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3623f65cac583c629a9ad482247f101c241b0947 | # Dataset Card for "chenyu_label_0.8_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.8_64 | [
"region:us"
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|
ceab9663b67da64ff1bbd128d2425c52a6a1cb4c | # Dataset Card for "chenyu_mask_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_mask_64 | [
"region:us"
]
| 2023-03-02T14:26:31+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10038428.1, "num_examples": 91242}, {"name": "validation", "num_bytes": 1115380.9, "num_examples": 10138}], "download_size": 0, "dataset_size": 11153809.0}} | 2023-03-02T15:29:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_mask_64"
More Information needed | [
"# Dataset Card for \"chenyu_mask_64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_mask_64\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_mask_64\"\n\nMore Information needed"
]
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|
29b55613d0a701c118b5b1cdc6e90d28d9da137f | # Dataset Card for "chenyu_label_0.2_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.2_64 | [
"region:us"
]
| 2023-03-02T14:27:09+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 22581474.259608556, "num_examples": 36740}, {"name": "validation", "num_bytes": 2509530.740391446, "num_examples": 4083}], "download_size": 0, "dataset_size": 25091005.0}} | 2023-03-02T15:29:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.2_64"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.2_64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.2_64\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.2_64\"\n\nMore Information needed"
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|
02fe1da8db953dc155122000f6fc714c556ab068 | # Dataset Card for "chenyu_label_0.8_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.8_16 | [
"region:us"
]
| 2023-03-02T14:28:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6963334.680874696, "num_examples": 38893}, {"name": "validation", "num_bytes": 773803.3191253038, "num_examples": 4322}], "download_size": 0, "dataset_size": 7737138.0}} | 2023-03-02T15:29:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.8_16"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.8_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.8_16\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.8_16\"\n\nMore Information needed"
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|
e76a04c8e1e2ce5e660770463f2d9eccf08e4d68 | # Dataset Card for "chenyu_mask_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_mask_32 | [
"region:us"
]
| 2023-03-02T14:28:40+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4349637.991911224, "num_examples": 53852}, {"name": "validation", "num_bytes": 483329.00808877597, "num_examples": 5984}], "download_size": 0, "dataset_size": 4832967.0}} | 2023-03-02T15:29:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_mask_32"
More Information needed | [
"# Dataset Card for \"chenyu_mask_32\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_mask_32\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_mask_32\"\n\nMore Information needed"
]
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|
55e5665e71f1b5445efe48fa9136df2edd2ae855 | # Dataset Card for "chenyu_mask_72"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_mask_72 | [
"region:us"
]
| 2023-03-02T14:29:03+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11235591.986864993, "num_examples": 96749}, {"name": "validation", "num_bytes": 1248412.013135006, "num_examples": 10750}], "download_size": 0, "dataset_size": 12484004.0}} | 2023-03-02T15:29:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_mask_72"
More Information needed | [
"# Dataset Card for \"chenyu_mask_72\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_mask_72\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_mask_72\"\n\nMore Information needed"
]
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|
4b7437a4b1384e6d2ceb6f6c79487468e055d4ac | # Dataset Card for "chenyu_mask_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_mask_16 | [
"region:us"
]
| 2023-03-02T14:29:35+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 396987.0548030322, "num_examples": 7242}, {"name": "validation", "num_bytes": 44127.94519696781, "num_examples": 805}], "download_size": 0, "dataset_size": 441115.0}} | 2023-03-02T15:30:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_mask_16"
More Information needed | [
"# Dataset Card for \"chenyu_mask_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_mask_16\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_mask_16\"\n\nMore Information needed"
]
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|
290f6a0851c1df3bd4057b21d988bab2c5c527e1 |
# Dataset Card for Self Instruct
## Table of Contents
- [Dataset Card for Self Instruct](#dataset-card-for-self-instruct)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [self\_instruct](#self_instruct)
- [super\_natural\_instructions](#super_natural_instructions)
- [p3](#p3)
- [human\_eval](#human_eval)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [self\_instruct](#self_instruct-1)
- [super\_natural\_instructions](#super_natural_instructions-1)
- [p3](#p3-1)
- [human\_eval](#human_eval-1)
- [Data Fields](#data-fields)
- [self\_instruct](#self_instruct-2)
- [super\_natural\_instructions](#super_natural_instructions-2)
- [p3](#p3-2)
- [human\_eval](#human_eval-2)
- [Data Splits](#data-splits)
- [self\_instruct](#self_instruct-3)
- [super\_natural\_instructions](#super_natural_instructions-3)
- [p3](#p3-3)
- [human\_eval](#human_eval-3)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/yizhongw/self-instruct
- **Paper:** https://arxiv.org/abs/2212.10560
- **Leaderboard:**
- **Point of Contact:** Yizhong Wang
### Dataset Summary
Self-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.
A part of this framework, the Self-Instruct authors released a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors also released a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of [the Self Instruct paper](https://arxiv.org/abs/2212.10560).
To enable comparison on public datasets, Self-Instruct also contains 50k examples from the P3 and Super Natural Instructions datasets.
### Supported Tasks and Leaderboards
The datasets in Self-Instruct are designed for _instruction training_ pretrained language models. The following subsets are provided as part of Self Instruct.
#### self_instruct
82k prompts and model completions generated via OpenAI's `davinci` engine.
#### super_natural_instructions
50k expert written instructions and demonstrations sampled from the [Super Natural Instructions dataset](https://arxiv.org/abs/2204.07705)
#### p3
50k crowd-sourced instructions and demonstrations sampled from the [Public Pool of Prompts (P3) dataset](https://huggingface.co/datasets/bigscience/P3)
#### human_eval
252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of [the self-instruct paper](https://arxiv.org/abs/2212.10560). Please refer to [the human evaluation information]([human_eval/README.md](https://github.com/yizhongw/self-instruct/blob/main/human_eval/README.md)) for more details.
### Languages
The data in Self Instruct are in English (BCP-47 en).
## Dataset Structure
### Data Instances
#### self_instruct
An example pf "train" looks as follows:
```json
{
"prompt": "Explain the origin of life on earth. Output:",
"completion": " Life on Earth is believed to have begun about 3.5 billion years ago, when the planet was still in its early stages of formation. The earliest evidence for life on Earth are fossils of microorganisms (bacteria and archaea) that lived approximately 3.5 billion years ago. These organisms were capable of converting carbon dioxide into organic compounds using sunlight as an energy source. This process is known as photosynthesis.",
}
```
#### super_natural_instructions
An example pf "train" looks as follows:
```json
{
"prompt": "Write a correct answer for the question. You should reason about prototypical situations and provide the correct answer. Avoid answers that provide incomplete justification for the question.\n\nInput: name something people often buy on credit.\n\nOutput:",
"completion": " appliances",
}
```
#### p3
An example pf "train" looks as follows:
```json
{
"prompt": "Determine if the following two sentences paraphrase each other or not.\nSent 1: By adding sufficient oxygen to compensate for the metabolic usage , rebreathing the carbon dioxide and removing the gas , most of the volume is conserved .\nSent 2: By adding sufficient oxygen to compensate for the metabolic consumption , removing the carbon dioxide and reinhaling the gas , most of the volume is conserved .\n",
"completion": "No",
}
```
#### human_eval
An example pf "train" looks as follows:
```json
{
"id": "user_oriented_task_136",
"motivation_app": "Goodreads",
"instruction": "Choose the best books from the given genre.",
"instances": {
"input": ["Crime & Mystery"],
"output": [
"1- The Girl with the Dragon Tattoo\n2- And Then There Were None\n3- Angels & Demons\n4- Rebecca\n5- In Cold Blood\n6- The Godfather\n7- The Lovely Bones\n8- Gone Girl\n9- The Name of the Rose\n10- Shutter Island"
],
},
}
```
### Data Fields
The data fields for each configuration are as follows.
#### self_instruct
* `prompt`: The instruction provided to the model or human labeler.
* `completion`: A completion provided by the model or human labeler.
#### super_natural_instructions
* `prompt`: The instruction provided to the model or human labeler.
* `completion`: A completion provided by the model or human labeler.
#### p3
* `prompt`: The instruction provided to the model or human labeler.
* `completion`: A completion provided by the model or human labeler.
#### human_eval
* `id`: The ID associated with the labelling task
* `motivation_app`: The application associated with the task
* `instruction`: The instruction written by the human labeler.
* `instances.input`: The input that forms part of the complete instruction
* `instances.output`: The human written demonstration
### Data Splits
#### self_instruct
| | train |
|---------------|------:|
| self_instruct | 82612 |
#### super_natural_instructions
| | train | test |
|----------------------------|------:|------:|
| super_natural_instructions | 50000 | 11810 |
#### p3
| | train |
|----|------:|
| p3 | 52657 |
#### human_eval
| | train |
|------------|------:|
| human_eval | 252 |
## Dataset Creation
### 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
The `self_instruct` data is generated by a language model (GPT-3) and inevitably contains some errors or biases. The authors analyzed the data quality on 200 random instructions in our paper, and found that 46% of the data points may have problems. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{selfinstruct,
title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh},
journal={arXiv preprint arXiv:2212.10560},
year={2022}
}
``` | yizhongw/self_instruct | [
"license:apache-2.0",
"arxiv:2212.10560",
"arxiv:2204.07705",
"region:us"
]
| 2023-03-02T14:29:46+00:00 | {"license": "apache-2.0", "dataset_info": [{"config_name": "self_instruct", "features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20527462, "num_examples": 82612}], "download_size": 24113858, "dataset_size": 20527462}, {"config_name": "human_eval", "features": [{"name": "id", "dtype": "string"}, {"name": "motivation_app", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "instances", "sequence": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 151244, "num_examples": 252}], "download_size": 170193, "dataset_size": 151244}, {"config_name": "super_natural_instructions", "features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40352923, "num_examples": 50000}, {"name": "test", "num_bytes": 9713953, "num_examples": 11810}], "download_size": 52975509, "dataset_size": 50066876}, {"config_name": "prompt_source", "features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57368889, "num_examples": 52657}], "download_size": 60126945, "dataset_size": 57368889}, {"config_name": "p3", "features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57368889, "num_examples": 52657}], "download_size": 60126945, "dataset_size": 57368889}]} | 2023-03-07T10:07:36+00:00 | [
"2212.10560",
"2204.07705"
]
| []
| TAGS
#license-apache-2.0 #arxiv-2212.10560 #arxiv-2204.07705 #region-us
| Dataset Card for Self Instruct
==============================
Table of Contents
-----------------
* Dataset Card for Self Instruct
+ Table of Contents
+ Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
* self\_instruct
* super\_natural\_instructions
* p3
* human\_eval
- Languages
+ Dataset Structure
- Data Instances
* self\_instruct
* super\_natural\_instructions
* p3
* human\_eval
- Data Fields
* self\_instruct
* super\_natural\_instructions
* p3
* human\_eval
- Data Splits
* self\_instruct
* super\_natural\_instructions
* p3
* human\_eval
+ 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
- Citation Information
Dataset Description
-------------------
* Homepage:
* Repository: URL
* Paper: URL
* Leaderboard:
* Point of Contact: Yizhong Wang
### Dataset Summary
Self-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.
A part of this framework, the Self-Instruct authors released a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors also released a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of the Self Instruct paper.
To enable comparison on public datasets, Self-Instruct also contains 50k examples from the P3 and Super Natural Instructions datasets.
### Supported Tasks and Leaderboards
The datasets in Self-Instruct are designed for *instruction training* pretrained language models. The following subsets are provided as part of Self Instruct.
#### self\_instruct
82k prompts and model completions generated via OpenAI's 'davinci' engine.
#### super\_natural\_instructions
50k expert written instructions and demonstrations sampled from the Super Natural Instructions dataset
#### p3
50k crowd-sourced instructions and demonstrations sampled from the Public Pool of Prompts (P3) dataset
#### human\_eval
252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of the self-instruct paper. Please refer to the human evaluation information) for more details.
### Languages
The data in Self Instruct are in English (BCP-47 en).
Dataset Structure
-----------------
### Data Instances
#### self\_instruct
An example pf "train" looks as follows:
#### super\_natural\_instructions
An example pf "train" looks as follows:
#### p3
An example pf "train" looks as follows:
#### human\_eval
An example pf "train" looks as follows:
### Data Fields
The data fields for each configuration are as follows.
