmentation"], "pretty_name": "California Burned Areas", "tags": ["climate"]} | 2024-01-23T09:41:05+00:00 | [
"2401.11519"
]
| []
| TAGS
#task_categories-image-segmentation #size_categories-n<1K #license-openrail #climate #arxiv-2401.11519 #doi-10.57967/hf/0389 #region-us
| # California Burned Areas Dataset
Working on adding more data
## Dataset Description
- Paper: Pre-Print and Version of Record
### Dataset Summary
This dataset contains images from Sentinel-2 satellites taken before and after a wildfire.
The ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images.
### Supported Tasks
The dataset is designed to do binary semantic segmentation of burned vs unburned areas.
## Dataset Structure
We opted to use HDF5 to grant better portability and lower file size than GeoTIFF.
### Dataset opening
Using the dataset library, you download only the pre-patched raw version for simplicity.
The dataset was compressed using 'h5py' and BZip2 from 'hdf5plugin'. WARNING: 'hdf5plugin' is necessary to extract data.
### Data Instances
Each matrix has a shape of 5490x5490xC, where C is 12 for pre-fire and post-fire images, while it is 0 for binary masks.
Pre-patched version is provided with matrices of size 512x512xC, too. In this case, only mask with at least one positive pixel is present.
You can find two versions of the dataset: _raw_ (without any transformation) and _normalized_ (with data normalized in the range 0-255).
Our suggestion is to use the _raw_ version to have the possibility to apply any wanted pre-processing step.
### Data Fields
In each standard HDF5 file, you can find post-fire, pre-fire images, and binary masks. The file is structured in this way:
where 'foldn' and 'foldm' are fold names and 'uidn' is a unique identifier for the wildfire.
For the pre-patched version, the structure is:
the fold name is stored as an attribute.
### Data Splits
There are 5 random splits whose names are: 0, 1, 2, 3, and 4.
### Source Data
Data are collected directly from Copernicus Open Access Hub through the API. The band files are aggregated into one single matrix.
## Additional Information
### Licensing Information
This work is under OpenRAIL license.
If you plan to use this dataset in your work please give the credit to Sentinel-2 mission and the California Department of Forestry and Fire Protection and cite using this BibTex:
| [
"# California Burned Areas Dataset\n\nWorking on adding more data",
"## Dataset Description\n\n- Paper: Pre-Print and Version of Record",
"### Dataset Summary\n\nThis dataset contains images from Sentinel-2 satellites taken before and after a wildfire. \nThe ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images.",
"### Supported Tasks\n\nThe dataset is designed to do binary semantic segmentation of burned vs unburned areas.",
"## Dataset Structure\n\nWe opted to use HDF5 to grant better portability and lower file size than GeoTIFF.",
"### Dataset opening\n\nUsing the dataset library, you download only the pre-patched raw version for simplicity.\n\n\nThe dataset was compressed using 'h5py' and BZip2 from 'hdf5plugin'. WARNING: 'hdf5plugin' is necessary to extract data.",
"### Data Instances\n\nEach matrix has a shape of 5490x5490xC, where C is 12 for pre-fire and post-fire images, while it is 0 for binary masks.\nPre-patched version is provided with matrices of size 512x512xC, too. In this case, only mask with at least one positive pixel is present.\n\nYou can find two versions of the dataset: _raw_ (without any transformation) and _normalized_ (with data normalized in the range 0-255). \nOur suggestion is to use the _raw_ version to have the possibility to apply any wanted pre-processing step.",
"### Data Fields\n\nIn each standard HDF5 file, you can find post-fire, pre-fire images, and binary masks. The file is structured in this way:\n\n\n\nwhere 'foldn' and 'foldm' are fold names and 'uidn' is a unique identifier for the wildfire.\n\nFor the pre-patched version, the structure is:\n\nthe fold name is stored as an attribute.",
"### Data Splits\n\nThere are 5 random splits whose names are: 0, 1, 2, 3, and 4.",
"### Source Data\n\nData are collected directly from Copernicus Open Access Hub through the API. The band files are aggregated into one single matrix.",
"## Additional Information",
"### Licensing Information\n\nThis work is under OpenRAIL license.\n\n\n\nIf you plan to use this dataset in your work please give the credit to Sentinel-2 mission and the California Department of Forestry and Fire Protection and cite using this BibTex:"
]
| [
"TAGS\n#task_categories-image-segmentation #size_categories-n<1K #license-openrail #climate #arxiv-2401.11519 #doi-10.57967/hf/0389 #region-us \n",
"# California Burned Areas Dataset\n\nWorking on adding more data",
"## Dataset Description\n\n- Paper: Pre-Print and Version of Record",
"### Dataset Summary\n\nThis dataset contains images from Sentinel-2 satellites taken before and after a wildfire. \nThe ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images.",
"### Supported Tasks\n\nThe dataset is designed to do binary semantic segmentation of burned vs unburned areas.",
"## Dataset Structure\n\nWe opted to use HDF5 to grant better portability and lower file size than GeoTIFF.",
"### Dataset opening\n\nUsing the dataset library, you download only the pre-patched raw version for simplicity.\n\n\nThe dataset was compressed using 'h5py' and BZip2 from 'hdf5plugin'. WARNING: 'hdf5plugin' is necessary to extract data.",
"### Data Instances\n\nEach matrix has a shape of 5490x5490xC, where C is 12 for pre-fire and post-fire images, while it is 0 for binary masks.\nPre-patched version is provided with matrices of size 512x512xC, too. In this case, only mask with at least one positive pixel is present.\n\nYou can find two versions of the dataset: _raw_ (without any transformation) and _normalized_ (with data normalized in the range 0-255). \nOur suggestion is to use the _raw_ version to have the possibility to apply any wanted pre-processing step.",
"### Data Fields\n\nIn each standard HDF5 file, you can find post-fire, pre-fire images, and binary masks. The file is structured in this way:\n\n\n\nwhere 'foldn' and 'foldm' are fold names and 'uidn' is a unique identifier for the wildfire.\n\nFor the pre-patched version, the structure is:\n\nthe fold name is stored as an attribute.",
"### Data Splits\n\nThere are 5 random splits whose names are: 0, 1, 2, 3, and 4.",
"### Source Data\n\nData are collected directly from Copernicus Open Access Hub through the API. The band files are aggregated into one single matrix.",
"## Additional Information",
"### Licensing Information\n\nThis work is under OpenRAIL license.\n\n\n\nIf you plan to use this dataset in your work please give the credit to Sentinel-2 mission and the California Department of Forestry and Fire Protection and cite using this BibTex:"
]
| [
59,
14,
15,
52,
28,
28,
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141,
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| [
"passage: TAGS\n#task_categories-image-segmentation #size_categories-n<1K #license-openrail #climate #arxiv-2401.11519 #doi-10.57967/hf/0389 #region-us \n# California Burned Areas Dataset\n\nWorking on adding more data## Dataset Description\n\n- Paper: Pre-Print and Version of Record### Dataset Summary\n\nThis dataset contains images from Sentinel-2 satellites taken before and after a wildfire. \nThe ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images.### Supported Tasks\n\nThe dataset is designed to do binary semantic segmentation of burned vs unburned areas.## Dataset Structure\n\nWe opted to use HDF5 to grant better portability and lower file size than GeoTIFF.### Dataset opening\n\nUsing the dataset library, you download only the pre-patched raw version for simplicity.\n\n\nThe dataset was compressed using 'h5py' and BZip2 from 'hdf5plugin'. WARNING: 'hdf5plugin' is necessary to extract data.### Data Instances\n\nEach matrix has a shape of 5490x5490xC, where C is 12 for pre-fire and post-fire images, while it is 0 for binary masks.\nPre-patched version is provided with matrices of size 512x512xC, too. In this case, only mask with at least one positive pixel is present.\n\nYou can find two versions of the dataset: _raw_ (without any transformation) and _normalized_ (with data normalized in the range 0-255). \nOur suggestion is to use the _raw_ version to have the possibility to apply any wanted pre-processing step.### Data Fields\n\nIn each standard HDF5 file, you can find post-fire, pre-fire images, and binary masks. The file is structured in this way:\n\n\n\nwhere 'foldn' and 'foldm' are fold names and 'uidn' is a unique identifier for the wildfire.\n\nFor the pre-patched version, the structure is:\n\nthe fold name is stored as an attribute."
]
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|
71e465b80cb7e61e7237236283ab210ffda6b706 | # Dataset Card for NB Tale, module 1 and 2 (< 15 sec. segments)
## Dataset Description
- **Homepage:**
- **Repository:** <https://github.com/scribe-project/nodalida_2023_combined_training>
- **Paper:**
```
@inproceedings{
solberg2023improving,
title={Improving Generalization of Norwegian {ASR} with Limited Linguistic Resources},
author={Per Erik Solberg and Pablo Ortiz and Phoebe Parsons and Torbj{\o}rn Svendsen and Giampiero Salvi},
booktitle={The 24rd Nordic Conference on Computational Linguistics},
year={2023}
}
```
- **Point of Contact:** [Per Erik Solberg](mailto:[email protected])
### Dataset Summary
This is the version of the Bokmål segments of module 1 and 2 of NB Tale used for testing the models
in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.
It only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers.
Speakers with `region` set to `foreign` were filtered out [when analyzing the data in the paper](https://github.com/scribe-project/nodalida_2023_combined_training/blob/main/analysis/analysis.ipynb).
### Languages
Norwegian Bokmål
## Dataset Creation
### Source Data
The full version of this dataset is found in [the repository of the Norwegian Language Bank](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-31/)
#### Initial Data Collection and Normalization
The data was retrieved using the [Spraakbanken downloader](https://pypi.org/project/spraakbanken-downloader/) and standardized
using the [combined dataset standardization scripts](https://github.com/scribe-project/asr-standardized-combined). Bokmål segments with a duration < 15 seconds were
extracted using [this code](https://github.com/scribe-project/nodalida_2023_combined_training/blob/main/make_datasets/make_nbtale_csvs.ipynb).
## Licensing Information
[CC0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{
solberg2023improving,
title={Improving Generalization of Norwegian {ASR} with Limited Linguistic Resources},
author={Per Erik Solberg and Pablo Ortiz and Phoebe Parsons and Torbj{\o}rn Svendsen and Giampiero Salvi},
booktitle={The 24rd Nordic Conference on Computational Linguistics},
year={2023}
}
``` | scribe-project/nbtale12 | [
"region:us"
]
| 2023-01-24T10:43:42+00:00 | {"dataset_info": {"features": [{"name": "speaker_id", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "utterance_id", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "raw_text", "dtype": "string"}, {"name": "full_audio_file", "dtype": "string"}, {"name": "original_data_split", "dtype": "string"}, {"name": "region", "dtype": "string"}, {"name": "duration", "dtype": "float64"}, {"name": "start", "dtype": "float64"}, {"name": "end", "dtype": "float64"}, {"name": "utterance_audio_file", "dtype": "audio"}, {"name": "standardized_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2078070414.74, "num_examples": 6630}], "download_size": 1624762124, "dataset_size": 2078070414.74}} | 2023-04-25T09:30:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for NB Tale, module 1 and 2 (< 15 sec. segments)
## Dataset Description
- Homepage:
- Repository: <URL
- Paper:
- Point of Contact: Per Erik Solberg
### Dataset Summary
This is the version of the Bokmål segments of module 1 and 2 of NB Tale used for testing the models
in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.
It only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers.
Speakers with 'region' set to 'foreign' were filtered out when analyzing the data in the paper.
### Languages
Norwegian Bokmål
## Dataset Creation
### Source Data
The full version of this dataset is found in the repository of the Norwegian Language Bank
#### Initial Data Collection and Normalization
The data was retrieved using the Spraakbanken downloader and standardized
using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were
extracted using this code.
## Licensing Information
CC0
| [
"# Dataset Card for NB Tale, module 1 and 2 (< 15 sec. segments)",
"## Dataset Description\n\n- Homepage: \n- Repository: <URL\n- Paper:\n\n\n- Point of Contact: Per Erik Solberg",
"### Dataset Summary\n\nThis is the version of the Bokmål segments of module 1 and 2 of NB Tale used for testing the models \n in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.\nIt only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers. \nSpeakers with 'region' set to 'foreign' were filtered out when analyzing the data in the paper.",
"### Languages\n\nNorwegian Bokmål",
"## Dataset Creation",
"### Source Data\nThe full version of this dataset is found in the repository of the Norwegian Language Bank",
"#### Initial Data Collection and Normalization\n\nThe data was retrieved using the Spraakbanken downloader and standardized\n using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were\n extracted using this code.",
"## Licensing Information\n\nCC0"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for NB Tale, module 1 and 2 (< 15 sec. segments)",
"## Dataset Description\n\n- Homepage: \n- Repository: <URL\n- Paper:\n\n\n- Point of Contact: Per Erik Solberg",
"### Dataset Summary\n\nThis is the version of the Bokmål segments of module 1 and 2 of NB Tale used for testing the models \n in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.\nIt only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers. \nSpeakers with 'region' set to 'foreign' were filtered out when analyzing the data in the paper.",
"### Languages\n\nNorwegian Bokmål",
"## Dataset Creation",
"### Source Data\nThe full version of this dataset is found in the repository of the Norwegian Language Bank",
"#### Initial Data Collection and Normalization\n\nThe data was retrieved using the Spraakbanken downloader and standardized\n using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were\n extracted using this code.",
"## Licensing Information\n\nCC0"
]
| [
6,
20,
26,
114,
7,
5,
23,
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]
| [
"passage: TAGS\n#region-us \n# Dataset Card for NB Tale, module 1 and 2 (< 15 sec. segments)## Dataset Description\n\n- Homepage: \n- Repository: <URL\n- Paper:\n\n\n- Point of Contact: Per Erik Solberg### Dataset Summary\n\nThis is the version of the Bokmål segments of module 1 and 2 of NB Tale used for testing the models \n in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.\nIt only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers. \nSpeakers with 'region' set to 'foreign' were filtered out when analyzing the data in the paper.### Languages\n\nNorwegian Bokmål## Dataset Creation### Source Data\nThe full version of this dataset is found in the repository of the Norwegian Language Bank#### Initial Data Collection and Normalization\n\nThe data was retrieved using the Spraakbanken downloader and standardized\n using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were\n extracted using this code.## Licensing Information\n\nCC0"
]
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|
723b15939b1b6f0253404732b6192ebfadb33b29 | # Dataset Card for NB Tale, module 3 (< 15 sec. segments)
## Dataset Description
- **Homepage:**
- **Repository:** <https://github.com/scribe-project/nodalida_2023_combined_training>
- **Paper:**
```
@inproceedings{
solberg2023improving,
title={Improving Generalization of Norwegian {ASR} with Limited Linguistic Resources},
author={Per Erik Solberg and Pablo Ortiz and Phoebe Parsons and Torbj{\o}rn Svendsen and Giampiero Salvi},
booktitle={The 24rd Nordic Conference on Computational Linguistics},
year={2023}
}
```
- **Point of Contact:** [Per Erik Solberg](mailto:[email protected])
### Dataset Summary
This is the version of the Bokmål segments of module 3 of NB Tale used for testing the models
in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.
It only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers.
Speakers with `region` set to `foreign` were filtered out [when analyzing the data in the paper](https://github.com/scribe-project/nodalida_2023_combined_training/blob/main/analysis/analysis.ipynb).
### Languages
Norwegian Bokmål
## Dataset Creation
### Source Data
The full version of this dataset is found in [the repository of the Norwegian Language Bank](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-31/)
#### Initial Data Collection and Normalization
The data was retrieved using the [Spraakbanken downloader](https://pypi.org/project/spraakbanken-downloader/) and standardized
using the [combined dataset standardization scripts](https://github.com/scribe-project/asr-standardized-combined). Bokmål segments with a duration < 15 seconds were
extracted using [this code](https://github.com/scribe-project/nodalida_2023_combined_training/blob/main/make_datasets/make_nbtale_csvs.ipynb).
## Licensing Information
[CC0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{
solberg2023improving,
title={Improving Generalization of Norwegian {ASR} with Limited Linguistic Resources},
author={Per Erik Solberg and Pablo Ortiz and Phoebe Parsons and Torbj{\o}rn Svendsen and Giampiero Salvi},
booktitle={The 24rd Nordic Conference on Computational Linguistics},
year={2023}
}
``` | scribe-project/nbtale3 | [
"region:us"
]
| 2023-01-24T10:44:07+00:00 | {"dataset_info": {"features": [{"name": "speaker_id", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "utterance_id", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "raw_text", "dtype": "string"}, {"name": "full_audio_file", "dtype": "string"}, {"name": "original_data_split", "dtype": "string"}, {"name": "region", "dtype": "string"}, {"name": "duration", "dtype": "float64"}, {"name": "start", "dtype": "float64"}, {"name": "end", "dtype": "float64"}, {"name": "utterance_audio_file", "dtype": "audio"}, {"name": "standardized_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1233495883.99, "num_examples": 8033}], "download_size": 1287266972, "dataset_size": 1233495883.99}} | 2023-04-25T09:32:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for NB Tale, module 3 (< 15 sec. segments)
## Dataset Description
- Homepage:
- Repository: <URL
- Paper:
- Point of Contact: Per Erik Solberg
### Dataset Summary
This is the version of the Bokmål segments of module 3 of NB Tale used for testing the models
in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.
It only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers.
Speakers with 'region' set to 'foreign' were filtered out when analyzing the data in the paper.
### Languages
Norwegian Bokmål
## Dataset Creation
### Source Data
The full version of this dataset is found in the repository of the Norwegian Language Bank
#### Initial Data Collection and Normalization
The data was retrieved using the Spraakbanken downloader and standardized
using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were
extracted using this code.
## Licensing Information
CC0
| [
"# Dataset Card for NB Tale, module 3 (< 15 sec. segments)",
"## Dataset Description\n\n- Homepage: \n- Repository: <URL\n- Paper:\n\n\n- Point of Contact: Per Erik Solberg",
"### Dataset Summary\n\nThis is the version of the Bokmål segments of module 3 of NB Tale used for testing the models \n in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.\nIt only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers. \nSpeakers with 'region' set to 'foreign' were filtered out when analyzing the data in the paper.",
"### Languages\n\nNorwegian Bokmål",
"## Dataset Creation",
"### Source Data\nThe full version of this dataset is found in the repository of the Norwegian Language Bank",
"#### Initial Data Collection and Normalization\n\nThe data was retrieved using the Spraakbanken downloader and standardized\n using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were\n extracted using this code.",
"## Licensing Information\n\nCC0"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for NB Tale, module 3 (< 15 sec. segments)",
"## Dataset Description\n\n- Homepage: \n- Repository: <URL\n- Paper:\n\n\n- Point of Contact: Per Erik Solberg",
"### Dataset Summary\n\nThis is the version of the Bokmål segments of module 3 of NB Tale used for testing the models \n in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.\nIt only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers. \nSpeakers with 'region' set to 'foreign' were filtered out when analyzing the data in the paper.",
"### Languages\n\nNorwegian Bokmål",
"## Dataset Creation",
"### Source Data\nThe full version of this dataset is found in the repository of the Norwegian Language Bank",
"#### Initial Data Collection and Normalization\n\nThe data was retrieved using the Spraakbanken downloader and standardized\n using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were\n extracted using this code.",
"## Licensing Information\n\nCC0"
]
| [
6,
18,
26,
112,
7,
5,
23,
56,
7
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for NB Tale, module 3 (< 15 sec. segments)## Dataset Description\n\n- Homepage: \n- Repository: <URL\n- Paper:\n\n\n- Point of Contact: Per Erik Solberg### Dataset Summary\n\nThis is the version of the Bokmål segments of module 3 of NB Tale used for testing the models \n in the paper *Improving Generalization of Norwegian ASR with Limited Linguistic Resources* presented at NoDaLiDa 2023.\nIt only contains segments of a length < 15 sec. This dataset contains both native and non-native speakers. \nSpeakers with 'region' set to 'foreign' were filtered out when analyzing the data in the paper.### Languages\n\nNorwegian Bokmål## Dataset Creation### Source Data\nThe full version of this dataset is found in the repository of the Norwegian Language Bank#### Initial Data Collection and Normalization\n\nThe data was retrieved using the Spraakbanken downloader and standardized\n using the combined dataset standardization scripts. Bokmål segments with a duration < 15 seconds were\n extracted using this code.## Licensing Information\n\nCC0"
]
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|
0c930e96cb7c548a40101f0462080bd27a5d38b5 | # Dataset Card for "AToMiC-Qrels-v0.2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TREC-AToMiC/AToMiC-Qrels-v0.2 | [
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T13:11:24+00:00 | {"license": "cc-by-sa-4.0", "dataset_info": {"features": [{"name": "text_id", "dtype": "string"}, {"name": "Q0", "dtype": "string"}, {"name": "image_id", "dtype": "string"}, {"name": "rel", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 789840, "num_examples": 9873}, {"name": "validation", "num_bytes": 1424080, "num_examples": 17801}, {"name": "train", "num_bytes": 352152240, "num_examples": 4401903}], "download_size": 205636566, "dataset_size": 354366160}} | 2023-02-14T21:31:18+00:00 | []
| []
| TAGS
#license-cc-by-sa-4.0 #region-us
| # Dataset Card for "AToMiC-Qrels-v0.2"
More Information needed | [
"# Dataset Card for \"AToMiC-Qrels-v0.2\"\n\nMore Information needed"
]
| [
"TAGS\n#license-cc-by-sa-4.0 #region-us \n",
"# Dataset Card for \"AToMiC-Qrels-v0.2\"\n\nMore Information needed"
]
| [
17,
21
]
| [
"passage: TAGS\n#license-cc-by-sa-4.0 #region-us \n# Dataset Card for \"AToMiC-Qrels-v0.2\"\n\nMore Information needed"
]
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|
0fb6736f06a9edabca384e5d8a08235701eab1c3 |
# Dataset Card for "relbert/nell"
## Dataset Description
- **Repository:** [https://github.com/xwhan/One-shot-Relational-Learning](https://github.com/xwhan/One-shot-Relational-Learning)
- **Paper:** [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/)
- **Dataset:** Never Ending Language Learner (NELL) dataset for one-shot link prediction.
### Dataset Summary
This is NELL-ONE dataset for the few-shots link prediction proposed in [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/).
Please see [NELL paper](https://www.cs.cmu.edu/~tom/pubs/NELL_aaai15.pdf) to know more about the original dataset.