#### self\_instruct
* 'prompt': The instruction provided to the model or human labeler.
* 'completion': A completion provided by the model or human labeler.
#### super\_natural\_instructions
* 'prompt': The instruction provided to the model or human labeler.
* 'completion': A completion provided by the model or human labeler.
#### p3
* 'prompt': The instruction provided to the model or human labeler.
* 'completion': A completion provided by the model or human labeler.
#### human\_eval
* 'id': The ID associated with the labelling task
* 'motivation\_app': The application associated with the task
* 'instruction': The instruction written by the human labeler.
* 'URL': The input that forms part of the complete instruction
* 'URL': The human written demonstration
### Data Splits
#### self\_instruct
#### super\_natural\_instructions
#### p3
#### human\_eval
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
The 'self\_instruct' data is generated by a language model (GPT-3) and inevitably contains some errors or biases. The authors analyzed the data quality on 200 random instructions in our paper, and found that 46% of the data points may have problems. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
Additional Information
----------------------
### Dataset Curators
### Licensing Information
| [
"### Dataset Summary\n\n\nSelf-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.\n\n\nA part of this framework, the Self-Instruct authors released a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\n\n\nThe authors also released a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of the Self Instruct paper.\n\n\nTo enable comparison on public datasets, Self-Instruct also contains 50k examples from the P3 and Super Natural Instructions datasets.",
"### Supported Tasks and Leaderboards\n\n\nThe datasets in Self-Instruct are designed for *instruction training* pretrained language models. The following subsets are provided as part of Self Instruct.",
"#### self\\_instruct\n\n\n82k prompts and model completions generated via OpenAI's 'davinci' engine.",
"#### super\\_natural\\_instructions\n\n\n50k expert written instructions and demonstrations sampled from the Super Natural Instructions dataset",
"#### p3\n\n\n50k crowd-sourced instructions and demonstrations sampled from the Public Pool of Prompts (P3) dataset",
"#### human\\_eval\n\n\n252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of the self-instruct paper. Please refer to the human evaluation information) for more details.",
"### Languages\n\n\nThe data in Self Instruct are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### self\\_instruct\n\n\nAn example pf \"train\" looks as follows:",
"#### super\\_natural\\_instructions\n\n\nAn example pf \"train\" looks as follows:",
"#### p3\n\n\nAn example pf \"train\" looks as follows:",
"#### human\\_eval\n\n\nAn example pf \"train\" looks as follows:",
"### Data Fields\n\n\nThe data fields for each configuration are as follows.",
"#### self\\_instruct\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.",
"#### super\\_natural\\_instructions\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.",
"#### p3\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.",
"#### human\\_eval\n\n\n* 'id': The ID associated with the labelling task\n* 'motivation\\_app': The application associated with the task\n* 'instruction': The instruction written by the human labeler.\n* 'URL': The input that forms part of the complete instruction\n* 'URL': The human written demonstration",
"### Data Splits",
"#### self\\_instruct",
"#### super\\_natural\\_instructions",
"#### p3",
"#### human\\_eval\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\nThe 'self\\_instruct' data is generated by a language model (GPT-3) and inevitably contains some errors or biases. The authors analyzed the data quality on 200 random instructions in our paper, and found that 46% of the data points may have problems. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information"
]
| [
"TAGS\n#license-apache-2.0 #arxiv-2212.10560 #arxiv-2204.07705 #region-us \n",
"### Dataset Summary\n\n\nSelf-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.\n\n\nA part of this framework, the Self-Instruct authors released a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\n\n\nThe authors also released a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of the Self Instruct paper.\n\n\nTo enable comparison on public datasets, Self-Instruct also contains 50k examples from the P3 and Super Natural Instructions datasets.",
"### Supported Tasks and Leaderboards\n\n\nThe datasets in Self-Instruct are designed for *instruction training* pretrained language models. The following subsets are provided as part of Self Instruct.",
"#### self\\_instruct\n\n\n82k prompts and model completions generated via OpenAI's 'davinci' engine.",
"#### super\\_natural\\_instructions\n\n\n50k expert written instructions and demonstrations sampled from the Super Natural Instructions dataset",
"#### p3\n\n\n50k crowd-sourced instructions and demonstrations sampled from the Public Pool of Prompts (P3) dataset",
"#### human\\_eval\n\n\n252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of the self-instruct paper. Please refer to the human evaluation information) for more details.",
"### Languages\n\n\nThe data in Self Instruct are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### self\\_instruct\n\n\nAn example pf \"train\" looks as follows:",
"#### super\\_natural\\_instructions\n\n\nAn example pf \"train\" looks as follows:",
"#### p3\n\n\nAn example pf \"train\" looks as follows:",
"#### human\\_eval\n\n\nAn example pf \"train\" looks as follows:",
"### Data Fields\n\n\nThe data fields for each configuration are as follows.",
"#### self\\_instruct\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.",
"#### super\\_natural\\_instructions\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.",
"#### p3\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.",
"#### human\\_eval\n\n\n* 'id': The ID associated with the labelling task\n* 'motivation\\_app': The application associated with the task\n* 'instruction': The instruction written by the human labeler.\n* 'URL': The input that forms part of the complete instruction\n* 'URL': The human written demonstration",
"### Data Splits",
"#### self\\_instruct",
"#### super\\_natural\\_instructions",
"#### p3",
"#### human\\_eval\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\nThe 'self\\_instruct' data is generated by a language model (GPT-3) and inevitably contains some errors or biases. The authors analyzed the data quality on 200 random instructions in our paper, and found that 46% of the data points may have problems. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information"
]
| [
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4,
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4,
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| [
"passage: TAGS\n#license-apache-2.0 #arxiv-2212.10560 #arxiv-2204.07705 #region-us \n### Dataset Summary\n\n\nSelf-Instruct is a framework that helps language models improve their ability to follow natural language instructions. It does this by using the model's own generations to create a large collection of instructional data. With Self-Instruct, it is possible to improve the instruction-following capabilities of language models without relying on extensive manual annotation.\n\n\nA part of this framework, the Self-Instruct authors released a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\n\n\nThe authors also released a new set of 252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of the Self Instruct paper.\n\n\nTo enable comparison on public datasets, Self-Instruct also contains 50k examples from the P3 and Super Natural Instructions datasets.### Supported Tasks and Leaderboards\n\n\nThe datasets in Self-Instruct are designed for *instruction training* pretrained language models. The following subsets are provided as part of Self Instruct.#### self\\_instruct\n\n\n82k prompts and model completions generated via OpenAI's 'davinci' engine.#### super\\_natural\\_instructions\n\n\n50k expert written instructions and demonstrations sampled from the Super Natural Instructions dataset#### p3\n\n\n50k crowd-sourced instructions and demonstrations sampled from the Public Pool of Prompts (P3) dataset#### human\\_eval\n\n\n252 expert-written tasks and their instructions motivated by user-oriented applications (rather than well-studied NLP tasks). This data is used in the human evaluation section of the self-instruct paper. Please refer to the human evaluation information) for more details.### Languages\n\n\nThe data in Self Instruct are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------### Data Instances",
"passage: #### self\\_instruct\n\n\nAn example pf \"train\" looks as follows:#### super\\_natural\\_instructions\n\n\nAn example pf \"train\" looks as follows:#### p3\n\n\nAn example pf \"train\" looks as follows:#### human\\_eval\n\n\nAn example pf \"train\" looks as follows:### Data Fields\n\n\nThe data fields for each configuration are as follows.#### self\\_instruct\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.#### super\\_natural\\_instructions\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.#### p3\n\n\n* 'prompt': The instruction provided to the model or human labeler.\n* 'completion': A completion provided by the model or human labeler.#### human\\_eval\n\n\n* 'id': The ID associated with the labelling task\n* 'motivation\\_app': The application associated with the task\n* 'instruction': The instruction written by the human labeler.\n* 'URL': The input that forms part of the complete instruction\n* 'URL': The human written demonstration### Data Splits#### self\\_instruct#### super\\_natural\\_instructions#### p3#### human\\_eval\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"
]
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|
c7d7bbe57adb4366cdb1df18b31075ba9fafd6ec | # Dataset Card for "chenyu_label_0.2_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.2_16 | [
"region:us"
]
| 2023-03-02T14:29:55+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6521321.650540137, "num_examples": 36740}, {"name": "validation", "num_bytes": 724729.3494598633, "num_examples": 4083}], "download_size": 0, "dataset_size": 7246051.0}} | 2023-03-02T15:30:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.2_16"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.2_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.2_16\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.2_16\"\n\nMore Information needed"
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|
5ea2fdbfa1a7a29f57c1d757140edce4b6ea6ffe | # Dataset Card for "chenyu_label_0.5_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.5_16 | [
"region:us"
]
| 2023-03-02T14:30:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6743113.545731417, "num_examples": 37825}, {"name": "validation", "num_bytes": 749274.4542685829, "num_examples": 4203}], "download_size": 0, "dataset_size": 7492388.0}} | 2023-03-02T15:30:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.5_16"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.5_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.5_16\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.5_16\"\n\nMore Information needed"
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|
f4fec466ce5648ff1cb0c2c9dc36ef8c8d011e91 | # Dataset Card for "chenyu_label_0.8_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.8_96 | [
"region:us"
]
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| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.8_96"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.8_96\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.8_96\"\n\nMore Information needed"
]
| [
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18
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.8_96\"\n\nMore Information needed"
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|
31eb08cf2903b4daff11baacad24e6511b5a1ec1 | # Dataset Card for "miniwob_plusplus_T5_labeled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwob_plusplus_T5_labeled | [
"region:us"
]
| 2023-03-02T14:31:41+00:00 | {"dataset_info": {"features": [{"name": "episodes", "dtype": "string"}, {"name": "target_actions", "dtype": "string"}, {"name": "target_refs", "dtype": "string"}, {"name": "target_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 144354418, "num_examples": 228097}], "download_size": 10399106, "dataset_size": 144354418}} | 2023-03-02T14:32:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwob_plusplus_T5_labeled"
More Information needed | [
"# Dataset Card for \"miniwob_plusplus_T5_labeled\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwob_plusplus_T5_labeled\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwob_plusplus_T5_labeled\"\n\nMore Information needed"
]
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|
5f9ee51a9c54073a638dcdd3d4aa845575a12b49 | # Dataset Card for "chenyu_mask_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_mask_96 | [
"region:us"
]
| 2023-03-02T14:31:59+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14231274.228681685, "num_examples": 107748}, {"name": "validation", "num_bytes": 1581384.771318315, "num_examples": 11973}], "download_size": 0, "dataset_size": 15812659.0}} | 2023-03-02T15:30:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_mask_96"
More Information needed | [
"# Dataset Card for \"chenyu_mask_96\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_mask_96\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_mask_96\"\n\nMore Information needed"
]
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|
a579dcd7be05748173666a13717ca008f3805d3e | # Dataset Card for "chenyu_label_0.8_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.8_32 | [
"region:us"
]