- Number of instances
| | train | validation | test |
|:--------------------------------|--------:|-------------:|-------:|
| number of pairs | 5498 | 878 | 1352 |
| number of unique relation types | 32 | 4 | 6 |
- Number of pairs in each relation type
| | number of pairs (train) | number of pairs (validation) | number of pairs (test) |
|:---------------------------------------------------|--------------------------:|-------------------------------:|-------------------------:|
| concept:airportincity | 210 | 0 | 0 |
| concept:athleteledsportsteam | 424 | 0 | 0 |
| concept:automobilemakercardealersinstateorprovince | 78 | 0 | 0 |
| concept:bankboughtbank | 58 | 0 | 0 |
| concept:ceoof | 271 | 0 | 0 |
| concept:cityradiostation | 99 | 0 | 0 |
| concept:citytelevisionstation | 316 | 0 | 0 |
| concept:countriessuchascountries | 100 | 0 | 0 |
| concept:countrycapital | 211 | 0 | 0 |
| concept:countryhascitizen | 182 | 0 | 0 |
| concept:countryoforganizationheadquarters | 166 | 0 | 0 |
| concept:countrystates | 169 | 0 | 0 |
| concept:drugpossiblytreatsphysiologicalcondition | 91 | 0 | 0 |
| concept:fatherofperson | 108 | 0 | 0 |
| concept:fooddecreasestheriskofdisease | 1 | 0 | 0 |
| concept:hasofficeincountry | 283 | 0 | 0 |
| concept:leaguecoaches | 71 | 0 | 0 |
| concept:leaguestadiums | 279 | 0 | 0 |
| concept:musicartistmusician | 118 | 0 | 0 |
| concept:musicgenressuchasmusicgenres | 107 | 0 | 0 |
| concept:organizationnamehasacronym | 61 | 0 | 0 |
| concept:personalsoknownas | 78 | 0 | 0 |
| concept:personleadsgeopoliticalorganization | 120 | 0 | 0 |
| concept:personmovedtostateorprovince | 225 | 0 | 0 |
| concept:politicianrepresentslocation | 258 | 0 | 0 |
| concept:politicianusholdsoffice | 216 | 0 | 0 |
| concept:statehascapital | 151 | 0 | 0 |
| concept:stateorprovinceoforganizationheadquarters | 118 | 0 | 0 |
| concept:teamhomestadium | 138 | 0 | 0 |
| concept:teamplaysincity | 338 | 0 | 0 |
| concept:topmemberoforganization | 354 | 0 | 0 |
| concept:wifeof | 99 | 0 | 0 |
| concept:bankbankincountry | 0 | 229 | 0 |
| concept:cityalsoknownas | 0 | 356 | 0 |
| concept:parentofperson | 0 | 217 | 0 |
| concept:politicalgroupofpoliticianus | 0 | 76 | 0 |
| concept:automobilemakerdealersincity | 0 | 0 | 177 |
| concept:automobilemakerdealersincountry | 0 | 0 | 96 |
| concept:geopoliticallocationresidenceofpersion | 0 | 0 | 143 |
| concept:politicianusendorsespoliticianus | 0 | 0 | 386 |
| concept:producedby | 0 | 0 | 209 |
| concept:teamcoach | 0 | 0 | 341 |
- Number of entity types
| | head (train) | tail (train) | head (validation) | tail (validation) | head (test) | tail (test) |
|:-------------------------|---------------:|---------------:|--------------------:|--------------------:|--------------:|--------------:|
| actor | 6 | 2 | 0 | 0 | 0 | 0 |
| airport | 152 | 0 | 0 | 0 | 0 | 0 |
| astronaut | 4 | 0 | 0 | 1 | 0 | 1 |
| athlete | 353 | 21 | 1 | 2 | 0 | 59 |
| attraction | 4 | 1 | 0 | 0 | 0 | 0 |
| automobilemaker | 131 | 29 | 0 | 0 | 273 | 54 |
| bank | 109 | 126 | 144 | 0 | 0 | 0 |
| biotechcompany | 14 | 80 | 0 | 0 | 0 | 10 |
| building | 4 | 0 | 0 | 0 | 0 | 0 |
| celebrity | 6 | 5 | 0 | 0 | 4 | 2 |
| ceo | 423 | 0 | 0 | 0 | 0 | 0 |
| city | 342 | 852 | 316 | 316 | 42 | 161 |
| coach | 29 | 61 | 0 | 3 | 0 | 245 |
| comedian | 1 | 0 | 0 | 0 | 0 | 0 |
| company | 76 | 549 | 1 | 0 | 1 | 144 |
| country | 755 | 455 | 0 | 197 | 27 | 91 |
| county | 36 | 39 | 11 | 11 | 10 | 4 |
| creditunion | 1 | 0 | 0 | 0 | 0 | 0 |
| criminal | 3 | 0 | 1 | 0 | 0 | 1 |
| director | 2 | 0 | 0 | 0 | 0 | 1 |
| drug | 91 | 0 | 0 | 0 | 1 | 0 |
| female | 116 | 8 | 38 | 9 | 3 | 3 |
| geopoliticallocation | 184 | 112 | 96 | 29 | 24 | 8 |
| geopoliticalorganization | 28 | 68 | 8 | 21 | 1 | 7 |
| governmentorganization | 25 | 95 | 74 | 0 | 0 | 0 |
| island | 15 | 4 | 4 | 6 | 1 | 0 |
| journalist | 4 | 0 | 0 | 0 | 0 | 1 |
| male | 132 | 78 | 37 | 52 | 1 | 5 |
| model | 2 | 0 | 0 | 0 | 0 | 0 |
| monarch | 4 | 3 | 4 | 1 | 0 | 0 |
| museum | 1 | 5 | 0 | 0 | 0 | 0 |
| musicartist | 118 | 5 | 0 | 0 | 0 | 0 |
| musicgenre | 107 | 107 | 0 | 0 | 0 | 0 |
| musician | 5 | 124 | 0 | 0 | 0 | 0 |
| newspaper | 3 | 2 | 0 | 0 | 0 | 0 |
| organization | 23 | 86 | 1 | 1 | 32 | 2 |
| person | 350 | 256 | 116 | 131 | 0 | 96 |
| personafrica | 1 | 3 | 0 | 0 | 0 | 0 |
| personasia | 1 | 3 | 0 | 0 | 0 | 0 |
| personaustralia | 38 | 5 | 0 | 0 | 0 | 5 |
| personcanada | 19 | 14 | 0 | 0 | 0 | 0 |
| personeurope | 9 | 7 | 14 | 4 | 0 | 1 |
| personmexico | 57 | 14 | 0 | 0 | 0 | 20 |
| personnorthamerica | 9 | 6 | 0 | 0 | 0 | 3 |
| personsouthamerica | 1 | 1 | 0 | 17 | 0 | 0 |
| personus | 41 | 21 | 2 | 0 | 1 | 6 |
| planet | 1 | 0 | 0 | 0 | 0 | 1 |
| politician | 107 | 5 | 0 | 1 | 23 | 58 |
| politicianus | 408 | 12 | 3 | 71 | 352 | 360 |
| politicsblog | 2 | 3 | 0 | 0 | 0 | 0 |
| port | 7 | 0 | 0 | 0 | 0 | 0 |
| professor | 7 | 2 | 0 | 0 | 1 | 0 |
| publication | 1 | 21 | 0 | 0 | 0 | 0 |
| recordlabel | 1 | 13 | 0 | 0 | 0 | 0 |
| retailstore | 1 | 15 | 0 | 0 | 0 | 0 |
| school | 54 | 1 | 0 | 0 | 11 | 0 |
| scientist | 5 | 2 | 0 | 1 | 0 | 0 |
| sportsleague | 356 | 12 | 0 | 0 | 0 | 0 |
| sportsteam | 392 | 430 | 0 | 0 | 295 | 0 |
| stateorprovince | 254 | 602 | 0 | 0 | 38 | 0 |
| transportation | 36 | 2 | 0 | 0 | 0 | 0 |
| university | 3 | 15 | 0 | 0 | 0 | 0 |
| visualizablescene | 20 | 7 | 3 | 3 | 3 | 3 |
| visualizablething | 1 | 1 | 1 | 1 | 0 | 0 |
| website | 7 | 31 | 0 | 0 | 0 | 0 |
| caf_ | 0 | 1 | 0 | 0 | 0 | 0 |
| continent | 0 | 1 | 0 | 0 | 0 | 0 |
| disease | 0 | 92 | 0 | 0 | 0 | 0 |
| hotel | 0 | 1 | 0 | 0 | 0 | 0 |
| magazine | 0 | 5 | 0 | 0 | 0 | 0 |
| nongovorganization | 0 | 4 | 0 | 0 | 0 | 0 |
| nonprofitorganization | 0 | 2 | 0 | 0 | 0 | 0 |
| park | 0 | 1 | 0 | 0 | 0 | 0 |
| petroleumrefiningcompany | 0 | 6 | 0 | 0 | 0 | 0 |
| politicaloffice | 0 | 216 | 0 | 0 | 0 | 0 |
| politicalparty | 0 | 6 | 2 | 0 | 0 | 0 |
| radiostation | 0 | 93 | 0 | 0 | 0 | 0 |
| river | 0 | 4 | 0 | 0 | 0 | 0 |
| stadiumoreventvenue | 0 | 417 | 0 | 0 | 0 | 0 |
| televisionnetwork | 0 | 1 | 0 | 0 | 0 | 0 |
| televisionstation | 0 | 221 | 0 | 0 | 0 | 0 |
| trainstation | 0 | 2 | 0 | 0 | 0 | 0 |
| writer | 0 | 3 | 1 | 0 | 0 | 0 |
| zoo | 0 | 1 | 0 | 0 | 0 | 0 |
| automobilemodel | 0 | 0 | 0 | 0 | 100 | 0 |
| product | 0 | 0 | 0 | 0 | 62 | 0 |
| software | 0 | 0 | 0 | 0 | 42 | 0 |
| videogame | 0 | 0 | 0 | 0 | 4 | 0 |
## Dataset Structure
An example of `test` looks as below.
```shell
{
"relation": "concept:producedby",
"head": "Toyota Tacoma",
"head_type": "automobilemodel",
"tail": "Toyota",
"tail_type": "automobilemaker"
}
```
## Citation Information
```
@inproceedings{xiong-etal-2018-one,
title = "One-Shot Relational Learning for Knowledge Graphs",
author = "Xiong, Wenhan and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1223",
doi = "10.18653/v1/D18-1223",
pages = "1980--1990",
abstract = "Knowledge graphs (KG) are the key components of various natural language processing applications. To further expand KGs{'} coverage, previous studies on knowledge graph completion usually require a large number of positive examples for each relation. However, we observe long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.",
}
```
| relbert/nell | [
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:other",
"region:us"
]
| 2023-01-24T13:35:37+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "pretty_name": "relbert/nell"} | 2023-02-02T15:25:04+00:00 | []
| [
"en"
]
| TAGS
#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us
| Dataset Card for "relbert/nell"
===============================
Dataset Description
-------------------
* Repository: URL
* Paper: URL
* Dataset: Never Ending Language Learner (NELL) dataset for one-shot link prediction.
### Dataset Summary
This is NELL-ONE dataset for the few-shots link prediction proposed in URL
Please see NELL paper to know more about the original dataset.
* Number of instances
* Number of pairs in each relation type
* Number of entity types
Dataset Structure
-----------------
An example of 'test' looks as below.
| [
"### Dataset Summary\n\n\nThis is NELL-ONE dataset for the few-shots link prediction proposed in URL\nPlease see NELL paper to know more about the original dataset.\n\n\n* Number of instances\n\n\n\n* Number of pairs in each relation type\n\n\n\n* Number of entity types\n\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'test' looks as below."
]
| [
"TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n",
"### Dataset Summary\n\n\nThis is NELL-ONE dataset for the few-shots link prediction proposed in URL\nPlease see NELL paper to know more about the original dataset.\n\n\n* Number of instances\n\n\n\n* Number of pairs in each relation type\n\n\n\n* Number of entity types\n\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'test' looks as below."
]
| [
33,
78
]
| [
"passage: TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n### Dataset Summary\n\n\nThis is NELL-ONE dataset for the few-shots link prediction proposed in URL\nPlease see NELL paper to know more about the original dataset.\n\n\n* Number of instances\n\n\n\n* Number of pairs in each relation type\n\n\n\n* Number of entity types\n\n\n\nDataset Structure\n-----------------\n\n\nAn example of 'test' looks as below."
]
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|
f13fba1ee71663a99c369a02501663d773e751c8 | # Dataset Card for "yannic_ada_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juancopi81/yannic_ada_embeddings | [
"region:us"
]
| 2023-01-24T13:45:56+00:00 | {"dataset_info": {"features": [{"name": "TITLE", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "transcription_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "ada_embedding", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 127436085, "num_examples": 3194}], "download_size": 81996580, "dataset_size": 127436085}} | 2023-01-24T13:46:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "yannic_ada_embeddings"
More Information needed | [
"# Dataset Card for \"yannic_ada_embeddings\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"yannic_ada_embeddings\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"yannic_ada_embeddings\"\n\nMore Information needed"
]
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|
bdd59e10860746202dfdc452e0ac3a9eaa25268d | # The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
- Original Paper and Dataset [here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T)
- Kaggle dataset [here](https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000?resource=download)
# Introduction to datasets
Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc).
More than 50% of lesions are confirmed through histopathology (histo), the ground truth for the rest of the cases is either follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal).
The test set is not public, but the evaluation server remains running (see the challenge website). Any publications written using the HAM10000 data should be evaluated on the official test set hosted there, so that methods can be fairly compared.
- Test site can be accessed [here](https://challenge.isic-archive.com/landing/2018/)
# Disclaimer and additional information
This is a contribution to open sourced data in hugging face for image data. Images can be obtained from above links.
Train test split was done using a stratified splitting by cancer/diagnosis type. The code to stratify the dataset can be obtained on my github [here](https://github.com/marmal88/skin_cancer).
I do not own any rights to above images.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marmal88/skin_cancer | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"language:en",
"skin_cancer",
"HAM10000",
"region:us"
]
| 2023-01-24T13:53:28+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["image-classification", "image-segmentation"], "pretty_name": "HAM10000", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "string"}, {"name": "lesion_id", "dtype": "string"}, {"name": "dx", "dtype": "string"}, {"name": "dx_type", "dtype": "string"}, {"name": "age", "dtype": "float64"}, {"name": "sex", "dtype": "string"}, {"name": "localization", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2490501038.358, "num_examples": 9577}, {"name": "test", "num_bytes": 351507473.24, "num_examples": 1285}, {"name": "validation", "num_bytes": 681758880.144, "num_examples": 2492}], "download_size": 3693626934, "dataset_size": 3523767391.7419996}, "tags": ["skin_cancer", "HAM10000"]} | 2023-01-25T02:21:28+00:00 | []
| [
"en"
]
| TAGS
#task_categories-image-classification #task_categories-image-segmentation #size_categories-1K<n<10K #language-English #skin_cancer #HAM10000 #region-us
| # The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
- Original Paper and Dataset here
- Kaggle dataset here
# Introduction to datasets
Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc).
More than 50% of lesions are confirmed through histopathology (histo), the ground truth for the rest of the cases is either follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal).
The test set is not public, but the evaluation server remains running (see the challenge website). Any publications written using the HAM10000 data should be evaluated on the official test set hosted there, so that methods can be fairly compared.
- Test site can be accessed here
# Disclaimer and additional information
This is a contribution to open sourced data in hugging face for image data. Images can be obtained from above links.
Train test split was done using a stratified splitting by cancer/diagnosis type. The code to stratify the dataset can be obtained on my github here.
I do not own any rights to above images.
More Information needed | [
"# The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions\n\n- Original Paper and Dataset here\n- Kaggle dataset here",
"# Introduction to datasets\nTraining of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 (\"Human Against Machine with 10000 training images\") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc).\n\nMore than 50% of lesions are confirmed through histopathology (histo), the ground truth for the rest of the cases is either follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal). \n\nThe test set is not public, but the evaluation server remains running (see the challenge website). Any publications written using the HAM10000 data should be evaluated on the official test set hosted there, so that methods can be fairly compared.\n\n- Test site can be accessed here",
"# Disclaimer and additional information\nThis is a contribution to open sourced data in hugging face for image data. Images can be obtained from above links. \n\nTrain test split was done using a stratified splitting by cancer/diagnosis type. The code to stratify the dataset can be obtained on my github here.\n\nI do not own any rights to above images.\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-image-segmentation #size_categories-1K<n<10K #language-English #skin_cancer #HAM10000 #region-us \n",
"# The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions\n\n- Original Paper and Dataset here\n- Kaggle dataset here",
"# Introduction to datasets\nTraining of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 (\"Human Against Machine with 10000 training images\") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc).\n\nMore than 50% of lesions are confirmed through histopathology (histo), the ground truth for the rest of the cases is either follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal). \n\nThe test set is not public, but the evaluation server remains running (see the challenge website). Any publications written using the HAM10000 data should be evaluated on the official test set hosted there, so that methods can be fairly compared.\n\n- Test site can be accessed here",
"# Disclaimer and additional information\nThis is a contribution to open sourced data in hugging face for image data. Images can be obtained from above links. \n\nTrain test split was done using a stratified splitting by cancer/diagnosis type. The code to stratify the dataset can be obtained on my github here.\n\nI do not own any rights to above images.\n\nMore Information needed"
]
| [
53,
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"passage: TAGS\n#task_categories-image-classification #task_categories-image-segmentation #size_categories-1K<n<10K #language-English #skin_cancer #HAM10000 #region-us \n# The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions\n\n- Original Paper and Dataset here\n- Kaggle dataset here# Introduction to datasets\nTraining of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 (\"Human Against Machine with 10000 training images\") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc).\n\nMore than 50% of lesions are confirmed through histopathology (histo), the ground truth for the rest of the cases is either follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal). \n\nThe test set is not public, but the evaluation server remains running (see the challenge website). Any publications written using the HAM10000 data should be evaluated on the official test set hosted there, so that methods can be fairly compared.\n\n- Test site can be accessed here"
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|
36b558057afaa3643872d44ccf35a9a8c0e7d57f | SQUAD dataset with incorrect answers options generated with a T5-base fine-tuned on multiqad-200k dataset | b-yukky/squad-augmented-hq | [
"region:us"
]
| 2023-01-24T14:23:57+00:00 | {} | 2023-01-30T04:15:35+00:00 | []
| []
| TAGS
#region-us
| SQUAD dataset with incorrect answers options generated with a T5-base fine-tuned on multiqad-200k dataset | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
| [
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|
37a77001f48db65c0322b3bab6d78f27abd96856 |
# Dataset Card for DBLP-QuAD
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [DBLP-QuAD Homepage]()
- **Repository:** [DBLP-QuAD Repository](https://github.com/awalesushil/DBLP-QuAD)
- **Paper:** DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph
- **Point of Contact:** [Sushil Awale](mailto:[email protected])
### Dataset Summary
DBLP-QuAD is a scholarly knowledge graph question answering dataset with 10,000 question - SPARQL query pairs targeting the DBLP knowledge graph. The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.
## Dataset Structure
### Data Instances
An example of a question is given below:
```
{
"id": "Q0577",
"query_type": "MULTI_FACT",
"question": {
"string": "What are the primary affiliations of the authors of the paper 'Graphical Partitions and Graphical Relations'?"
},
"paraphrased_question": {
"string": "List the primary affiliations of the authors of 'Graphical Partitions and Graphical Relations'."
},
"query": {
"sparql": "SELECT DISTINCT ?answer WHERE { <https://dblp.org/rec/journals/fuin/ShaheenS19> <https://dblp.org/rdf/schema#authoredBy> ?x . ?x <https://dblp.org/rdf/schema#primaryAffiliation> ?answer }"
},
"template_id": "TP11",
"entities": [
"<https://dblp.org/rec/journals/fuin/ShaheenS19>"
],
"relations": [
"<https://dblp.org/rdf/schema#authoredBy>",
"<https://dblp.org/rdf/schema#primaryAffiliation>"
],
"temporal": false,
"held_out": true
}
```
### Data Fields
- `id`: the id of the question
- `question`: a string containing the question
- `paraphrased_question`: a paraphrased version of the question
- `query`: a SPARQL query that answers the question
- `query_type`: the type of the query
- `query_template`: the template of the query
- `entities`: a list of entities in the question
- `relations`: a list of relations in the question
- `temporal`: a boolean indicating whether the question contains a temporal expression
- `held_out`: a boolean indicating whether the question is held out from the training set
### Data Splits
The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.
## Additional Information
### Licensing Information
DBLP-QuAD is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
In review.
### Contributions
Thanks to [@awalesushil](https://github.com/awalesushil) for adding this dataset.
| awalesushil/DBLP-QuAD | [
"task_categories:question-answering",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"knowledge-base-qa",
"region:us"
]
| 2023-01-24T15:04:12+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": [], "pretty_name": "DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph", "tags": ["knowledge-base-qa"]} | 2023-02-15T17:32:06+00:00 | []
| [
"en"
]
| TAGS
#task_categories-question-answering #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #knowledge-base-qa #region-us
|
# Dataset Card for DBLP-QuAD
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage: [DBLP-QuAD Homepage]()
- Repository: DBLP-QuAD Repository
- Paper: DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph
- Point of Contact: Sushil Awale
### Dataset Summary
DBLP-QuAD is a scholarly knowledge graph question answering dataset with 10,000 question - SPARQL query pairs targeting the DBLP knowledge graph. The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.
## Dataset Structure
### Data Instances
An example of a question is given below:
### Data Fields
- 'id': the id of the question
- 'question': a string containing the question
- 'paraphrased_question': a paraphrased version of the question
- 'query': a SPARQL query that answers the question
- 'query_type': the type of the query
- 'query_template': the template of the query
- 'entities': a list of entities in the question
- 'relations': a list of relations in the question
- 'temporal': a boolean indicating whether the question contains a temporal expression
- 'held_out': a boolean indicating whether the question is held out from the training set
### Data Splits
The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.
## Additional Information
### Licensing Information
DBLP-QuAD is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
In review.
### Contributions
Thanks to @awalesushil for adding this dataset.
| [
"# Dataset Card for DBLP-QuAD",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: [DBLP-QuAD Homepage]()\n- Repository: DBLP-QuAD Repository\n- Paper: DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph\n- Point of Contact: Sushil Awale",
"### Dataset Summary\n\nDBLP-QuAD is a scholarly knowledge graph question answering dataset with 10,000 question - SPARQL query pairs targeting the DBLP knowledge graph. The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.",
"## Dataset Structure",
"### Data Instances\n\nAn example of a question is given below:",
"### Data Fields\n\n- 'id': the id of the question\n- 'question': a string containing the question\n- 'paraphrased_question': a paraphrased version of the question\n- 'query': a SPARQL query that answers the question\n- 'query_type': the type of the query\n- 'query_template': the template of the query\n- 'entities': a list of entities in the question\n- 'relations': a list of relations in the question\n- 'temporal': a boolean indicating whether the question contains a temporal expression\n- 'held_out': a boolean indicating whether the question is held out from the training set",
"### Data Splits\n\nThe dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.",
"## Additional Information",
"### Licensing Information\n\nDBLP-QuAD is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).\n\n\n\nIn review.",
"### Contributions\n\nThanks to @awalesushil for adding this dataset."
]
| [
"TAGS\n#task_categories-question-answering #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #knowledge-base-qa #region-us \n",
"# Dataset Card for DBLP-QuAD",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: [DBLP-QuAD Homepage]()\n- Repository: DBLP-QuAD Repository\n- Paper: DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph\n- Point of Contact: Sushil Awale",
"### Dataset Summary\n\nDBLP-QuAD is a scholarly knowledge graph question answering dataset with 10,000 question - SPARQL query pairs targeting the DBLP knowledge graph. The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.",
"## Dataset Structure",
"### Data Instances\n\nAn example of a question is given below:",
"### Data Fields\n\n- 'id': the id of the question\n- 'question': a string containing the question\n- 'paraphrased_question': a paraphrased version of the question\n- 'query': a SPARQL query that answers the question\n- 'query_type': the type of the query\n- 'query_template': the template of the query\n- 'entities': a list of entities in the question\n- 'relations': a list of relations in the question\n- 'temporal': a boolean indicating whether the question contains a temporal expression\n- 'held_out': a boolean indicating whether the question is held out from the training set",
"### Data Splits\n\nThe dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.",
"## Additional Information",
"### Licensing Information\n\nDBLP-QuAD is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).\n\n\n\nIn review.",
"### Contributions\n\nThanks to @awalesushil for adding this dataset."