| 2023-03-02T14:32:25+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 13201256.187504338, "num_examples": 38893}, {"name": "validation", "num_bytes": 1466994.8124956612, "num_examples": 4322}], "download_size": 0, "dataset_size": 14668251.0}} | 2023-03-02T15:30:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.8_32"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.8_32\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.8_32\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.8_32\"\n\nMore Information needed"
]
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|
eb123a05db4c33760a7fb6c7873cfd73a35e6b9f | # Dataset Card for "chenyu_label_0.2_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.2_32 | [
"region:us"
]
| 2023-03-02T14:32:59+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 12265067.11608652, "num_examples": 36740}, {"name": "validation", "num_bytes": 1363044.8839134802, "num_examples": 4083}], "download_size": 0, "dataset_size": 13628112.0}} | 2023-03-02T15:31:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.2_32"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.2_32\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.2_32\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.2_32\"\n\nMore Information needed"
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|
bf7ff8ace138cedcda17ff01b222d99680ef4692 | # Dataset Card for "chenyu_label_0.8_72"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.8_72 | [
"region:us"
]
| 2023-03-02T14:33:36+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 26831739.356959388, "num_examples": 38893}, {"name": "validation", "num_bytes": 2981687.643040611, "num_examples": 4322}], "download_size": 0, "dataset_size": 29813427.0}} | 2023-03-02T15:31:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.8_72"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.8_72\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.8_72\"\n\nMore Information needed"
]
| [
6,
18
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.8_72\"\n\nMore Information needed"
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|
4254e778ad574752507f63e102f32cfae4fb4d1e | # Dataset Card for "chenyu_label_0.5_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.5_96 | [
"region:us"
]
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| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.5_96"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.5_96\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.5_96\"\n\nMore Information needed"
]
| [
6,
18
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.5_96\"\n\nMore Information needed"
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|
68f81105ac4f1ac5d2f608c2f4b9c2d592df5101 | # Dataset Card for "chenyu_label_0.2_72"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.2_72 | [
"region:us"
]
| 2023-03-02T14:35:17+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 25024384.115327142, "num_examples": 36740}, {"name": "validation", "num_bytes": 2781016.884672856, "num_examples": 4083}], "download_size": 0, "dataset_size": 27805401.0}} | 2023-03-02T15:31:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.2_72"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.2_72\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.2_72\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.2_72\"\n\nMore Information needed"
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|
3b588e7a0658380d7dc3cfad50639a8ef4d99fda | # Dataset Card for "chenyu_label_0.2_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.2_96 | [
"region:us"
]
| 2023-03-02T14:36:08+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}, {"name": "72", "dtype": "int64"}, {"name": "73", "dtype": "int64"}, {"name": "74", "dtype": "int64"}, {"name": "75", "dtype": "int64"}, {"name": "76", "dtype": "int64"}, {"name": "77", "dtype": "int64"}, {"name": "78", "dtype": "int64"}, {"name": "79", "dtype": "int64"}, {"name": "80", "dtype": "int64"}, {"name": "81", "dtype": "int64"}, {"name": "82", "dtype": "int64"}, {"name": "83", "dtype": "int64"}, {"name": "84", "dtype": "int64"}, {"name": "85", "dtype": "int64"}, {"name": "86", "dtype": "int64"}, {"name": "87", "dtype": "int64"}, {"name": "88", "dtype": "int64"}, {"name": "89", "dtype": "int64"}, {"name": "90", "dtype": "int64"}, {"name": "91", "dtype": "int64"}, {"name": "92", "dtype": "int64"}, {"name": "93", "dtype": "int64"}, {"name": "94", "dtype": "int64"}, {"name": "95", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 32193011.232883424, "num_examples": 36740}, {"name": "validation", "num_bytes": 3577682.7671165764, "num_examples": 4083}], "download_size": 0, "dataset_size": 35770694.0}} | 2023-03-02T15:31:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.2_96"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.2_96\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.2_96\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.2_96\"\n\nMore Information needed"
]
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|
81ab80c74b8659bf4596f96d5174ad12b20775d7 | # Dataset Card for "chenyu_label_0.5_72"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.5_72 | [
"region:us"
]
| 2023-03-02T14:36:59+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 25931250.588298276, "num_examples": 37825}, {"name": "validation", "num_bytes": 2881402.4117017225, "num_examples": 4203}], "download_size": 0, "dataset_size": 28812653.0}} | 2023-03-02T15:31:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.5_72"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.5_72\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.5_72\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.5_72\"\n\nMore Information needed"
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|
f9f2ac8d1f435187b949bf37b31e21320df8b216 | # Dataset Card for "chenyu_label_0.5_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.5_64 | [
"region:us"
]
| 2023-03-02T14:37:40+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 23408905.925216522, "num_examples": 37825}, {"name": "validation", "num_bytes": 2601127.0747834775, "num_examples": 4203}], "download_size": 0, "dataset_size": 26010033.0}} | 2023-03-02T15:32:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.5_64"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.5_64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.5_64\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.5_64\"\n\nMore Information needed"
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|
4df2a56fcfbb79f6aa977168d2be7783d1041a57 | # Dataset Card for "chenyu_label_0.5_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/chenyu_label_0.5_32 | [
"region:us"
]
| 2023-03-02T14:38:30+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 12733446.771794993, "num_examples": 37825}, {"name": "validation", "num_bytes": 1414902.228205006, "num_examples": 4203}], "download_size": 0, "dataset_size": 14148349.0}} | 2023-03-02T15:32:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chenyu_label_0.5_32"
More Information needed | [
"# Dataset Card for \"chenyu_label_0.5_32\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chenyu_label_0.5_32\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chenyu_label_0.5_32\"\n\nMore Information needed"
]
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|
e71f6fdfee081996574f9a356522a311d66d569b | # Dataset Card for "index"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/index | [
"region:us"
]
| 2023-03-02T14:47:32+00:00 | {"dataset_info": {"features": [{"name": "index", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 535, "num_examples": 20}], "download_size": 745, "dataset_size": 535}} | 2023-03-02T14:47:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "index"
More Information needed | [
"# Dataset Card for \"index\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"index\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"index\"\n\nMore Information needed"
]
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|
f3ca985d5e0c34499b6d8db2f8585fc01bd2d893 | # Dataset Card for "summarization-sft-heirarchical-valid1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmayhem93/summarization-sft-heirarchical-valid1 | [
"region:us"
]
| 2023-03-02T14:55:09+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86573178, "num_examples": 33088}], "download_size": 24194064, "dataset_size": 86573178}} | 2023-03-02T14:55:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "summarization-sft-heirarchical-valid1"
More Information needed | [
"# Dataset Card for \"summarization-sft-heirarchical-valid1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"summarization-sft-heirarchical-valid1\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"summarization-sft-heirarchical-valid1\"\n\nMore Information needed"
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|
2220d9a110482550a04513da9d8b2802dfb0f031 |
# Dataset Card for ECMWF CAMS Forecast
## Dataset Description
- **Homepage: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-forecasts?tab=overview
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact: [email protected]
### Dataset Summary
This is a dataset of converted ECMWF CAMS Air Quality forecasts over Europe on a 0.1x0.1 degree grid. The data is available on a 3-year rolling archive, so this repo is attempting to keep more of that data public.
The data has been converted to Zarr, and only the height levels of 0m,50m, 250m,500m,1000m,2000m,3000m,and 5000m have been kept.
Additionally, the forecasts go out to 96 hours from ECMWF, but this dataset only contains forecasts up to 48 hours into the future, as it is more focused on being useful for short-term solar forecasting over the next 48 hours, and to reduce file size.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
Each day is a Zarr containing 13 different aerosols on the 6 height levels, going out 48 hourly-timesteps to the future, from midnight on that day. These can be opened with Zarr, and have been chunked into quarters spatially (along latitude and longitude),
and in a single chunk temporally and height-wise. In other words, each variable has 4 chunks. No data has been modified or changed from the original values.
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### 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
[More Information Needed]
### Contributions
[More Information Needed] | openclimatefix/ecmwf-cams-forecast | [
"license:mit",
"doi:10.57967/hf/0886",
"region:us"
]
| 2023-03-02T15:15:30+00:00 | {"license": "mit"} | 2023-05-08T19:33:24+00:00 | []
| []
| TAGS
#license-mit #doi-10.57967/hf/0886 #region-us
|
# Dataset Card for ECMWF CAMS Forecast
## Dataset Description
- Homepage: URL
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: jacob@URL
### Dataset Summary
This is a dataset of converted ECMWF CAMS Air Quality forecasts over Europe on a 0.1x0.1 degree grid. The data is available on a 3-year rolling archive, so this repo is attempting to keep more of that data public.
The data has been converted to Zarr, and only the height levels of 0m,50m, 250m,500m,1000m,2000m,3000m,and 5000m have been kept.
Additionally, the forecasts go out to 96 hours from ECMWF, but this dataset only contains forecasts up to 48 hours into the future, as it is more focused on being useful for short-term solar forecasting over the next 48 hours, and to reduce file size.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
Each day is a Zarr containing 13 different aerosols on the 6 height levels, going out 48 hourly-timesteps to the future, from midnight on that day. These can be opened with Zarr, and have been chunked into quarters spatially (along latitude and longitude),
and in a single chunk temporally and height-wise. In other words, each variable has 4 chunks. No data has been modified or changed from the original values.
### Data Instances
### Data Fields
### 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
### Contributions
| [
"# Dataset Card for ECMWF CAMS Forecast",
"## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: jacob@URL",
"### Dataset Summary\n\nThis is a dataset of converted ECMWF CAMS Air Quality forecasts over Europe on a 0.1x0.1 degree grid. The data is available on a 3-year rolling archive, so this repo is attempting to keep more of that data public.\nThe data has been converted to Zarr, and only the height levels of 0m,50m, 250m,500m,1000m,2000m,3000m,and 5000m have been kept. \nAdditionally, the forecasts go out to 96 hours from ECMWF, but this dataset only contains forecasts up to 48 hours into the future, as it is more focused on being useful for short-term solar forecasting over the next 48 hours, and to reduce file size.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure\n\nEach day is a Zarr containing 13 different aerosols on the 6 height levels, going out 48 hourly-timesteps to the future, from midnight on that day. These can be opened with Zarr, and have been chunked into quarters spatially (along latitude and longitude),\nand in a single chunk temporally and height-wise. In other words, each variable has 4 chunks. No data has been modified or changed from the original values.",
"### Data Instances",
"### Data Fields",
"### 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",
"### Contributions"
]
| [
"TAGS\n#license-mit #doi-10.57967/hf/0886 #region-us \n",
"# Dataset Card for ECMWF CAMS Forecast",
"## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: jacob@URL",
"### Dataset Summary\n\nThis is a dataset of converted ECMWF CAMS Air Quality forecasts over Europe on a 0.1x0.1 degree grid. The data is available on a 3-year rolling archive, so this repo is attempting to keep more of that data public.\nThe data has been converted to Zarr, and only the height levels of 0m,50m, 250m,500m,1000m,2000m,3000m,and 5000m have been kept. \nAdditionally, the forecasts go out to 96 hours from ECMWF, but this dataset only contains forecasts up to 48 hours into the future, as it is more focused on being useful for short-term solar forecasting over the next 48 hours, and to reduce file size.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure\n\nEach day is a Zarr containing 13 different aerosols on the 6 height levels, going out 48 hourly-timesteps to the future, from midnight on that day. These can be opened with Zarr, and have been chunked into quarters spatially (along latitude and longitude),\nand in a single chunk temporally and height-wise. In other words, each variable has 4 chunks. No data has been modified or changed from the original values.",