]
| [
90,
10,
120,
64,
62,
6,
15,
162,
23,
5,
30,
18
]
| [
"passage: TAGS\n#task_categories-question-answering #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #knowledge-base-qa #region-us \n# Dataset Card for DBLP-QuAD## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: [DBLP-QuAD Homepage]()\n- Repository: DBLP-QuAD Repository\n- Paper: DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph\n- Point of Contact: Sushil Awale### Dataset Summary\n\nDBLP-QuAD is a scholarly knowledge graph question answering dataset with 10,000 question - SPARQL query pairs targeting the DBLP knowledge graph. The dataset is split into 7,000 training, 1,000 validation and 2,000 test questions.## Dataset Structure### Data Instances\n\nAn example of a question is given below:"
]
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]
|
b3662175d3b2482d711f18559b7acc2a5bccc600 |
## Loading the dataset with a specific configuration
There are 3 different OCR versions to choose from with their original format or standardized DUE format, as well as the option to load the documents as filepaths or as binaries (PDF).
To load a specific configuration, pass a config from one of the following:
```python
#{bin_}{Amazon,Azure,Tesseract}_{original,due}
['Amazon_due', 'Amazon_original', 'Azure_due', 'Azure_original', 'Tesseract_due', 'Tesseract_original',
'bin_Amazon_due', 'bin_Amazon_original', 'bin_Azure_due', 'bin_Azure_original', 'bin_Tesseract_due', 'bin_Tesseract_original']
```
Loading the dataset:
```python
from datasets import load_dataset
ds = load_dataset("jordyvl/DUDE_loader", 'Amazon_original')
```
This dataset repository contains helper functions to convert the dataset to ImDB (image database) format.
We advise to clone the repository and run it according to your preferences (OCR version, lowercasing, ...).
When running the above data loading script, you should be able to find the extracted binaries under the [HF_CACHE](https://huggingface.co/docs/datasets/cache):
`HF_CACHE/datasets/downloads/extracted/<hash>/DUDE_train-val-test_binaries`, which can be reused for the `data_dir` argument.
For example:
```bash
python3 DUDE_imdb_loader.py \
--data_dir ~/.cache/huggingface/datasets/downloads/extracted/7adde0ed7b0150b7f6b32e52bcad452991fde0f3407c8a87e74b1cb475edaa5b/DUDE_train-val-test_binaries/
```
For baselines, we recommend having a look at the [MP-DocVQA repository](https://github.com/rubenpt91/MP-DocVQA-Framework)
We strongly encourage you to benchmark your best models and submit test set predictions on the [DUDE competition leaderboard](https://rrc.cvc.uab.es/?ch=23)
To help with test set predictions, we have included a sample submission file `RRC_DUDE_testset_submission_example.json`.
| jordyvl/DUDE_loader | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-24T15:20:01+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "DUDE"} | 2023-10-03T09:54:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us
|
## Loading the dataset with a specific configuration
There are 3 different OCR versions to choose from with their original format or standardized DUE format, as well as the option to load the documents as filepaths or as binaries (PDF).
To load a specific configuration, pass a config from one of the following:
Loading the dataset:
This dataset repository contains helper functions to convert the dataset to ImDB (image database) format.
We advise to clone the repository and run it according to your preferences (OCR version, lowercasing, ...).
When running the above data loading script, you should be able to find the extracted binaries under the HF_CACHE:
'HF_CACHE/datasets/downloads/extracted/<hash>/DUDE_train-val-test_binaries', which can be reused for the 'data_dir' argument.
For example:
For baselines, we recommend having a look at the MP-DocVQA repository
We strongly encourage you to benchmark your best models and submit test set predictions on the DUDE competition leaderboard
To help with test set predictions, we have included a sample submission file 'RRC_DUDE_testset_submission_example.json'.
| [
"## Loading the dataset with a specific configuration\n\nThere are 3 different OCR versions to choose from with their original format or standardized DUE format, as well as the option to load the documents as filepaths or as binaries (PDF).\nTo load a specific configuration, pass a config from one of the following:\n\n\n\nLoading the dataset:\n\n\nThis dataset repository contains helper functions to convert the dataset to ImDB (image database) format. \nWe advise to clone the repository and run it according to your preferences (OCR version, lowercasing, ...).\nWhen running the above data loading script, you should be able to find the extracted binaries under the HF_CACHE: \n'HF_CACHE/datasets/downloads/extracted/<hash>/DUDE_train-val-test_binaries', which can be reused for the 'data_dir' argument.\n\nFor example: \n\n\n\nFor baselines, we recommend having a look at the MP-DocVQA repository\n\nWe strongly encourage you to benchmark your best models and submit test set predictions on the DUDE competition leaderboard\nTo help with test set predictions, we have included a sample submission file 'RRC_DUDE_testset_submission_example.json'."
]
| [
"TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n",
"## Loading the dataset with a specific configuration\n\nThere are 3 different OCR versions to choose from with their original format or standardized DUE format, as well as the option to load the documents as filepaths or as binaries (PDF).\nTo load a specific configuration, pass a config from one of the following:\n\n\n\nLoading the dataset:\n\n\nThis dataset repository contains helper functions to convert the dataset to ImDB (image database) format. \nWe advise to clone the repository and run it according to your preferences (OCR version, lowercasing, ...).\nWhen running the above data loading script, you should be able to find the extracted binaries under the HF_CACHE: \n'HF_CACHE/datasets/downloads/extracted/<hash>/DUDE_train-val-test_binaries', which can be reused for the 'data_dir' argument.\n\nFor example: \n\n\n\nFor baselines, we recommend having a look at the MP-DocVQA repository\n\nWe strongly encourage you to benchmark your best models and submit test set predictions on the DUDE competition leaderboard\nTo help with test set predictions, we have included a sample submission file 'RRC_DUDE_testset_submission_example.json'."
]
| [
43,
285
]
| [
"passage: TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n## Loading the dataset with a specific configuration\n\nThere are 3 different OCR versions to choose from with their original format or standardized DUE format, as well as the option to load the documents as filepaths or as binaries (PDF).\nTo load a specific configuration, pass a config from one of the following:\n\n\n\nLoading the dataset:\n\n\nThis dataset repository contains helper functions to convert the dataset to ImDB (image database) format. \nWe advise to clone the repository and run it according to your preferences (OCR version, lowercasing, ...).\nWhen running the above data loading script, you should be able to find the extracted binaries under the HF_CACHE: \n'HF_CACHE/datasets/downloads/extracted/<hash>/DUDE_train-val-test_binaries', which can be reused for the 'data_dir' argument.\n\nFor example: \n\n\n\nFor baselines, we recommend having a look at the MP-DocVQA repository\n\nWe strongly encourage you to benchmark your best models and submit test set predictions on the DUDE competition leaderboard\nTo help with test set predictions, we have included a sample submission file 'RRC_DUDE_testset_submission_example.json'."
]
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|
2a2b792e3d85d053a9b9d1123411fc85c491ed47 | # Dataset Card for "PickaPic-random-prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/PickaPic-random-prompts | [
"region:us"
]
| 2023-01-24T15:27:44+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9702, "num_examples": 200}], "download_size": 8488, "dataset_size": 9702}} | 2023-01-24T15:27:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "PickaPic-random-prompts"
More Information needed | [
"# Dataset Card for \"PickaPic-random-prompts\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"PickaPic-random-prompts\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"PickaPic-random-prompts\"\n\nMore Information needed"
]
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|
457eb6f6d134d340b93eeed4374b224928a870e8 | # Dataset Card for "runwayml-stable-diffusion-v1-5-eval-random-prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/runwayml-stable-diffusion-v1-5-eval-random-prompts | [
"region:us"
]
| 2023-01-24T15:37:11+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32392, "num_examples": 200}], "download_size": 11291, "dataset_size": 32392}} | 2023-01-24T15:37:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "runwayml-stable-diffusion-v1-5-eval-random-prompts"
More Information needed | [
"# Dataset Card for \"runwayml-stable-diffusion-v1-5-eval-random-prompts\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"runwayml-stable-diffusion-v1-5-eval-random-prompts\"\n\nMore Information needed"
]
| [
6,
33
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"runwayml-stable-diffusion-v1-5-eval-random-prompts\"\n\nMore Information needed"
]
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|
f017f3fb62da2088f7dd13669c7dbf3b81e77272 | # Dataset Card for "julioprofe"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juancopi81/julioprofe | [
"task_categories:automatic-speech-recognition",
"whisper",
"whispering",
"medium",
"region:us"
]
| 2023-01-24T15:38:53+00:00 | {"task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "CHANNEL_NAME", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TITLE", "dtype": "string"}, {"name": "DESCRIPTION", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "SEGMENTS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17940671, "num_examples": 1285}], "download_size": 6527309, "dataset_size": 17940671}, "tags": ["whisper", "whispering", "medium"]} | 2023-02-11T23:31:32+00:00 | []
| []
| TAGS
#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us
| # Dataset Card for "julioprofe"
More Information needed | [
"# Dataset Card for \"julioprofe\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us \n",
"# Dataset Card for \"julioprofe\"\n\nMore Information needed"
]
| [
34,
13
]
| [
"passage: TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #medium #region-us \n# Dataset Card for \"julioprofe\"\n\nMore Information needed"
]
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|
59db562a3f9702050bc3ea050528607a38fac89d |
# LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization
A collaboration between [reciTAL](https://recital.ai/en/), [MLIA](https://mlia.lip6.fr/) (ISIR, Sorbonne Université), [Meta AI](https://ai.facebook.com/), and [Università di Trento](https://www.unitn.it/)
## PubMed-Lay dataset for summarization
PubMed-Lay is an enhanced version of the PubMed summarization dataset, for which layout information is provided.
### Data Fields
- `article_id`: article id
- `article_words`: sequence of words constituting the body of the article
- `article_bboxes`: sequence of corresponding word bounding boxes
- `norm_article_bboxes`: sequence of corresponding normalized word bounding boxes
- `abstract`: a string containing the abstract of the article
- `article_pdf_url`: URL of the article's PDF
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_.
| Dataset Split | Number of Instances |
| ------------- | --------------------|
| Train | 78,234 |
| Validation | 4,084 |
| Test | 4,350 |
## Citation
``` latex
@article{nguyen2023loralay,
title={LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization},
author={Nguyen, Laura and Scialom, Thomas and Piwowarski, Benjamin and Staiano, Jacopo},
journal={arXiv preprint arXiv:2301.11312},
year={2023}
}
```
| nglaura/pubmedlay-summarization | [
"task_categories:summarization",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-01-24T15:46:55+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["summarization"], "pretty_name": "PubMed-Lay"} | 2023-04-11T09:10:19+00:00 | []
| [
"en"
]
| TAGS
#task_categories-summarization #language-English #license-apache-2.0 #region-us
| LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization
============================================================================================
A collaboration between reciTAL, MLIA (ISIR, Sorbonne Université), Meta AI, and Università di Trento
PubMed-Lay dataset for summarization
------------------------------------
PubMed-Lay is an enhanced version of the PubMed summarization dataset, for which layout information is provided.
### Data Fields
* 'article\_id': article id
* 'article\_words': sequence of words constituting the body of the article
* 'article\_bboxes': sequence of corresponding word bounding boxes
* 'norm\_article\_bboxes': sequence of corresponding normalized word bounding boxes
* 'abstract': a string containing the abstract of the article
* 'article\_pdf\_url': URL of the article's PDF
### Data Splits
This dataset has 3 splits: *train*, *validation*, and *test*.
| [
"### Data Fields\n\n\n* 'article\\_id': article id\n* 'article\\_words': sequence of words constituting the body of the article\n* 'article\\_bboxes': sequence of corresponding word bounding boxes\n* 'norm\\_article\\_bboxes': sequence of corresponding normalized word bounding boxes\n* 'abstract': a string containing the abstract of the article\n* 'article\\_pdf\\_url': URL of the article's PDF",
"### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*."
]
| [
"TAGS\n#task_categories-summarization #language-English #license-apache-2.0 #region-us \n",
"### Data Fields\n\n\n* 'article\\_id': article id\n* 'article\\_words': sequence of words constituting the body of the article\n* 'article\\_bboxes': sequence of corresponding word bounding boxes\n* 'norm\\_article\\_bboxes': sequence of corresponding normalized word bounding boxes\n* 'abstract': a string containing the abstract of the article\n* 'article\\_pdf\\_url': URL of the article's PDF",
"### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*."
]
| [
28,
117,
29
]
| [
"passage: TAGS\n#task_categories-summarization #language-English #license-apache-2.0 #region-us \n### Data Fields\n\n\n* 'article\\_id': article id\n* 'article\\_words': sequence of words constituting the body of the article\n* 'article\\_bboxes': sequence of corresponding word bounding boxes\n* 'norm\\_article\\_bboxes': sequence of corresponding normalized word bounding boxes\n* 'abstract': a string containing the abstract of the article\n* 'article\\_pdf\\_url': URL of the article's PDF### Data Splits\n\n\nThis dataset has 3 splits: *train*, *validation*, and *test*."
]
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|
43ff36807a2d8d082de5198418a235cd9d7871a6 | # Dataset Card for "utd_reddit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rami/utd_reddit | [
"region:us"
]
| 2023-01-24T16:31:10+00:00 | {"dataset_info": {"features": [{"name": "j52edo", "struct": [{"name": "title", "dtype": "string"}, {"name": "selftext", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "permalink", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "comments", "struct": [{"name": "g7p723l", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7pmgai", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7q0gtr", "struct": [{"name": "body", "dtype": "string"}]}]}]}, {"name": "g7p6z8q", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7q37rw", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": 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| []
| TAGS
#region-us
| # Dataset Card for "utd_reddit"
More Information needed | [
"# Dataset Card for \"utd_reddit\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"utd_reddit\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"utd_reddit\"\n\nMore Information needed"
]
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|
05230cc51e502fad9ffc2349fbe777d84ff30569 |
# Dataset Card for Dataset Name
## Dataset Description
- **Repository: [Self-Instruct](https://github.com/yizhongw/self-instruct)**
- **Paper: [Self-Instruct: Aligning Language Model with Self Generated Instructions](https://arxiv.org/abs/2212.10560)**
### Dataset Summary
This dataset is a copy of yizhongw's data from the github above, note this was created on 24th Jan 2023.
## Dataset Structure
GPT3-finetuning format (prompt + completion)
### Data Fields
Prompt
"Task: [Instruction] Output:"
Completion
"[Answer]<|endoftext|>"
### Data Splits
No splits
## Dataset Creation
### Curation Rationale
Effeciently create a large dataset by using GPT3 to generate the data
### Annotations
The dataset was made and annotated by GPT3
### Dataset Curators
yizhongw
### Licensing Information
Apache 2.0
### Citation Information
I am not the creator of this dataset, please see the GitHub link above.
| eastwind/self-instruct-base | [
"license:apache-2.0",
"arxiv:2212.10560",
"region:us"
]
| 2023-01-24T16:58:51+00:00 | {"license": "apache-2.0"} | 2023-01-24T17:44:49+00:00 | [
"2212.10560"
]
| []
| TAGS
#license-apache-2.0 #arxiv-2212.10560 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Repository: Self-Instruct
- Paper: Self-Instruct: Aligning Language Model with Self Generated Instructions
### Dataset Summary
This dataset is a copy of yizhongw's data from the github above, note this was created on 24th Jan 2023.
## Dataset Structure
GPT3-finetuning format (prompt + completion)
### Data Fields
Prompt
"Task: [Instruction] Output:"
Completion
"[Answer]<|endoftext|>"
### Data Splits
No splits
## Dataset Creation
### Curation Rationale
Effeciently create a large dataset by using GPT3 to generate the data
### Annotations
The dataset was made and annotated by GPT3
### Dataset Curators
yizhongw
### Licensing Information
Apache 2.0
I am not the creator of this dataset, please see the GitHub link above.
| [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Repository: Self-Instruct \n- Paper: Self-Instruct: Aligning Language Model with Self Generated Instructions",
"### Dataset Summary\n\nThis dataset is a copy of yizhongw's data from the github above, note this was created on 24th Jan 2023.",
"## Dataset Structure\nGPT3-finetuning format (prompt + completion)",
"### Data Fields\n\nPrompt\n\n\"Task: [Instruction] Output:\"\n\nCompletion\n\n\"[Answer]<|endoftext|>\"",
"### Data Splits\n\nNo splits",
"## Dataset Creation",
"### Curation Rationale\n\nEffeciently create a large dataset by using GPT3 to generate the data",
"### Annotations\n\nThe dataset was made and annotated by GPT3",
"### Dataset Curators\n\nyizhongw",
"### Licensing Information\n\nApache 2.0\n\n\n\nI am not the creator of this dataset, please see the GitHub link above."
]
| [
"TAGS\n#license-apache-2.0 #arxiv-2212.10560 #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Repository: Self-Instruct \n- Paper: Self-Instruct: Aligning Language Model with Self Generated Instructions",
"### Dataset Summary\n\nThis dataset is a copy of yizhongw's data from the github above, note this was created on 24th Jan 2023.",
"## Dataset Structure\nGPT3-finetuning format (prompt + completion)",
"### Data Fields\n\nPrompt\n\n\"Task: [Instruction] Output:\"\n\nCompletion\n\n\"[Answer]<|endoftext|>\"",
"### Data Splits\n\nNo splits",
"## Dataset Creation",
"### Curation Rationale\n\nEffeciently create a large dataset by using GPT3 to generate the data",
"### Annotations\n\nThe dataset was made and annotated by GPT3",
"### Dataset Curators\n\nyizhongw",
"### Licensing Information\n\nApache 2.0\n\n\n\nI am not the creator of this dataset, please see the GitHub link above."
]
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"passage: TAGS\n#license-apache-2.0 #arxiv-2212.10560 #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Repository: Self-Instruct \n- Paper: Self-Instruct: Aligning Language Model with Self Generated Instructions### Dataset Summary\n\nThis dataset is a copy of yizhongw's data from the github above, note this was created on 24th Jan 2023.## Dataset Structure\nGPT3-finetuning format (prompt + completion)### Data Fields\n\nPrompt\n\n\"Task: [Instruction] Output:\"\n\nCompletion\n\n\"[Answer]<|endoftext|>\"### Data Splits\n\nNo splits## Dataset Creation### Curation Rationale\n\nEffeciently create a large dataset by using GPT3 to generate the data### Annotations\n\nThe dataset was made and annotated by GPT3### Dataset Curators\n\nyizhongw### Licensing Information\n\nApache 2.0\n\n\n\nI am not the creator of this dataset, please see the GitHub link above."
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|
1d5773ce7f66782a89a6439a81261c84c298e2a3 |
# Dataset Card for NusaX-MT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/mt)
- **Paper:** [EACL 2022](https://arxiv.org/abs/2205.15960)
- **Point of Contact:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/mt)
### Dataset Summary
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.
### Supported Tasks and Leaderboards
- Machine translation for Indonesian languages
### Languages
All possible pairs of the following:
- ace: acehnese,
- ban: balinese,
- bjn: banjarese,
- bug: buginese,
- eng: english,
- ind: indonesian,
- jav: javanese,
- mad: madurese,
- min: minangkabau,
- nij: ngaju,
- sun: sundanese,
- bbc: toba_batak,
## Dataset Creation
### Curation Rationale
There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.
### Source Data
#### Initial Data Collection and Normalization
NusaX-MT is a dataset for machine translation in Indonesian langauges that has been expertly translated by native speakers.
#### Who are the source language producers?
The data was produced by humans (native speakers).
### Annotations
#### Annotation process
NusaX-MT is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.
#### Who are the annotators?
Native speakers of both Indonesian and the corresponding languages.
Annotators were compensated based on the number of translated samples.
### Personal and Sensitive Information
Personal information is removed.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
NusaX is created from review text. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Licensing Information
CC-BY-SA 4.0.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please contact authors for any information on the dataset.
### Citation Information
```
@misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
Jey Han and Sennrich, Rico and Ruder, Sebastian},
year={2022},
eprint={2205.15960},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
| indonlp/NusaX-MT | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ace",
"language:ban",
"language:bjn",
"language:bug",
"language:en",
"language:id",
"language:jv",
"language:mad",
"language:min",
"language:nij",
"language:su",
"language:bbc",
"license:cc-by-sa-4.0",
"arxiv:2205.15960",
"region:us"
]
| 2023-01-24T17:05:31+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["ace", "ban", "bjn", "bug", "en", "id", "jv", "mad", "min", "nij", "su", "bbc"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["translation"], "pretty_name": "NusaX-MT", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text_1", "dtype": "string"}, {"name": "text_2", "dtype": "string"}, {"name": "text_1_lang", "dtype": "string"}, {"name": "text_2_lang", "dtype": "string"}]}} | 2023-01-24T17:21:03+00:00 | [
"2205.15960"
]
| [
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]
| TAGS
#task_categories-translation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-English #language-Indonesian #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-Batak Toba #license-cc-by-sa-4.0 #arxiv-2205.15960 #region-us
|
# Dataset Card for NusaX-MT
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Repository: GitHub
- Paper: EACL 2022
- Point of Contact: GitHub
### Dataset Summary
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.
### Supported Tasks and Leaderboards
- Machine translation for Indonesian languages
### Languages
All possible pairs of the following:
- ace: acehnese,
- ban: balinese,
- bjn: banjarese,
- bug: buginese,
- eng: english,
- ind: indonesian,
- jav: javanese,
- mad: madurese,
- min: minangkabau,
- nij: ngaju,
- sun: sundanese,
- bbc: toba_batak,
## Dataset Creation
### Curation Rationale
There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.
### Source Data
#### Initial Data Collection and Normalization
NusaX-MT is a dataset for machine translation in Indonesian langauges that has been expertly translated by native speakers.
#### Who are the source language producers?
The data was produced by humans (native speakers).
### Annotations
#### Annotation process
NusaX-MT is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.
#### Who are the annotators?
Native speakers of both Indonesian and the corresponding languages.
Annotators were compensated based on the number of translated samples.
### Personal and Sensitive Information
Personal information is removed.
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
NusaX is created from review text. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Licensing Information
CC-BY-SA 4.0.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please contact authors for any information on the dataset.
### Contributions
Thanks to @afaji for adding this dataset.
| [
"# Dataset Card for NusaX-MT",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Repository: GitHub\n- Paper: EACL 2022\n- Point of Contact: GitHub",
"### Dataset Summary\n\nNusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.\nNusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.",
"### Supported Tasks and Leaderboards\n\n- Machine translation for Indonesian languages",
"### Languages\n\nAll possible pairs of the following:\n\n- ace: acehnese,\n- ban: balinese,\n- bjn: banjarese,\n- bug: buginese,\n- eng: english,\n- ind: indonesian,\n- jav: javanese,\n- mad: madurese,\n- min: minangkabau,\n- nij: ngaju,\n- sun: sundanese,\n- bbc: toba_batak,",
"## Dataset Creation",
"### Curation Rationale\n\nThere is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nNusaX-MT is a dataset for machine translation in Indonesian langauges that has been expertly translated by native speakers.",
"#### Who are the source language producers?\n\nThe data was produced by humans (native speakers).",
"### Annotations",
"#### Annotation process\n\nNusaX-MT is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.",
"#### Who are the annotators?\n\nNative speakers of both Indonesian and the corresponding languages.\nAnnotators were compensated based on the number of translated samples.",
"### Personal and Sensitive Information\n\nPersonal information is removed.",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases\n\nNusaX is created from review text. These data sources may contain some bias.",
"### Other Known Limitations\n\nNo other known limitations",
"## Additional Information",
"### Licensing Information\n\nCC-BY-SA 4.0.\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\nPlease contact authors for any information on the dataset.",
"### Contributions\n\nThanks to @afaji for adding this dataset."