"### Data Instances",
"### Data Fields",
"### 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",
"### Contributions"
]
| [
23,
12,
30,
167,
10,
4,
110,
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5,
5,
5,
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4,
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"passage: TAGS\n#license-mit #doi-10.57967/hf/0886 #region-us \n# Dataset Card for ECMWF CAMS Forecast## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact: jacob@URL### Dataset Summary\n\nThis is a dataset of converted ECMWF CAMS Air Quality forecasts over Europe on a 0.1x0.1 degree grid. The data is available on a 3-year rolling archive, so this repo is attempting to keep more of that data public.\nThe data has been converted to Zarr, and only the height levels of 0m,50m, 250m,500m,1000m,2000m,3000m,and 5000m have been kept. \nAdditionally, the forecasts go out to 96 hours from ECMWF, but this dataset only contains forecasts up to 48 hours into the future, as it is more focused on being useful for short-term solar forecasting over the next 48 hours, and to reduce file size.### Supported Tasks and Leaderboards### Languages## Dataset Structure\n\nEach day is a Zarr containing 13 different aerosols on the 6 height levels, going out 48 hourly-timesteps to the future, from midnight on that day. These can be opened with Zarr, and have been chunked into quarters spatially (along latitude and longitude),\nand in a single chunk temporally and height-wise. In other words, each variable has 4 chunks. No data has been modified or changed from the original values.### Data Instances### Data Fields### 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### Contributions"
]
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|
dc6f07a9be919b327f56f794ddaaf99b898811f3 | # Dataset Card for "adv-ele"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LuisaRomana/adv-ele | [
"region:us"
]
| 2023-03-02T15:39:50+00:00 | {"dataset_info": {"features": [{"name": "ADV", "dtype": "string"}, {"name": "ELE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 430918.56140350876, "num_examples": 1732}, {"name": "test", "num_bytes": 107978.43859649122, "num_examples": 434}], "download_size": 294610, "dataset_size": 538897.0}} | 2023-03-02T15:39:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "adv-ele"
More Information needed | [
"# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
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|
fb1ee08b1a99e8cf850a1d43c654d6b6a5c08441 | # Dataset Card for "bert-base-uncased-finetuned-tquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dilanbakr/bert-base-uncased-finetuned-tquad | [
"region:us"
]
| 2023-03-02T15:42:30+00:00 | {"dataset_info": {"features": [{"name": "contexts", "dtype": "string"}, {"name": "questions", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "dtype": "string"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 10360301, "num_examples": 8308}, {"name": "validation", "num_bytes": 3803178, "num_examples": 2676}], "download_size": 1618809, "dataset_size": 14163479}} | 2023-03-02T15:42:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bert-base-uncased-finetuned-tquad"
More Information needed | [
"# Dataset Card for \"bert-base-uncased-finetuned-tquad\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bert-base-uncased-finetuned-tquad\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bert-base-uncased-finetuned-tquad\"\n\nMore Information needed"
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|
c53feb7ad2c78282130e174e829b24839b52607b |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### 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
[More Information Needed]
### Contributions
[More Information Needed] | Rcronshaw/chestxrays | [
"region:us"
]
| 2023-03-02T15:47:45+00:00 | {} | 2023-03-08T10:49:59+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### 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
### Contributions
| [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### 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",
"### Contributions"
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"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### 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",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### 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### Contributions"
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|
6d728a40f6539b74a81211df295e6fd70f3d86f5 | # Dataset Card for "adv-ele"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bhpardo/adv-ele | [
"region:us"
]
| 2023-03-02T16:02:13+00:00 | {"dataset_info": {"features": [{"name": "ADV", "dtype": "string"}, {"name": "ELE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 430918.56140350876, "num_examples": 1732}, {"name": "test", "num_bytes": 107978.43859649122, "num_examples": 434}], "download_size": 293711, "dataset_size": 538897.0}} | 2023-03-02T16:02:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "adv-ele"
More Information needed | [
"# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
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|
a7a8d744eb47f4e177ca6a3e6f68debbdb583238 | # Dataset Card for "fewshot_restaurants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/fewshot_restaurants | [
"region:us"
]
| 2023-03-02T16:54:48+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 13096, "num_examples": 48}, {"name": "validation", "num_bytes": 3433, "num_examples": 12}, {"name": "test", "num_bytes": 878494, "num_examples": 3588}], "download_size": 367718, "dataset_size": 895023}} | 2023-03-29T23:15:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fewshot_restaurants"
More Information needed | [
"# Dataset Card for \"fewshot_restaurants\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fewshot_restaurants\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fewshot_restaurants\"\n\nMore Information needed"
]
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|
a277443c54866e97d28edc8cf64a45d06f97ce7e | # Dataset Card for "summarization-sft-heirarchical-valid2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmayhem93/summarization-sft-heirarchical-valid2 | [
"region:us"
]
| 2023-03-02T17:01:51+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 131656080, "num_examples": 50720}], "download_size": 38150074, "dataset_size": 131656080}} | 2023-03-02T17:02:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "summarization-sft-heirarchical-valid2"
More Information needed | [
"# Dataset Card for \"summarization-sft-heirarchical-valid2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"summarization-sft-heirarchical-valid2\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"summarization-sft-heirarchical-valid2\"\n\nMore Information needed"
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|
7834572e5270d8106c41e494279c8cf0b0dc8130 | # Dataset Card for "uspto_full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | chenxran/uspto_full | [
"region:us"
]
| 2023-03-02T17:09:31+00:00 | {"dataset_info": {"features": [{"name": "reaction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 144904894, "num_examples": 1693749}, {"name": "valid", "num_bytes": 8025722, "num_examples": 94063}, {"name": "test", "num_bytes": 8090417, "num_examples": 94227}], "download_size": 50910823, "dataset_size": 161021033}} | 2023-03-02T17:09:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "uspto_full"
More Information needed | [
"# Dataset Card for \"uspto_full\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"uspto_full\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"uspto_full\"\n\nMore Information needed"
]
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|
6f665c7ec37ac2c8d20b0f6b90775531888dc666 | # Dataset Card for "tquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dilanbakr/tquad | [
"region:us"
]
| 2023-03-02T17:16:15+00:00 | {"dataset_info": {"features": [{"name": "contexts", "dtype": "string"}, {"name": "questions", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "dtype": "string"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 10360301, "num_examples": 8308}, {"name": "validation", "num_bytes": 3803178, "num_examples": 2676}], "download_size": 1618809, "dataset_size": 14163479}} | 2023-03-02T17:16:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tquad"
More Information needed | [
"# Dataset Card for \"tquad\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tquad\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tquad\"\n\nMore Information needed"
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|
9f153c3fd77460541f28e7c5d39d38a2f62a78fe | # Dataset Card for "mimic_attitude_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tanoManzo/mimic_attitude_dataset | [
"region:us"
]
| 2023-03-02T17:23:55+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 348621, "num_examples": 223}], "download_size": 213133, "dataset_size": 348621}} | 2023-03-02T17:23:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mimic_attitude_dataset"
More Information needed | [
"# Dataset Card for \"mimic_attitude_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mimic_attitude_dataset\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mimic_attitude_dataset\"\n\nMore Information needed"
]
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|
af031c98dfafbf4791ba9b5af24cd793ecfa7db2 | # Dataset Card for "summarization-sft-heirarchical-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmayhem93/summarization-sft-heirarchical-test | [
"region:us"
]
| 2023-03-02T17:26:28+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 217854405, "num_examples": 83632}], "download_size": 62261487, "dataset_size": 217854405}} | 2023-03-02T17:27:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "summarization-sft-heirarchical-test"
More Information needed | [
"# Dataset Card for \"summarization-sft-heirarchical-test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"summarization-sft-heirarchical-test\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"summarization-sft-heirarchical-test\"\n\nMore Information needed"
]
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40b92367460ca9d8e55fa9096788492475ccf222 | # Dataset Card for "jupyter_python_max_line_length_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | loubnabnl/jupyter_python_max_line_length_1000 | [
"region:us"
]
| 2023-03-02T17:46:04+00:00 | {"dataset_info": {"features": [{"name": "hexsha", "dtype": "string"}, {"name": "ext", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_repo_licenses", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "avg_line_length", "dtype": "float64"}, {"name": "max_line_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1809605.8191577208, "num_examples": 174}], "download_size": 6042172, "dataset_size": 1809605.8191577208}} | 2023-03-02T17:46:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "jupyter_python_max_line_length_1000"
More Information needed | [
"# Dataset Card for \"jupyter_python_max_line_length_1000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"jupyter_python_max_line_length_1000\"\n\nMore Information needed"
]
| [
6,
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"jupyter_python_max_line_length_1000\"\n\nMore Information needed"
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|
fd3cc52d6a4dcc54a3e919e2e0c92dfe54d0f853 |
# Failures in 3D printing Dataset
This is a small dataset of images from failures in 3D print. That idea of this dataset is use for train and object detection model for failures detection on 3D printing.
In the images it detected 4 categories:
- **Error**: This refer a any error in the part except the type of error known like spaghetti
- **Extrusor**: The base of the extrusor
- **Part**: The part is the piece that is printing
- **Spagheti**: This is a type of error produced because the extrusor is printing on the air
## Structure
The structure of the dataset is
- **image_id:** Id of the image
- **image:** Image instance in PIL format
- **width:** Width of the image in pixels
- **height:** Height of the image in pixels
- **objects:** bounding boxes in the images
- **bbox:** coordinates of the bounding box. The coordinates are [x_center, y_center, bbox width, bbox height]
- **categories:** category of the bounding box. The categories are 0: error, 1: extrusor, 2: part and 3: spaghetti
## Download the dataset
```python
from datasets import load_dataset
dataset = load_dataset('Javiai/failures-3D-print')
```
## Show the Bounding Boxes
```python
import numpy as np
import os
from PIL import Image, ImageDraw
image = dataset["train"][0]["image"]
annotations = dataset["train"][0]["objects"]
draw = ImageDraw.Draw(image)
categories = ['error','extrusor','part','spagheti']
id2label = {index: x for index, x in enumerate(categories, start=0)}
label2id = {v: k for k, v in id2label.items()}
for i in range(len(annotations["categories"])):
box = annotations["bbox"][i]
class_idx = annotations["categories"][i]
x, y, w, h = tuple(box)
draw.rectangle((x - w/2, y - h/2, x + w/2, y + h/2), outline="red", width=1)
draw.text((x - w/2, y - h/2), id2label[class_idx], fill="white")
image
``` | Javiai/failures-3D-print | [
"task_categories:object-detection",
"size_categories:n<1K",
"license:unknown",
"region:us"
]
| 2023-03-02T18:13:10+00:00 | {"license": "unknown", "size_categories": ["n<1K"], "task_categories": ["object-detection"], "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "bbox", "sequence": {"sequence": "int64"}}, {"name": "categories", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 3878997, "num_examples": 73}], "download_size": 3549033, "dataset_size": 3878997}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-10-06T10:26:23+00:00 | []
| []
| TAGS
#task_categories-object-detection #size_categories-n<1K #license-unknown #region-us
|
# Failures in 3D printing Dataset
This is a small dataset of images from failures in 3D print. That idea of this dataset is use for train and object detection model for failures detection on 3D printing.