]
| [
"TAGS\n#task_categories-translation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-English #language-Indonesian #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-Batak Toba #license-cc-by-sa-4.0 #arxiv-2205.15960 #region-us \n",
"# Dataset Card for NusaX-MT",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Repository: GitHub\n- Paper: EACL 2022\n- Point of Contact: GitHub",
"### Dataset Summary\n\nNusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.\nNusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.",
"### Supported Tasks and Leaderboards\n\n- Machine translation for Indonesian languages",
"### Languages\n\nAll possible pairs of the following:\n\n- ace: acehnese,\n- ban: balinese,\n- bjn: banjarese,\n- bug: buginese,\n- eng: english,\n- ind: indonesian,\n- jav: javanese,\n- mad: madurese,\n- min: minangkabau,\n- nij: ngaju,\n- sun: sundanese,\n- bbc: toba_batak,",
"## Dataset Creation",
"### Curation Rationale\n\nThere is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nNusaX-MT is a dataset for machine translation in Indonesian langauges that has been expertly translated by native speakers.",
"#### Who are the source language producers?\n\nThe data was produced by humans (native speakers).",
"### Annotations",
"#### Annotation process\n\nNusaX-MT is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.",
"#### Who are the annotators?\n\nNative speakers of both Indonesian and the corresponding languages.\nAnnotators were compensated based on the number of translated samples.",
"### Personal and Sensitive Information\n\nPersonal information is removed.",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases\n\nNusaX is created from review text. These data sources may contain some bias.",
"### Other Known Limitations\n\nNo other known limitations",
"## Additional Information",
"### Licensing Information\n\nCC-BY-SA 4.0.\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\nPlease contact authors for any information on the dataset.",
"### Contributions\n\nThanks to @afaji for adding this dataset."
]
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"passage: TAGS\n#task_categories-translation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-English #language-Indonesian #language-Javanese #language-Madurese #language-Minangkabau #language-Ngaju #language-Sundanese #language-Batak Toba #license-cc-by-sa-4.0 #arxiv-2205.15960 #region-us \n# Dataset Card for NusaX-MT## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Repository: GitHub\n- Paper: EACL 2022\n- Point of Contact: GitHub### Dataset Summary\n\nNusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.\nNusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.### Supported Tasks and Leaderboards\n\n- Machine translation for Indonesian languages",
"passage: ### Languages\n\nAll possible pairs of the following:\n\n- ace: acehnese,\n- ban: balinese,\n- bjn: banjarese,\n- bug: buginese,\n- eng: english,\n- ind: indonesian,\n- jav: javanese,\n- mad: madurese,\n- min: minangkabau,\n- nij: ngaju,\n- sun: sundanese,\n- bbc: toba_batak,## Dataset Creation### Curation Rationale\n\nThere is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.### Source Data#### Initial Data Collection and Normalization\n\nNusaX-MT is a dataset for machine translation in Indonesian langauges that has been expertly translated by native speakers.#### Who are the source language producers?\n\nThe data was produced by humans (native speakers).### Annotations#### Annotation process\n\nNusaX-MT is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.#### Who are the annotators?\n\nNative speakers of both Indonesian and the corresponding languages.\nAnnotators were compensated based on the number of translated samples.### Personal and Sensitive Information\n\nPersonal information is removed.## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases\n\nNusaX is created from review text. These data sources may contain some bias.### Other Known Limitations\n\nNo other known limitations## Additional Information### Licensing Information\n\nCC-BY-SA 4.0.\n\nAttribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\nShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.\n\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\nPlease contact authors for any information on the dataset."
]
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|
0226c3b54722d272ae56448c4a3f9387d463a7ec | # Dataset Card for "stabilityai-stable-diffusion-2-1-eval-random-prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/stabilityai-stable-diffusion-2-1-eval-random-prompts | [
"region:us"
]
| 2023-01-24T17:06:03+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32792, "num_examples": 200}], "download_size": 11301, "dataset_size": 32792}} | 2023-01-24T17:06:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "stabilityai-stable-diffusion-2-1-eval-random-prompts"
More Information needed | [
"# Dataset Card for \"stabilityai-stable-diffusion-2-1-eval-random-prompts\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"stabilityai-stable-diffusion-2-1-eval-random-prompts\"\n\nMore Information needed"
]
| [
6,
32
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"stabilityai-stable-diffusion-2-1-eval-random-prompts\"\n\nMore Information needed"
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|
7867be796849bb70003075b3932083102dd1c4e0 | # Dataset Card for "pile-tokenized-10b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | NeelNanda/pile-tokenized-10b | [
"region:us"
]
| 2023-01-24T17:14:07+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "uint16"}], "splits": [{"name": "train", "num_bytes": 22153340700, "num_examples": 10795975}], "download_size": 19746448291, "dataset_size": 22153340700}} | 2023-01-24T20:52:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pile-tokenized-10b"
More Information needed | [
"# Dataset Card for \"pile-tokenized-10b\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pile-tokenized-10b\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pile-tokenized-10b\"\n\nMore Information needed"
]
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|
eda848e5bd7df7a2e50e46886e20c9df8f70ab8e |
# Chinese Metaphor Corpus (CMC)
## Dataset Description
- **Homepage:** https://github.com/liyucheng09/Metaphor_Generator
- **Repository:** https://github.com/liyucheng09/Metaphor_Generator
- **Paper:** CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
The first Chinese metaphor corpus serving both metaphor identification and generation. We construct a big metaphor resoruce in Chinese with around 9000 metaphorical sentences with tenor and vehicle annotated. Check out more details in the [github repo](https://github.com/liyucheng09/Metaphor_Generator) and our [paper](https://aclanthology.org/2022.coling-1.563/) presenting at COLING 2022.
首个中文比喻数据集，可以用于中文比喻识别与中文比喻生成。在[知乎](https://zhuanlan.zhihu.com/p/572740322)查看更多细节。
### Languages
Chinese
### Citation Information
```
@inproceedings{li-etal-2022-cm,
title = "{CM}-Gen: A Neural Framework for {C}hinese Metaphor Generation with Explicit Context Modelling",
author = "Li, Yucheng and
Lin, Chenghua and
Guerin, Frank",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.563",
pages = "6468--6479",
}
``` | liyucheng/chinese_metaphor_dataset | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-nc-sa-4.0",
"metaphor",
"figurative language",
"region:us"
]
| 2023-01-24T17:16:26+00:00 | {"language": ["zh"], "license": "cc-by-nc-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "pretty_name": "CMC", "tags": ["metaphor", "figurative language"]} | 2023-07-06T19:29:33+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text-generation #size_categories-1K<n<10K #language-Chinese #license-cc-by-nc-sa-4.0 #metaphor #figurative language #region-us
|
# Chinese Metaphor Corpus (CMC)
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling
- Leaderboard:
- Point of Contact: liyucheng09@URL
### Dataset Summary
The first Chinese metaphor corpus serving both metaphor identification and generation. We construct a big metaphor resoruce in Chinese with around 9000 metaphorical sentences with tenor and vehicle annotated. Check out more details in the github repo and our paper presenting at COLING 2022.
首个中文比喻数据集，可以用于中文比喻识别与中文比喻生成。在知乎查看更多细节。
### Languages
Chinese
| [
"# Chinese Metaphor Corpus (CMC)",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling\n- Leaderboard: \n- Point of Contact: liyucheng09@URL",
"### Dataset Summary\n\nThe first Chinese metaphor corpus serving both metaphor identification and generation. We construct a big metaphor resoruce in Chinese with around 9000 metaphorical sentences with tenor and vehicle annotated. Check out more details in the github repo and our paper presenting at COLING 2022.\n\n首个中文比喻数据集，可以用于中文比喻识别与中文比喻生成。在知乎查看更多细节。",
"### Languages\n\nChinese"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-Chinese #license-cc-by-nc-sa-4.0 #metaphor #figurative language #region-us \n",
"# Chinese Metaphor Corpus (CMC)",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling\n- Leaderboard: \n- Point of Contact: liyucheng09@URL",
"### Dataset Summary\n\nThe first Chinese metaphor corpus serving both metaphor identification and generation. We construct a big metaphor resoruce in Chinese with around 9000 metaphorical sentences with tenor and vehicle annotated. Check out more details in the github repo and our paper presenting at COLING 2022.\n\n首个中文比喻数据集，可以用于中文比喻识别与中文比喻生成。在知乎查看更多细节。",
"### Languages\n\nChinese"
]
| [
54,
9,
53,
96,
5
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-Chinese #license-cc-by-nc-sa-4.0 #metaphor #figurative language #region-us \n# Chinese Metaphor Corpus (CMC)## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling\n- Leaderboard: \n- Point of Contact: liyucheng09@URL### Dataset Summary\n\nThe first Chinese metaphor corpus serving both metaphor identification and generation. We construct a big metaphor resoruce in Chinese with around 9000 metaphorical sentences with tenor and vehicle annotated. Check out more details in the github repo and our paper presenting at COLING 2022.\n\n首个中文比喻数据集，可以用于中文比喻识别与中文比喻生成。在知乎查看更多细节。### Languages\n\nChinese"
]
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|
3adfe5ce52bb8d547d2f0b54a687810d9422880e | # Dataset Card for "es_hate_speech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pysentimiento/es_hate_speech | [
"region:us"
]
| 2023-01-24T17:53:38+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "HS", "dtype": {"class_label": {"names": {"0": "OK", "1": "HATEFUL"}}}}, {"name": "TR", "dtype": {"class_label": {"names": {"0": "GROUP", "1": "INDIVIDUAL"}}}}, {"name": "AG", "dtype": {"class_label": {"names": {"0": "NOT AGGRESSIVE", "1": "AGGRESSIVE"}}}}], "splits": [{"name": "dev", "num_bytes": 84567, "num_examples": 500}, {"name": "test", "num_bytes": 279000, "num_examples": 1600}, {"name": "train", "num_bytes": 752199, "num_examples": 4500}], "download_size": 698251, "dataset_size": 1115766}} | 2023-01-24T17:53:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "es_hate_speech"
More Information needed | [
"# Dataset Card for \"es_hate_speech\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"es_hate_speech\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"es_hate_speech\"\n\nMore Information needed"
]
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|
2fd28ca55e93f20a16394e6cc4c7761d6d758164 | # Dataset Card for "es_sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pysentimiento/es_sentiment | [
"region:us"
]
| 2023-01-24T17:54:20+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "neu", "2": "pos"}}}}], "splits": [{"name": "dev", "num_bytes": 273826, "num_examples": 2443}, {"name": "test", "num_bytes": 747378, "num_examples": 7264}, {"name": "train", "num_bytes": 520221, "num_examples": 4802}], "download_size": 1009181, "dataset_size": 1541425}} | 2023-01-24T17:54:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "es_sentiment"
More Information needed | [
"# Dataset Card for \"es_sentiment\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"es_sentiment\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"es_sentiment\"\n\nMore Information needed"
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|
e821c725fd2fd66186f4b19b6e3628091cbb921b | # Dataset Card for "academia2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juancopi81/academia2 | [
"task_categories:automatic-speech-recognition",
"whisper",
"whispering",
"base",
"region:us"
]
| 2023-01-24T18:34:54+00:00 | {"task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "CHANNEL_NAME", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TITLE", "dtype": "string"}, {"name": "DESCRIPTION", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "SEGMENTS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13790539, "num_examples": 388}], "download_size": 4608890, "dataset_size": 13790539}, "tags": ["whisper", "whispering", "base"]} | 2023-01-25T16:11:01+00:00 | []
| []
| TAGS
#task_categories-automatic-speech-recognition #whisper #whispering #base #region-us
| # Dataset Card for "academia2"
More Information needed | [
"# Dataset Card for \"academia2\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #base #region-us \n",
"# Dataset Card for \"academia2\"\n\nMore Information needed"
]
| [
33,
14
]
| [
"passage: TAGS\n#task_categories-automatic-speech-recognition #whisper #whispering #base #region-us \n# Dataset Card for \"academia2\"\n\nMore Information needed"
]
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|
fc9bcda0640617e6baa6946071f424beedc4f922 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/nfcorpus-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T18:57:21+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:18:39+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
0fb93dd23a7339b6dcd27e241cb9b5eca62d4d18 |
Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
<h4>Citation information:</h4>
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
| GBaker/MedQA-USMLE-4-options | [
"language:en",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-24T19:08:56+00:00 | {"language": ["en"], "license": "cc-by-4.0"} | 2023-01-24T19:18:09+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-cc-by-4.0 #region-us
|
Original dataset introduced by Jin et al. in What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
<h4>Citation information:</h4>
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
| []
| [
"TAGS\n#language-English #license-cc-by-4.0 #region-us \n"
]
| [
19
]
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|
b13bd7ebd4a08ec3385b0d6e3df6da8b94449949 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/fiqa-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:21:16+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:21:20+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