In the images it detected 4 categories:
- Error: This refer a any error in the part except the type of error known like spaghetti
- Extrusor: The base of the extrusor
- Part: The part is the piece that is printing
- Spagheti: This is a type of error produced because the extrusor is printing on the air
## Structure
The structure of the dataset is
- image_id: Id of the image
- image: Image instance in PIL format
- width: Width of the image in pixels
- height: Height of the image in pixels
- objects: bounding boxes in the images
- bbox: coordinates of the bounding box. The coordinates are [x_center, y_center, bbox width, bbox height]
- categories: category of the bounding box. The categories are 0: error, 1: extrusor, 2: part and 3: spaghetti
## Download the dataset
## Show the Bounding Boxes
| [
"# Failures in 3D printing Dataset\n\nThis is a small dataset of images from failures in 3D print. That idea of this dataset is use for train and object detection model for failures detection on 3D printing.\n\nIn the images it detected 4 categories:\n\n- Error: This refer a any error in the part except the type of error known like spaghetti\n- Extrusor: The base of the extrusor\n- Part: The part is the piece that is printing\n- Spagheti: This is a type of error produced because the extrusor is printing on the air",
"## Structure\n\nThe structure of the dataset is\n\n- image_id: Id of the image\n- image: Image instance in PIL format\n- width: Width of the image in pixels\n- height: Height of the image in pixels\n- objects: bounding boxes in the images\n - bbox: coordinates of the bounding box. The coordinates are [x_center, y_center, bbox width, bbox height]\n - categories: category of the bounding box. The categories are 0: error, 1: extrusor, 2: part and 3: spaghetti",
"## Download the dataset",
"## Show the Bounding Boxes"
]
| [
"TAGS\n#task_categories-object-detection #size_categories-n<1K #license-unknown #region-us \n",
"# Failures in 3D printing Dataset\n\nThis is a small dataset of images from failures in 3D print. That idea of this dataset is use for train and object detection model for failures detection on 3D printing.\n\nIn the images it detected 4 categories:\n\n- Error: This refer a any error in the part except the type of error known like spaghetti\n- Extrusor: The base of the extrusor\n- Part: The part is the piece that is printing\n- Spagheti: This is a type of error produced because the extrusor is printing on the air",
"## Structure\n\nThe structure of the dataset is\n\n- image_id: Id of the image\n- image: Image instance in PIL format\n- width: Width of the image in pixels\n- height: Height of the image in pixels\n- objects: bounding boxes in the images\n - bbox: coordinates of the bounding box. The coordinates are [x_center, y_center, bbox width, bbox height]\n - categories: category of the bounding box. The categories are 0: error, 1: extrusor, 2: part and 3: spaghetti",
"## Download the dataset",
"## Show the Bounding Boxes"
]
| [
34,
128,
126,
5,
7
]
| [
"passage: TAGS\n#task_categories-object-detection #size_categories-n<1K #license-unknown #region-us \n# Failures in 3D printing Dataset\n\nThis is a small dataset of images from failures in 3D print. That idea of this dataset is use for train and object detection model for failures detection on 3D printing.\n\nIn the images it detected 4 categories:\n\n- Error: This refer a any error in the part except the type of error known like spaghetti\n- Extrusor: The base of the extrusor\n- Part: The part is the piece that is printing\n- Spagheti: This is a type of error produced because the extrusor is printing on the air## Structure\n\nThe structure of the dataset is\n\n- image_id: Id of the image\n- image: Image instance in PIL format\n- width: Width of the image in pixels\n- height: Height of the image in pixels\n- objects: bounding boxes in the images\n - bbox: coordinates of the bounding box. The coordinates are [x_center, y_center, bbox width, bbox height]\n - categories: category of the bounding box. The categories are 0: error, 1: extrusor, 2: part and 3: spaghetti## Download the dataset## Show the Bounding Boxes"
]
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|
2a85b352d7ab660b0b06cf61219ec4f8438a1328 | # Dataset Card for "gesture-prediction-9-classes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | qfrodicio/gesture-prediction-9-classes | [
"region:us"
]
| 2023-03-02T18:48:17+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "gestures", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 360893, "num_examples": 1392}, {"name": "test", "num_bytes": 96706, "num_examples": 354}, {"name": "validation", "num_bytes": 120010, "num_examples": 449}], "download_size": 173305, "dataset_size": 577609}} | 2023-03-10T11:52:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "gesture-prediction-9-classes"
More Information needed | [
"# Dataset Card for \"gesture-prediction-9-classes\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"gesture-prediction-9-classes\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"gesture-prediction-9-classes\"\n\nMore Information needed"
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|
d32a69c016c4bc66140c985040f5528d1f5f5a8d | Just the tags of 116k random posts on e621. Used to train the [E621-Tag-Autocomplete](https://huggingface.co/0Tick/e621TagAutocomplete) Model | 0Tick/E621-Random-PostsTag-Scrape | [
"language:en",
"license:mit",
"region:us"
]
| 2023-03-02T18:49:03+00:00 | {"language": ["en"], "license": "mit"} | 2023-03-02T18:56:05+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-mit #region-us
| Just the tags of 116k random posts on e621. Used to train the E621-Tag-Autocomplete Model | []
| [
"TAGS\n#language-English #license-mit #region-us \n"
]
| [
15
]
| [
"passage: TAGS\n#language-English #license-mit #region-us \n"
]
| [
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|
be028eb3402be837fc7fdee30d0fc6eda40880d2 | Just the tags of 118k random posts on [danbooru](danbooru.donmai.us). Used to train the [Danbooru-Tag-Autocomplete](https://huggingface.co/0Tick/e621TagAutocomplete) Model | 0Tick/Danbooru-Random-Posts-Scrape | [
"language:en",
"license:mit",
"region:us"
]
| 2023-03-02T19:00:04+00:00 | {"language": ["en"], "license": "mit"} | 2023-03-02T19:05:35+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-mit #region-us
| Just the tags of 118k random posts on danbooru. Used to train the Danbooru-Tag-Autocomplete Model | []
| [
"TAGS\n#language-English #license-mit #region-us \n"
]
| [
15
]
| [
"passage: TAGS\n#language-English #license-mit #region-us \n"
]
| [
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|
3ca34a08a9e711d44c43bb337729a4895ef7ee19 | # Dataset Card for "gesture-prediction-5-classes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | qfrodicio/gesture-prediction-5-classes | [
"region:us"
]
| 2023-03-02T19:08:59+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "gestures", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 282355, "num_examples": 1124}, {"name": "test", "num_bytes": 68683, "num_examples": 270}, {"name": "validation", "num_bytes": 92021, "num_examples": 354}], "download_size": 133778, "dataset_size": 443059}} | 2023-03-10T11:53:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "gesture-prediction-5-classes"
More Information needed | [
"# Dataset Card for \"gesture-prediction-5-classes\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"gesture-prediction-5-classes\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"gesture-prediction-5-classes\"\n\nMore Information needed"
]
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|
c2e5fcd86a2ddefd912277479e86622d453512b4 | # Dataset Card for "gesture-prediction-21-classes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | qfrodicio/gesture-prediction-21-classes | [
"region:us"
]
| 2023-03-02T19:51:32+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "gestures", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 437051, "num_examples": 1649}, {"name": "test", "num_bytes": 115160, "num_examples": 423}, {"name": "validation", "num_bytes": 142541, "num_examples": 528}], "download_size": 207086, "dataset_size": 694752}} | 2023-03-10T11:49:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "gesture-prediction-21-classes"
More Information needed | [
"# Dataset Card for \"gesture-prediction-21-classes\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"gesture-prediction-21-classes\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"gesture-prediction-21-classes\"\n\nMore Information needed"
]
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|
1f76e1a37bb59da49016181b0d7c5756da01b9e7 | # Dataset Card for "small-persian-QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Hamid-reza/small-persian-QA | [
"region:us"
]
| 2023-03-02T20:02:45+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 2525696.3482460035, "num_examples": 1261}, {"name": "validation", "num_bytes": 260467.5806451613, "num_examples": 130}], "download_size": 1158438, "dataset_size": 2786163.9288911647}} | 2023-03-02T20:06:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "small-persian-QA"
More Information needed | [
"# Dataset Card for \"small-persian-QA\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"small-persian-QA\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"small-persian-QA\"\n\nMore Information needed"
]
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|
f9b6e0e72918d6108725690a9fa7768a685c438f |
# Sequence Based Protein - Peptide Binding Dataset
- Data sources:
- [Huang Laboratory](http://huanglab.phys.hust.edu.cn)
- [Propedia](http://bioinfo.dcc.ufmg.br/propedia/)
- [YAPP-Cd](https://www.biorxiv.org/content/10.1101/2021.06.16.448765v1)
- Dataset size: 16,370 sets of Protein-Peptide sequences that bind, the protein sequence
contains only the relevant chain.
- Train / Val split: the dataset is split to 80% train 10% val and 10% test.
| ronig/protein_binding_sequences | [
"license:mit",
"region:us"
]
| 2023-03-02T20:23:37+00:00 | {"license": "mit", "pretty_name": "Sequence Based Protein - Peptide Binding Dataset"} | 2023-06-24T09:04:53+00:00 | []
| []
| TAGS
#license-mit #region-us
|
# Sequence Based Protein - Peptide Binding Dataset
- Data sources:
- Huang Laboratory
- Propedia
- YAPP-Cd
- Dataset size: 16,370 sets of Protein-Peptide sequences that bind, the protein sequence
contains only the relevant chain.
- Train / Val split: the dataset is split to 80% train 10% val and 10% test.
| [
"# Sequence Based Protein - Peptide Binding Dataset\n\n- Data sources:\n - Huang Laboratory\n - Propedia\n - YAPP-Cd\n- Dataset size: 16,370 sets of Protein-Peptide sequences that bind, the protein sequence\n contains only the relevant chain.\n- Train / Val split: the dataset is split to 80% train 10% val and 10% test."
]
| [
"TAGS\n#license-mit #region-us \n",
"# Sequence Based Protein - Peptide Binding Dataset\n\n- Data sources:\n - Huang Laboratory\n - Propedia\n - YAPP-Cd\n- Dataset size: 16,370 sets of Protein-Peptide sequences that bind, the protein sequence\n contains only the relevant chain.\n- Train / Val split: the dataset is split to 80% train 10% val and 10% test."
]
| [
11,
88
]
| [
"passage: TAGS\n#license-mit #region-us \n# Sequence Based Protein - Peptide Binding Dataset\n\n- Data sources:\n - Huang Laboratory\n - Propedia\n - YAPP-Cd\n- Dataset size: 16,370 sets of Protein-Peptide sequences that bind, the protein sequence\n contains only the relevant chain.\n- Train / Val split: the dataset is split to 80% train 10% val and 10% test."
]
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|
dcf05dfeed39202f8197655c9077ba6af094c04a | # Dataset Card for "dummy-issues-database"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MiguelAngeloCwb/dummy-issues-database | [
"region:us"
]
| 2023-03-02T20:45:07+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "labels", "list": [{"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "assignees", "list": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "comments", "dtype": "int64"}, {"name": "created_at", "dtype": "string"}, {"name": "updated_at", "dtype": "string"}, {"name": "closed_at", "dtype": "string"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "diff_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "merged_at", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "total_count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16036629, "num_examples": 5609}], "download_size": 3927676, "dataset_size": 16036629}} | 2023-03-02T20:45:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dummy-issues-database"
More Information needed | [
"# Dataset Card for \"dummy-issues-database\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dummy-issues-database\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dummy-issues-database\"\n\nMore Information needed"
]
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|
5ee64c0e7814be61d9a9a9e528f5bfc8d5f04273 | # Dataset Card for "tobacco3482"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | maveriq/tobacco3482 | [
"region:us"
]
| 2023-03-02T20:46:53+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ADVE", "1": "Email", "2": "Form", "3": "Letter", "4": "Memo", "5": "News", "6": "Note", "7": "Report", "8": "Resume", "9": "Scientific"}}}}], "splits": [{"name": "train", "num_bytes": 1409969631.808, "num_examples": 3482}], "download_size": 1733093218, "dataset_size": 1409969631.808}} | 2023-03-02T21:23:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tobacco3482"
More Information needed | [
"# Dataset Card for \"tobacco3482\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tobacco3482\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tobacco3482\"\n\nMore Information needed"
]
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|
017fd51f48faeddcc7bbedcacc10c428dcfcab0b | # Dataset Card for "dummy-issues-with-comments-database"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MiguelAngeloCwb/dummy-issues-with-comments-database | [
"region:us"
]
| 2023-03-02T20:58:08+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "labels", "list": [{"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "assignees", "list": [{"name": "avatar_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "login", "dtype": "string"}, {"name": "node_id", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "updated_at", "dtype": "string"}, {"name": "closed_at", "dtype": "string"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "diff_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "merged_at", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "total_count", "dtype": "int64"}, {"name": "url", "dtype": "string"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 11164942, "num_examples": 2078}], "download_size": 3724562, "dataset_size": 11164942}} | 2023-03-02T20:58:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dummy-issues-with-comments-database"
More Information needed | [
"# Dataset Card for \"dummy-issues-with-comments-database\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dummy-issues-with-comments-database\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dummy-issues-with-comments-database\"\n\nMore Information needed"
]
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|
ef72410d953ea08f2c240640a3637ef618dff830 | # Dataset Card for "dummy-comments-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MiguelAngeloCwb/dummy-comments-dataset | [
"region:us"
]
| 2023-03-02T21:35:11+00:00 | {"dataset_info": {"features": [{"name": "html_url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "comment_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26454835, "num_examples": 4787}], "download_size": 5859592, "dataset_size": 26454835}} | 2023-03-02T21:35:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dummy-comments-dataset"
More Information needed | [
"# Dataset Card for \"dummy-comments-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dummy-comments-dataset\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dummy-comments-dataset\"\n\nMore Information needed"
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65c62f45d957977605f7db4340694be65f44ec58 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### 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
[More Information Needed]
### Contributions
[More Information Needed] | EarthnDusk/FFXIV_Data_and_Lora | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"ffxiv",
"video game",
"mmorpg",
"stable diffusion",
"region:us"
]
| 2023-03-02T22:03:23+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Final fantasy XIV Miqote and More Data + Lora", "tags": ["ffxiv", "video game", "mmorpg", "stable diffusion"]} | 2023-03-15T08:13:52+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #ffxiv #video game #mmorpg #stable diffusion #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### 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
### Contributions
| [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
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"### 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"
]
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"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
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"#### Initial Data Collection and Normalization",
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"#### 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"
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|
6be63726708d97f4685936b45f9b3e0894c89bde |
# Anybooru
## Synthetic Anime Image Dataset
Synthetic anime image dataset generated using Andite's Anything-v4.5 checkpoint with Danbooru2021 tags collected in https://gwern.net/danbooru2021.
See https://github.com/1lint/anybooru for details and code to generate your own variant of the dataset. I have also uploaded the extracted Danbooru2021 tags to https://huggingface.co/datasets/lint/danbooru_tags, this dataset was generated with a small subset of the tags in `2021_0_pruned.parquet`.
Each string of tags was used to generate 4 different images with different seeds. This serves a similar purpose as random resize crop and image flip transformations to train the model to focus on general concepts encoded in the tags, rather than memorizing specific images.
## Quick Start
```
from datasets import load_dataset
dataset = load_dataset('lint/anybooru')
sample = dataset['train'][0]
image = sample['image']
tags = image.info['tags']
print(tags)
```
## Samples
Each row of samples share the same generation prompt (string of tags).