ead875e08787c559c82fc4f9b09810f5492140e8 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/scifact-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:21:39+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:21:40+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
e7cbffa3aa5cbe6613fc7d81d0d823cf87d69b19 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/trec-news-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:22:47+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:23:15+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
d3fd37c14bb6cf5886688e2c871ee6600bf8c353 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/robust04-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:23:26+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:23:50+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
]
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0.11008518934249878,
0.07585329562425613,
0.12839171290397644,
0.07823500037193298,
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0.031300466507673264,
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|
cb4c02768580fc18b4f6968a9dde08d31a39183f | # Dataset Card for "PickaPic-ft-ranked"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/PickaPic-ft-ranked | [
"region:us"
]
| 2023-01-24T19:24:08+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1546519335.752, "num_examples": 3748}, {"name": "validation", "num_bytes": 78674902.0, "num_examples": 200}], "download_size": 1546455134, "dataset_size": 1625194237.752}} | 2023-01-26T06:49:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "PickaPic-ft-ranked"
More Information needed | [
"# Dataset Card for \"PickaPic-ft-ranked\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"PickaPic-ft-ranked\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"PickaPic-ft-ranked\"\n\nMore Information needed"
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|
6adc54411f0c00683ecfad33eb21b912cd50933d |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/scidocs-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:26:50+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:26:52+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
]
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0.008965534158051014,
0.08374572545289993,
-0.028826579451560974,
-0.16463200747966766,
0.05426986888051033,
-0.025396011769771576,
0.08680921047925949,
-0.2157660573720932,
-0.038948338478803635,
0.03492550924420357,
0.0033514630049467087,
-0.026338720694184303,
0.1159391924738884,
0.04105430841445923,
0.009594596922397614,
-0.0366186797618866,
-0.14745503664016724,
0.028892679139971733,
0.06139521673321724,
-0.04870668053627014,
-0.02577054500579834
]
|
f46d2212247b77aed6d05ecfcecadfaedc75f8bb |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/arguana-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:26:57+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:26:59+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
d69c13f113c079cd2777c9260e8d054ac4041977 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/trec-covid-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:27:07+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:27:17+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
a0ee7b8ac5d3d7397d21d458d8aec81f49d79df8 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/quora-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:27:55+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:28:14+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
cbe7a3a74261624564c24615aba58b10ec7f1a1b |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/webis-touche2020-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:29:28+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:29:44+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
aac925e88256b223e413a1088489adfd0915c7a3 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/hotpotqa-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:29:51+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:31:59+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
]
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0.11008518934249878,
0.07585329562425613,
0.12839171290397644,
0.07823500037193298,
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0.031300466507673264,
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|
7f2b2d3356af939381bf3798f5be97c3bbaa56bc | # Dataset Card for "oxford_pets_with_l14_emb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Isamu136/oxford_pets_with_l14_emb | [
"region:us"
]
| 2023-01-24T19:30:19+00:00 | {"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "dog", "dtype": "bool"}, {"name": "image", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 257189204.25, "num_examples": 7390}], "download_size": 261518905, "dataset_size": 257189204.25}} | 2023-01-24T19:30:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "oxford_pets_with_l14_emb"
More Information needed | [
"# Dataset Card for \"oxford_pets_with_l14_emb\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"oxford_pets_with_l14_emb\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"oxford_pets_with_l14_emb\"\n\nMore Information needed"
]
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|
5cf16c02f47d75a50a93b9b641fefd7845e96cc2 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/dbpedia-entity-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:32:09+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:34:05+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
]
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0.008965534158051014,
0.08374572545289993,
-0.028826579451560974,
-0.16463200747966766,
0.05426986888051033,
-0.025396011769771576,
0.08680921047925949,
-0.2157660573720932,
-0.038948338478803635,
0.03492550924420357,
0.0033514630049467087,
-0.026338720694184303,
0.1159391924738884,
0.04105430841445923,
0.009594596922397614,
-0.0366186797618866,
-0.14745503664016724,
0.028892679139971733,
0.06139521673321724,
-0.04870668053627014,
-0.02577054500579834
]
|
77c321dc940a437b5f8e06486f708b421ce4f75d |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/fever-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:36:21+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:38:47+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
0c838bb2f062e54c2f1f91b3c3691270f03c3446 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/climate-fever-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:39:40+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:42:05+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
56a8a7fc3e8ab80e8bde4c905c3eaf329cecf174 |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/signal1m-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:42:23+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:44:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
3a0447e841f716d4c696864c9890faa84ae4d58e |
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | income/nq-top-20-gen-queries | [
"task_categories:text-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-24T19:45:03+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": {"msmarco": ["1M<n<10M"], "trec-covid": ["100k<n<1M"], "nfcorpus": ["1K<n<10K"], "nq": ["1M<n<10M"], "hotpotqa": ["1M<n<10M"], "fiqa": ["10K<n<100K"], "arguana": ["1K<n<10K"], "touche-2020": ["100K<n<1M"], "cqadupstack": ["100K<n<1M"], "quora": ["100K<n<1M"], "dbpedia": ["1M<n<10M"], "scidocs": ["10K<n<100K"], "fever": ["1M<n<10M"], "climate-fever": ["1M<n<10M"], "scifact": ["1K<n<10K"]}, "source_datasets": [], "task_categories": ["text-retrieval"], "paperswithcode_id": "beir", "pretty_name": "BEIR Benchmark"} | 2023-01-24T19:46:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us
| NFCorpus: 20 generated queries (BEIR Benchmark)
===============================================
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
* DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
* id (str): unique document id in NFCorpus in the BEIR benchmark ('URL').
* Questions generated: 20
* Code used for generation: evaluate\_anserini\_docT5query\_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus
Dataset Card for BEIR Benchmark
===============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Leaderboard: URL
* Point of Contact: URL@URL
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
* Fact-checking: FEVER, Climate-FEVER, SciFact
* Question-Answering: NQ, HotpotQA, FiQA-2018
* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
* News Retrieval: TREC-NEWS, Robust04
* Argument Retrieval: Touche-2020, ArguAna
* Duplicate Question Retrieval: Quora, CqaDupstack
* Citation-Prediction: SCIDOCS
* Tweet Retrieval: Signal-1M
* Entity Retrieval: DBPedia
All these datasets have been preprocessed and can be used for your experiments.
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found here.
### Languages
All tasks are in English ('en').
Dataset Structure
-----------------
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{"\_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}'
* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\_id' with unique query identifier and 'text' with query text. For example: '{"\_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}'
* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'
### Data Instances
A high level example of any beir dataset:
### Data Fields
Examples from all configurations have the following features:
### Corpus
* 'corpus': a 'dict' feature representing the document title and passage text, made up of:
+ '\_id': a 'string' feature representing the unique document id
- 'title': a 'string' feature, denoting the title of the document.
- 'text': a 'string' feature, denoting the text of the document.
### Queries
* 'queries': a 'dict' feature representing the query, made up of:
+ '\_id': a 'string' feature representing the unique query id
+ 'text': a 'string' feature, denoting the text of the query.
### Qrels
* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:
+ '\_id': a 'string' feature representing the query id
- '\_id': a 'string' feature, denoting the document id.
- 'score': a 'int32' feature, denoting the relevance judgement between query and document.
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Cite as:
### Contributions
Thanks to @Nthakur20 for adding this dataset.
| [
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL",
"### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'",
"### Data Instances\n\n\nA high level example of any beir dataset:",
"### Data Fields\n\n\nExamples from all configurations have the following features:",
"### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.",
"### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.",
"### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCite as:",
"### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset."
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"passage: TAGS\n#task_categories-text-retrieval #multilinguality-monolingual #language-English #license-cc-by-sa-4.0 #region-us \n### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here.",
"passage: ### Languages\n\n\nAll tasks are in English ('en').\n\n\nDataset Structure\n-----------------\n\n\nAll BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:\n\n\n* 'corpus' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with three fields '\\_id' with unique document identifier, 'title' with document title (optional) and 'text' with document paragraph or passage. For example: '{\"\\_id\": \"doc1\", \"title\": \"Albert Einstein\", \"text\": \"Albert Einstein was a German-born....\"}'\n* 'queries' file: a '.jsonl' file (jsonlines) that contains a list of dictionaries, each with two fields '\\_id' with unique query identifier and 'text' with query text. For example: '{\"\\_id\": \"q1\", \"text\": \"Who developed the mass-energy equivalence formula?\"}'\n* 'qrels' file: a '.tsv' file (tab-seperated) that contains three columns, i.e. the 'query-id', 'corpus-id' and 'score' in this order. Keep 1st row as header. For example: 'q1 doc1 1'### Data Instances\n\n\nA high level example of any beir dataset:### Data Fields\n\n\nExamples from all configurations have the following features:### Corpus\n\n\n* 'corpus': a 'dict' feature representing the document title and passage text, made up of:\n\t+ '\\_id': a 'string' feature representing the unique document id\n\t\t- 'title': a 'string' feature, denoting the title of the document.\n\t\t- 'text': a 'string' feature, denoting the text of the document.### Queries\n\n\n* 'queries': a 'dict' feature representing the query, made up of:\n\t+ '\\_id': a 'string' feature representing the unique query id\n\t+ 'text': a 'string' feature, denoting the text of the query.### Qrels\n\n\n* 'qrels': a 'dict' feature representing the query document relevance judgements, made up of:\n\t+ '\\_id': a 'string' feature representing the query id\n\t\t- '\\_id': a 'string' feature, denoting the document id.\n\t\t- 'score': a 'int32' feature, denoting the relevance judgement between query and document.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nCite as:",
"passage: ### Contributions\n\n\nThanks to @Nthakur20 for adding this dataset.Top-20 generated queries for every passage in NFCorpus\n\n\nDataset Card for BEIR Benchmark\n===============================\n\n\nTable of Contents\n-----------------\n\n\n* Dataset Description\n\t+ Dataset Summary\n\t+ Supported Tasks and Leaderboards\n\t+ Languages\n* Dataset Structure\n\t+ Data Instances\n\t+ Data Fields\n\t+ Data Splits\n* Dataset Creation\n\t+ Curation Rationale\n\t+ Source Data\n\t+ Annotations\n\t+ Personal and Sensitive Information\n* Considerations for Using the Data\n\t+ Social Impact of Dataset\n\t+ Discussion of Biases\n\t+ Other Known Limitations\n* Additional Information\n\t+ Dataset Curators\n\t+ Licensing Information\n\t+ Citation Information\n\t+ Contributions\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Paper: URL\n* Leaderboard: URL\n* Point of Contact: URL@URL### Dataset Summary\n\n\nBEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:\n\n\n* Fact-checking: FEVER, Climate-FEVER, SciFact\n* Question-Answering: NQ, HotpotQA, FiQA-2018\n* Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus\n* News Retrieval: TREC-NEWS, Robust04\n* Argument Retrieval: Touche-2020, ArguAna\n* Duplicate Question Retrieval: Quora, CqaDupstack\n* Citation-Prediction: SCIDOCS\n* Tweet Retrieval: Signal-1M\n* Entity Retrieval: DBPedia\n\n\nAll these datasets have been preprocessed and can be used for your experiments.### Supported Tasks and Leaderboards\n\n\nThe dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.\n\n\nThe current best performing models can be found here."
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|
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