## Citations
```
@misc{danbooru2021, author = {Anonymous and Danbooru community and Gwern Branwen}, title = {Danbooru2021: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset}, howpublished = {\url{https://gwern.net/danbooru2021}}, url = {https://gwern.net/danbooru2021}, type = {dataset}, year = {2022}, month = {January}, timestamp = {2022-01-21}, note = {Accessed: 03/01/2023} }
```
| lint/anybooru | [
"license:openrail",
"region:us"
]
| 2023-03-03T00:43:12+00:00 | {"license": "openrail"} | 2023-03-03T01:20:54+00:00 | []
| []
| TAGS
#license-openrail #region-us
|
# Anybooru
## Synthetic Anime Image Dataset
Synthetic anime image dataset generated using Andite's Anything-v4.5 checkpoint with Danbooru2021 tags collected in URL
See URL for details and code to generate your own variant of the dataset. I have also uploaded the extracted Danbooru2021 tags to URL this dataset was generated with a small subset of the tags in '2021_0_pruned.parquet'.
Each string of tags was used to generate 4 different images with different seeds. This serves a similar purpose as random resize crop and image flip transformations to train the model to focus on general concepts encoded in the tags, rather than memorizing specific images.
## Quick Start
## Samples
Each row of samples share the same generation prompt (string of tags).
s
| [
"# Anybooru",
"## Synthetic Anime Image Dataset\n\nSynthetic anime image dataset generated using Andite's Anything-v4.5 checkpoint with Danbooru2021 tags collected in URL \nSee URL for details and code to generate your own variant of the dataset. I have also uploaded the extracted Danbooru2021 tags to URL this dataset was generated with a small subset of the tags in '2021_0_pruned.parquet'.\n\nEach string of tags was used to generate 4 different images with different seeds. This serves a similar purpose as random resize crop and image flip transformations to train the model to focus on general concepts encoded in the tags, rather than memorizing specific images.",
"## Quick Start",
"## Samples\n\nEach row of samples share the same generation prompt (string of tags). \n\n\n\n\ns"
]
| [
"TAGS\n#license-openrail #region-us \n",
"# Anybooru",
"## Synthetic Anime Image Dataset\n\nSynthetic anime image dataset generated using Andite's Anything-v4.5 checkpoint with Danbooru2021 tags collected in URL \nSee URL for details and code to generate your own variant of the dataset. I have also uploaded the extracted Danbooru2021 tags to URL this dataset was generated with a small subset of the tags in '2021_0_pruned.parquet'.\n\nEach string of tags was used to generate 4 different images with different seeds. This serves a similar purpose as random resize crop and image flip transformations to train the model to focus on general concepts encoded in the tags, rather than memorizing specific images.",
"## Quick Start",
"## Samples\n\nEach row of samples share the same generation prompt (string of tags). \n\n\n\n\ns"
]
| [
12,
4,
155,
3,
34
]
| [
"passage: TAGS\n#license-openrail #region-us \n# Anybooru## Synthetic Anime Image Dataset\n\nSynthetic anime image dataset generated using Andite's Anything-v4.5 checkpoint with Danbooru2021 tags collected in URL \nSee URL for details and code to generate your own variant of the dataset. I have also uploaded the extracted Danbooru2021 tags to URL this dataset was generated with a small subset of the tags in '2021_0_pruned.parquet'.\n\nEach string of tags was used to generate 4 different images with different seeds. This serves a similar purpose as random resize crop and image flip transformations to train the model to focus on general concepts encoded in the tags, rather than memorizing specific images.## Quick Start## Samples\n\nEach row of samples share the same generation prompt (string of tags). \n\n\n\n\ns"
]
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|
88a67121141e515dee27e6895998d7fc61bdf543 | The Tanakh dataset can be used for training and fine-tuning Biblical Hebrew language models. It consists of the vocalized text of the Hebrew Bible (excluding the Aramaic verses in Jeremiah 10:11; Daniel 2:4b–7:28; Ezra 4:8–6:18; 7:12-26) broken down by verse. The base text of each verse comes from the [Text-Fabric]( https://annotation.github.io/text-fabric/tf/) package and was subsequently processed in order remove accents and other paratextual markers as well as perform custom Unicode normalization using [hebrewtools]( https://pypi.org/project/hebrewtools/0.1.0/).
The script used to generate the Tanakh dataset can be found [here](https://github.com/gngpostalsrvc/BERiT/blob/main/input/generate_Tanakh_dataset.py).
| gngpostalsrvc/Tanakh | [
"region:us"
]
| 2023-03-03T01:04:00+00:00 | {} | 2023-04-05T19:24:21+00:00 | []
| []
| TAGS
#region-us
| The Tanakh dataset can be used for training and fine-tuning Biblical Hebrew language models. It consists of the vocalized text of the Hebrew Bible (excluding the Aramaic verses in Jeremiah 10:11; Daniel 2:4b–7:28; Ezra 4:8–6:18; 7:12-26) broken down by verse. The base text of each verse comes from the Text-Fabric package and was subsequently processed in order remove accents and other paratextual markers as well as perform custom Unicode normalization using hebrewtools.
The script used to generate the Tanakh dataset can be found here.
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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52e4297dce982af1cc8a6403f7c833d12304f075 | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
### Supported Tasks and Leaderboards
Multi-answer questioning, token classification
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
celex_id, input_ids, token_type_ids, attention_mask, labels
### Data Splits
validation samples
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
EURLEX dataset
#### 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
[More Information Needed]
### Contributions
[More Information Needed] | stuwang/QAmultilabelEURLEXsamples | [
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## Dataset Description
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- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
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English
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validation samples
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### Source Data
#### Initial Data Collection and Normalization
EURLEX dataset
#### 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
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|
e2ebcaffb348c18b8db25a3bba2ef91970f55f80 | # Dataset Card for "Zalo_Corpus_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | linhqyy/Zalo_Corpus_2 | [
"region:us"
]
| 2023-03-03T03:51:50+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11469054935.056, "num_examples": 56427}], "download_size": 11653394835, "dataset_size": 11469054935.056}} | 2023-03-03T04:01:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Zalo_Corpus_2"
More Information needed | [
"# Dataset Card for \"Zalo_Corpus_2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Zalo_Corpus_2\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Zalo_Corpus_2\"\n\nMore Information needed"
]
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|
b24f44bc269cdcf9c71034cfa232394962fcb31b | # Dataset Card for "babylm_wikipedia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sree1994/babylm_wikipedia | [
"region:us"
]
| 2023-03-03T03:54:02+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43740590, "num_examples": 178500}, {"name": "test", "num_bytes": 18923065, "num_examples": 76500}], "download_size": 43146728, "dataset_size": 62663655}} | 2023-03-03T04:09:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "babylm_wikipedia"
More Information needed | [
"# Dataset Card for \"babylm_wikipedia\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm_wikipedia\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm_wikipedia\"\n\nMore Information needed"
]
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|
5a6d761516f6d24b0107d8ab4fab6b849cbb6371 | # Dataset Card for "Zalo_Corpus_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | linhqyy/Zalo_Corpus_3 | [
"region:us"
]
| 2023-03-03T04:24:39+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10832798885.816, "num_examples": 98012}, {"name": "test", "num_bytes": 2682493085.088, "num_examples": 24503}], "download_size": 13425278612, "dataset_size": 13515291970.904}} | 2023-03-03T04:35:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Zalo_Corpus_3"
More Information needed | [
"# Dataset Card for \"Zalo_Corpus_3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Zalo_Corpus_3\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Zalo_Corpus_3\"\n\nMore Information needed"
]
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|
4d32ae5f6381442b21864568002541b65221feb5 |
# Dataset Card for youtube_subs_howto100M
## 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:** [HowTo100M homepage](https://www.di.ens.fr/willow/research/howto100m/)
- **Repository:** [HowTo100M repository](https://github.com/antoine77340/howto100m)
- **Paper:** [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://arxiv.org/abs/1906.03327)
### Dataset Summary
The `youtube_subs_howto100M` dataset is an English-language dataset of instruction-response pairs extracted from 309136 YouTube videos. The dataset was orignally inspired by and sourced from the HowTo100M dataset, which was developed for natural language search for video clips.
### Supported Tasks and Leaderboards
- `conversational`: The dataset can be used to train a model for instruction(request) and a long form of response generation. This dataset is originally prepared for the [Open Assistant](https://github.com/LAION-AI/Open-Assistant), which is an open-source chat-based large language model.
### Languages
Currently, all text in the dataset is in English.
## Dataset Structure
### Data Instances
A typical data point comprises an `instruction`, `response`, and a `source`
An example from the youtube_subs_howto100M looks as follows:
```
{"instruction": "Please explain how to remove plaque without going to the dentist 2016", "response": "mineral deposit on teeth is known as tartar or plaque as time passes by the amount of tartar increases and if you don't take care it can cause periodontitis of course the best way to remove tartar is paying a visit to your dentist but another way is to remove plaque at your home in this video you will learn how to remove plaque at home to do so you will need baking soda toothbrush salt you hydrogen peroxide cup you gentle pick you water anti septic mouthwash you step one first mix one tablespoon of bacon soda with TSP of salt into the cup after you at the toothbrush with warm water dip it into the mixture scrub teeth with an in spit continue the same process for five minutes step to mix a cup full with hydrogen peroxide with cup of warm water and rinse your mouth for one minute then spit and rinse with cup of cool water step 3 rub the yellow tartar from teeth with a dental pick be careful not to scrape the gums it may irritate and damage them step 4 rinse mouth with an antiseptic mouthwash and repeat every second day here are some other advice is to help you keep your beautiful smile tomatoes and strawberries tomatoes and strawberries are rich in vitamin C which is excellent for oral health you can rub these fruits directly onto your teeth and let it sit for five minutes this way the tartar buildup will soften cheese being a Swiss or cheddar before meals helps neutralize the acids that involve black creation an ingredient in a cheese works as a barrier agent guava both guava fruit and leaves are considered excellent anti black agents to help remove plaque accumulated on the teeth and gums gloss they have anti-inflammatory and analgesic properties that help reduce swelling and pain in the gums brush your teeth regularly with a soft brush and make vertical movements pay attention on the space between gums and teeth floss regularly consuming spicy food stimulates syllabary glands that way saliva cleans mouth in a natural way five bacteria with an orange peel before going to bed and don't rinse mouth", "source": "YouTube"}
```
### Data Fields
- `instruction`: a request for an explanation.
- `response`: a long text of response sentences, currently not punctuated.
- `source`: the source of the datapoint, currently all `YouTube`.
### Data Splits
The dataset does not have train/valid/eval splits now.
## Dataset Creation
### Curation Rationale
The original HowTo100M dataset was developed for natural language search for video clips, not necessarily for conversational or chat based training. However, the long monologue response can be regarded as a sequence of answers for a question, which can be induced from the video title. Therefore, a good amount of high-quality request-response(long) pairs can be extracted from HowTo100M youtube videos.
Concretely, this dataset is curated like below:
```
for each video in YouTube100M dataset
if video_title starts with `how to`
add `Please explain` to the title to make an `instruction`
extract subtitles from the video to make a `response`
```
### Source Data
#### Initial Data Collection and Normalization
Refer to the [Curation Rationale](#curation-rationale)
#### Who are the source language producers?
The language producers are YouTube users of the videos in HowTo100M dataset.
### Annotations
#### Annotation process
Refer to the [Curation Rationale](#curation-rationale)
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
Apache license 2.0
## Additional Information
### Dataset Curators
The youtube_subs_howto100M dataset was created by [@totuta](https://github.com/totuta). The original HowTo100M dataset was created by Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic.
### Licensing Information
[N/A]
### Citation Information
@inproceedings{miech19howto100m,
title={How{T}o100{M}: {L}earning a {T}ext-{V}ideo {E}mbedding by {W}atching {H}undred {M}illion {N}arrated {V}ideo {C}lips},
author={Miech, Antoine and Zhukov, Dimitri and Alayrac, Jean-Baptiste and Tapaswi, Makarand and Laptev, Ivan and Sivic, Josef},
booktitle={ICCV},
year={2019},
}
### Contributions
Thanks to [@totuta](https://github.com/totuta) for adding this dataset. | totuta/youtube_subs_howto100M | [
"task_categories:conversational",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"arxiv:1906.03327",
"region:us"
]
| 2023-03-03T04:56:10+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10M<n<100M"], "task_categories": ["conversational"], "pretty_name": "YouTube Subtitles of Instructions: HowTo100M", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1260882571, "num_examples": 309136}], "download_size": 668637627, "dataset_size": 1260882571}} | 2023-03-04T01:38:37+00:00 | [
"1906.03327"
]
| [
"en"
]
| TAGS
#task_categories-conversational #size_categories-10M<n<100M #language-English #license-apache-2.0 #arxiv-1906.03327 #region-us
|
# Dataset Card for youtube_subs_howto100M
## 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: HowTo100M homepage
- Repository: HowTo100M repository
- Paper: HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
### Dataset Summary
The 'youtube_subs_howto100M' dataset is an English-language dataset of instruction-response pairs extracted from 309136 YouTube videos. The dataset was orignally inspired by and sourced from the HowTo100M dataset, which was developed for natural language search for video clips.
### Supported Tasks and Leaderboards
- 'conversational': The dataset can be used to train a model for instruction(request) and a long form of response generation. This dataset is originally prepared for the Open Assistant, which is an open-source chat-based large language model.
### Languages
Currently, all text in the dataset is in English.
## Dataset Structure
### Data Instances
A typical data point comprises an 'instruction', 'response', and a 'source'
An example from the youtube_subs_howto100M looks as follows:
### Data Fields
- 'instruction': a request for an explanation.
- 'response': a long text of response sentences, currently not punctuated.
- 'source': the source of the datapoint, currently all 'YouTube'.
### Data Splits
The dataset does not have train/valid/eval splits now.
## Dataset Creation
### Curation Rationale
The original HowTo100M dataset was developed for natural language search for video clips, not necessarily for conversational or chat based training. However, the long monologue response can be regarded as a sequence of answers for a question, which can be induced from the video title. Therefore, a good amount of high-quality request-response(long) pairs can be extracted from HowTo100M youtube videos.
Concretely, this dataset is curated like below:
### Source Data
#### Initial Data Collection and Normalization
Refer to the Curation Rationale
#### Who are the source language producers?
The language producers are YouTube users of the videos in HowTo100M dataset.
### Annotations
#### Annotation process
Refer to the Curation Rationale
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
Apache license 2.0
## Additional Information
### Dataset Curators
The youtube_subs_howto100M dataset was created by @totuta. The original HowTo100M dataset was created by Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic.
### Licensing Information
[N/A]
@inproceedings{miech19howto100m,
title={How{T}o100{M}: {L}earning a {T}ext-{V}ideo {E}mbedding by {W}atching {H}undred {M}illion {N}arrated {V}ideo {C}lips},
author={Miech, Antoine and Zhukov, Dimitri and Alayrac, Jean-Baptiste and Tapaswi, Makarand and Laptev, Ivan and Sivic, Josef},
booktitle={ICCV},
year={2019},
}
### Contributions
Thanks to @totuta for adding this dataset. | [
"# Dataset Card for youtube_subs_howto100M",
"## Table of Contents\n- 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: HowTo100M homepage\n- Repository: HowTo100M repository\n- Paper: HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips",
"### Dataset Summary\n\nThe 'youtube_subs_howto100M' dataset is an English-language dataset of instruction-response pairs extracted from 309136 YouTube videos. The dataset was orignally inspired by and sourced from the HowTo100M dataset, which was developed for natural language search for video clips.",
"### Supported Tasks and Leaderboards\n\n- 'conversational': The dataset can be used to train a model for instruction(request) and a long form of response generation. This dataset is originally prepared for the Open Assistant, which is an open-source chat-based large language model.",
"### Languages\n\nCurrently, all text in the dataset is in English.",
"## Dataset Structure",
"### Data Instances\n\nA typical data point comprises an 'instruction', 'response', and a 'source'\n\nAn example from the youtube_subs_howto100M looks as follows:",
"### Data Fields\n\n- 'instruction': a request for an explanation.\n- 'response': a long text of response sentences, currently not punctuated.\n- 'source': the source of the datapoint, currently all 'YouTube'.",
"### Data Splits\n\nThe dataset does not have train/valid/eval splits now.",
"## Dataset Creation",
"### Curation Rationale\n\nThe original HowTo100M dataset was developed for natural language search for video clips, not necessarily for conversational or chat based training. However, the long monologue response can be regarded as a sequence of answers for a question, which can be induced from the video title. Therefore, a good amount of high-quality request-response(long) pairs can be extracted from HowTo100M youtube videos.\n\nConcretely, this dataset is curated like below:",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nRefer to the Curation Rationale",
"#### Who are the source language producers?\n\nThe language producers are YouTube users of the videos in HowTo100M dataset.",
"### Annotations",
"#### Annotation process\n\nRefer to the Curation Rationale",
"#### Who are the annotators?\n\n[N/A]",
"### Personal and Sensitive Information\n\n[N/A]",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\n[N/A]",
"### Discussion of Biases\n\n[N/A]",
"### Other Known Limitations\n\nApache license 2.0",
"## Additional Information",
"### Dataset Curators\n\nThe youtube_subs_howto100M dataset was created by @totuta. The original HowTo100M dataset was created by Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic.",
"### Licensing Information\n\n[N/A]\n\n\n\n@inproceedings{miech19howto100m,\n title={How{T}o100{M}: {L}earning a {T}ext-{V}ideo {E}mbedding by {W}atching {H}undred {M}illion {N}arrated {V}ideo {C}lips},\n author={Miech, Antoine and Zhukov, Dimitri and Alayrac, Jean-Baptiste and Tapaswi, Makarand and Laptev, Ivan and Sivic, Josef},\n booktitle={ICCV},\n year={2019},\n}",
"### Contributions\n\nThanks to @totuta for adding this dataset."
]
| [
"TAGS\n#task_categories-conversational #size_categories-10M<n<100M #language-English #license-apache-2.0 #arxiv-1906.03327 #region-us \n",
"# Dataset Card for youtube_subs_howto100M",
"## Table of Contents\n- 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: HowTo100M homepage\n- Repository: HowTo100M repository\n- Paper: HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips",
"### Dataset Summary\n\nThe 'youtube_subs_howto100M' dataset is an English-language dataset of instruction-response pairs extracted from 309136 YouTube videos. The dataset was orignally inspired by and sourced from the HowTo100M dataset, which was developed for natural language search for video clips.",
"### Supported Tasks and Leaderboards\n\n- 'conversational': The dataset can be used to train a model for instruction(request) and a long form of response generation. This dataset is originally prepared for the Open Assistant, which is an open-source chat-based large language model.",
"### Languages\n\nCurrently, all text in the dataset is in English.",
"## Dataset Structure",
"### Data Instances\n\nA typical data point comprises an 'instruction', 'response', and a 'source'\n\nAn example from the youtube_subs_howto100M looks as follows:",
"### Data Fields\n\n- 'instruction': a request for an explanation.\n- 'response': a long text of response sentences, currently not punctuated.\n- 'source': the source of the datapoint, currently all 'YouTube'.",
"### Data Splits\n\nThe dataset does not have train/valid/eval splits now.",
"## Dataset Creation",
"### Curation Rationale\n\nThe original HowTo100M dataset was developed for natural language search for video clips, not necessarily for conversational or chat based training. However, the long monologue response can be regarded as a sequence of answers for a question, which can be induced from the video title. Therefore, a good amount of high-quality request-response(long) pairs can be extracted from HowTo100M youtube videos.\n\nConcretely, this dataset is curated like below:",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nRefer to the Curation Rationale",
"#### Who are the source language producers?\n\nThe language producers are YouTube users of the videos in HowTo100M dataset.",
"### Annotations",
"#### Annotation process\n\nRefer to the Curation Rationale",
"#### Who are the annotators?\n\n[N/A]",
"### Personal and Sensitive Information\n\n[N/A]",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\n[N/A]",
"### Discussion of Biases\n\n[N/A]",
"### Other Known Limitations\n\nApache license 2.0",
"## Additional Information",
"### Dataset Curators\n\nThe youtube_subs_howto100M dataset was created by @totuta. The original HowTo100M dataset was created by Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic.",
"### Licensing Information\n\n[N/A]\n\n\n\n@inproceedings{miech19howto100m,\n title={How{T}o100{M}: {L}earning a {T}ext-{V}ideo {E}mbedding by {W}atching {H}undred {M}illion {N}arrated {V}ideo {C}lips},\n author={Miech, Antoine and Zhukov, Dimitri and Alayrac, Jean-Baptiste and Tapaswi, Makarand and Laptev, Ivan and Sivic, Josef},\n booktitle={ICCV},\n year={2019},\n}",
"### Contributions\n\nThanks to @totuta for adding this dataset."
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"passage: TAGS\n#task_categories-conversational #size_categories-10M<n<100M #language-English #license-apache-2.0 #arxiv-1906.03327 #region-us \n# Dataset Card for youtube_subs_howto100M## Table of Contents\n- 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: HowTo100M homepage\n- Repository: HowTo100M repository\n- Paper: HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips### Dataset Summary\n\nThe 'youtube_subs_howto100M' dataset is an English-language dataset of instruction-response pairs extracted from 309136 YouTube videos. The dataset was orignally inspired by and sourced from the HowTo100M dataset, which was developed for natural language search for video clips.### Supported Tasks and Leaderboards\n\n- 'conversational': The dataset can be used to train a model for instruction(request) and a long form of response generation. This dataset is originally prepared for the Open Assistant, which is an open-source chat-based large language model.### Languages\n\nCurrently, all text in the dataset is in English.## Dataset Structure### Data Instances\n\nA typical data point comprises an 'instruction', 'response', and a 'source'\n\nAn example from the youtube_subs_howto100M looks as follows:### Data Fields\n\n- 'instruction': a request for an explanation.\n- 'response': a long text of response sentences, currently not punctuated.\n- 'source': the source of the datapoint, currently all 'YouTube'."
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|
a4161f4436fd668026d7295df4f4d020f0a0b78f |
# Ziya-LLaMA-13B-v1
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- API: [Fengshen-OpenAPI](https://fengshenbang-lm.com/open-api)
# 姜子牙系列模型
- [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1)
- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward)
## 简介 Brief Introduction
姜子牙通用大模型V1是基于LLaMa的130亿参数的大规模预训练模型，具备翻译，编程，文本分类，信息抽取，摘要，文案生成，常识问答和数学计算等能力。目前姜子牙通用大模型已完成大规模预训练、多任务有监督微调和人类反馈学习三阶段的训练过程。
The Ziya-LLaMA-13B-v1 is a large-scale pre-trained model based on LLaMA with 13 billion parameters. It has the ability to perform tasks such as translation, programming, text classification, information extraction, summarization, copywriting, common sense Q&A, and mathematical calculation. The Ziya-LLaMA-13B-v1 has undergone three stages of training: large-scale continual pre-training (PT), multi-task supervised fine-tuning (SFT), and human feedback learning (RM, PPO).
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | AGI模型 | 姜子牙 Ziya | LLaMA | 13B | English&Chinese |
## 模型信息 Model Information
### 继续预训练 Continual pretraining
原始数据包含英文和中文，其中英文数据来自openwebtext、Books、Wikipedia和Code，中文数据来自清洗后的悟道数据集、自建的中文数据集。在对原始数据进行去重、模型打分、数据分桶、规则过滤、敏感主题过滤和数据评估后，最终得到125B tokens的有效数据。
为了解决LLaMA原生分词对中文编解码效率低下的问题，我们在LLaMA词表的基础上增加了8k个常见中文字，通过和LLaMA原生的词表去重，最终得到一个39410大小的词表，并通过复用Transformers里LlamaTokenizer来实现了这一效果。
在增量训练过程中，我们使用了160张40GB的A100，采用2.6M tokens的训练集样本数量和FP 16的混合精度，吞吐量达到118 TFLOP per GPU per second。因此我们能够在8天的时间里在原生的LLaMA-13B模型基础上，增量训练110B tokens的数据。
训练期间，虽然遇到了机器宕机、底层框架bug、loss spike等各种问题，但我们通过快速调整，保证了增量训练的稳定性。我们也放出训练过程的loss曲线，让大家了解可能出现的问题。
The original data contains both English and Chinese, with English data from openwebtext, Books, Wikipedia, and Code, and Chinese data from the cleaned Wudao dataset and self-built Chinese dataset. After deduplication, model scoring, data bucketing, rule filtering, sensitive topic filtering, and data evaluation, we finally obtained 125 billion tokens of valid data.
To address the issue of low efficiency in Chinese encoding and decoding caused by the native word segmentation of LLaMa, we added 8,000 commonly used Chinese characters to the LLaMa vocabulary. By removing duplicates with the original LLaMa vocabulary, we finally obtained a vocabulary of size 39,410. We achieved this by reusing the LlamaTokenizer in Transformers.
During the incremental training process, we used 160 A100s with a total of 40GB memory, using a training dataset with 2.6 million tokens and mixed precision of FP16. The throughput reached 118 TFLOP per GPU per second. As a result, we were able to incrementally train 110 billion tokens of data on top of the native LLaMa-13B model in just 8 days.
Throughout the training process, we encountered various issues such as machine crashes, underlying framework bugs, and loss spikes. However, we ensured the stability of the incremental training by making rapid adjustments. We have also released the loss curve during the training process to help everyone understand the potential issues that may arise.
<img src="https://wandb.ai/fengshenbang/llama2_13b_cpt_v1/reports/Untitled-Report--Vmlldzo0OTM3MjQ1" width=1000 height=600>
<iframe src="https://wandb.ai/fengshenbang/llama2_13b_cpt_v1/reports/Untitled-Report--Vmlldzo0OTM3MjQ1" style="border:none;height:1024px;width:100%">
### 多任务有监督微调 Supervised finetuning
在多任务有监督微调阶段，采用了课程学习（curiculum learning）和增量训练（continual learning）的策略，用大模型辅助划分已有的数据难度，然后通过“Easy To Hard”的方式，分多个阶段进行SFT训练。
SFT训练数据包含多个高质量的数据集，均经过人工筛选和校验：
- Self-Instruct构造的数据（约2M）：BELLE、Alpaca、Alpaca-GPT4等多个数据集
- 内部收集Code数据（300K）：包含leetcode、多种Code任务形式
- 内部收集推理/逻辑相关数据（500K）：推理、申论、数学应用题、数值计算等
- 中英平行语料（2M）：中英互译语料、COT类型翻译语料、古文翻译语料等
- 多轮对话语料（500K）：Self-Instruct生成、任务型多轮对话、Role-Playing型多轮对话等
During the supervised fine-tuning (SFT) phase of multitask learning, we used a strategy of curriculum learning and incremental training. We used the large model to assist in partitioning the existing data by difficulty and then conducted SFT training in multiple stages using the "easy to hard" approach.
The SFT training data consists of multiple high-quality datasets that have been manually selected and verified, including approximately 2 million samples from datasets such as BELLE, Alpaca, and Alpaca-GPT4, 300,000 samples of internally collected code data including LeetCode and various code tasks, 500,000 samples of internally collected inference/logic-related data such as reasoning, argumentative essays, mathematical application questions, and numerical calculations, 2 million samples of Chinese-English parallel corpora including translation, COT-type translation, and classical Chinese translation, and 500,000 samples of multi-turn dialogue corpora including self-instructed generation, task-oriented multi-turn dialogue, and role-playing multi-turn dialogue.
### 人类反馈学习 Human-Feedback training
为了进一步提升模型的综合表现，使其能够充分理解人类意图、减少“幻觉”和不安全的输出，基于指令微调后的模型，进行了人类反馈训练（Human-Feedback Training，HFT）。在训练中，我们采用了以人类反馈强化学习（RM、PPO）为主，结合多种其他手段联合训练的方法，手段包括人类反馈微调（Human-Feedback Fine-tuning，HFFT）、后见链微调（Chain-of-Hindsight Fine-tuning，COHFT）、AI反馈（AI Feedback）和基于规则的奖励系统（Rule-based Reward System，RBRS）等，用来弥补PPO方法的短板，加速训练。
我们在内部自研的框架上实现了HFT的训练流程，该框架可以利用最少8张40G的A100显卡完成Ziya-LLaMA-13B-v1的全参数训练。在PPO训练中，我们没有限制生成样本的长度，以确保长文本任务的奖励准确性。每次训练的总经验池尺寸超过100k样本，确保了训练的充分性。
To further improve the overall performance of the model, enabling it to fully understand human intentions, reduce "hallucinations" and unsafe outputs, we conducted Human-Feedback Training (HFT) based on the model fine-tuned with instructions. In the training process, we used a variety of methods, including human feedback reinforcement learning (RM, PPO), combined with other methods such as Human-Feedback Fine-tuning (HFFT), Chain-of-Hindsight Fine-tuning (COHFT), AI feedback, and Rule-based Reward System (RBRS), to supplement the shortcomings of the PPO method and accelerate training.
We implemented the HFT training process on an internally developed framework, which can use a minimum of 8 40GB A100 GPUs to complete the full parameter training of Ziya-LLaMA-13B-v1. In the PPO training, we did not limit the length of the generated samples to ensure the accuracy of rewards for long-text tasks. The total experience pool size for each training exceeded 100k samples, ensuring the sufficiency of the training.
### 效果评估 Performance
## 使用 Usage
```python3
from transformers import LlamaTokenizer
from transformers import LlamaForCausalLM
tokenizer = LlamaTokenizer.from_pretrained('IDEA-CCNL/Ziya-LLaMA-13B-v1')
model = LlamaForCausalLM.from_pretrained('IDEA-CCNL/Ziya-LLaMA-13B-v1')
model.eval()
prompt = '<human>:1+1=？\n<bot>:'
inputs = tokenizer(prompt, return_tensors="pt")
# Generate
generate_ids = model.generate(inputs.input_ids, max_length=128)
decode = tokenizer.batch_decode(generate_ids)[0]
print(decode)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
欢迎引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
``` | suolyer/testb | [
"language:en",
"language:zh",
"license:apache-2.0",
"arxiv:2210.08590",
"region:us"
]
| 2023-03-03T08:15:23+00:00 | {"language": ["en", "zh"], "license": "apache-2.0"} | 2023-07-21T08:09:26+00:00 | [
"2210.08590"
]
| [
"en",
"zh"
]
| TAGS
#language-English #language-Chinese #license-apache-2.0 #arxiv-2210.08590 #region-us
| Ziya-LLaMA-13B-v1
=================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
* API: Fengshen-OpenAPI
姜子牙系列模型
=======
* Ziya-LLaMA-13B-v1
* Ziya-LLaMA-7B-Reward
简介 Brief Introduction
---------------------
姜子牙通用大模型V1是基于LLaMa的130亿参数的大规模预训练模型，具备翻译，编程，文本分类，信息抽取，摘要，文案生成，常识问答和数学计算等能力。目前姜子牙通用大模型已完成大规模预训练、多任务有监督微调和人类反馈学习三阶段的训练过程。
The Ziya-LLaMA-13B-v1 is a large-scale pre-trained model based on LLaMA with 13 billion parameters. It has the ability to perform tasks such as translation, programming, text classification, information extraction, summarization, copywriting, common sense Q&A, and mathematical calculation. The Ziya-LLaMA-13B-v1 has undergone three stages of training: large-scale continual pre-training (PT), multi-task supervised fine-tuning (SFT), and human feedback learning (RM, PPO).
模型分类 Model Taxonomy
-------------------
模型信息 Model Information
----------------------
### 继续预训练 Continual pretraining
原始数据包含英文和中文，其中英文数据来自openwebtext、Books、Wikipedia和Code，中文数据来自清洗后的悟道数据集、自建的中文数据集。在对原始数据进行去重、模型打分、数据分桶、规则过滤、敏感主题过滤和数据评估后，最终得到125B tokens的有效数据。
为了解决LLaMA原生分词对中文编解码效率低下的问题，我们在LLaMA词表的基础上增加了8k个常见中文字，通过和LLaMA原生的词表去重，最终得到一个39410大小的词表，并通过复用Transformers里LlamaTokenizer来实现了这一效果。
在增量训练过程中，我们使用了160张40GB的A100，采用2.6M tokens的训练集样本数量和FP 16的混合精度，吞吐量达到118 TFLOP per GPU per second。因此我们能够在8天的时间里在原生的LLaMA-13B模型基础上，增量训练110B tokens的数据。
训练期间，虽然遇到了机器宕机、底层框架bug、loss spike等各种问题，但我们通过快速调整，保证了增量训练的稳定性。我们也放出训练过程的loss曲线，让大家了解可能出现的问题。
The original data contains both English and Chinese, with English data from openwebtext, Books, Wikipedia, and Code, and Chinese data from the cleaned Wudao dataset and self-built Chinese dataset. After deduplication, model scoring, data bucketing, rule filtering, sensitive topic filtering, and data evaluation, we finally obtained 125 billion tokens of valid data.
To address the issue of low efficiency in Chinese encoding and decoding caused by the native word segmentation of LLaMa, we added 8,000 commonly used Chinese characters to the LLaMa vocabulary. By removing duplicates with the original LLaMa vocabulary, we finally obtained a vocabulary of size 39,410. We achieved this by reusing the LlamaTokenizer in Transformers.
During the incremental training process, we used 160 A100s with a total of 40GB memory, using a training dataset with 2.6 million tokens and mixed precision of FP16. The throughput reached 118 TFLOP per GPU per second. As a result, we were able to incrementally train 110 billion tokens of data on top of the native LLaMa-13B model in just 8 days.
Throughout the training process, we encountered various issues such as machine crashes, underlying framework bugs, and loss spikes. However, we ensured the stability of the incremental training by making rapid adjustments. We have also released the loss curve during the training process to help everyone understand the potential issues that may arise.
<img src="URL width=1000 height=600>
### 多任务有监督微调 Supervised finetuning
在多任务有监督微调阶段，采用了课程学习（curiculum learning）和增量训练（continual learning）的策略，用大模型辅助划分已有的数据难度，然后通过“Easy To Hard”的方式，分多个阶段进行SFT训练。
SFT训练数据包含多个高质量的数据集，均经过人工筛选和校验：
* Self-Instruct构造的数据（约2M）：BELLE、Alpaca、Alpaca-GPT4等多个数据集
* 内部收集Code数据（300K）：包含leetcode、多种Code任务形式
* 内部收集推理/逻辑相关数据（500K）：推理、申论、数学应用题、数值计算等
* 中英平行语料（2M）：中英互译语料、COT类型翻译语料、古文翻译语料等
* 多轮对话语料（500K）：Self-Instruct生成、任务型多轮对话、Role-Playing型多轮对话等
During the supervised fine-tuning (SFT) phase of multitask learning, we used a strategy of curriculum learning and incremental training. We used the large model to assist in partitioning the existing data by difficulty and then conducted SFT training in multiple stages using the "easy to hard" approach.
The SFT training data consists of multiple high-quality datasets that have been manually selected and verified, including approximately 2 million samples from datasets such as BELLE, Alpaca, and Alpaca-GPT4, 300,000 samples of internally collected code data including LeetCode and various code tasks, 500,000 samples of internally collected inference/logic-related data such as reasoning, argumentative essays, mathematical application questions, and numerical calculations, 2 million samples of Chinese-English parallel corpora including translation, COT-type translation, and classical Chinese translation, and 500,000 samples of multi-turn dialogue corpora including self-instructed generation, task-oriented multi-turn dialogue, and role-playing multi-turn dialogue.
### 人类反馈学习 Human-Feedback training
为了进一步提升模型的综合表现，使其能够充分理解人类意图、减少“幻觉”和不安全的输出，基于指令微调后的模型，进行了人类反馈训练（Human-Feedback Training，HFT）。在训练中，我们采用了以人类反馈强化学习（RM、PPO）为主，结合多种其他手段联合训练的方法，手段包括人类反馈微调（Human-Feedback Fine-tuning，HFFT）、后见链微调（Chain-of-Hindsight Fine-tuning，COHFT）、AI反馈（AI Feedback）和基于规则的奖励系统（Rule-based Reward System，RBRS）等，用来弥补PPO方法的短板，加速训练。
我们在内部自研的框架上实现了HFT的训练流程，该框架可以利用最少8张40G的A100显卡完成Ziya-LLaMA-13B-v1的全参数训练。在PPO训练中，我们没有限制生成样本的长度，以确保长文本任务的奖励准确性。每次训练的总经验池尺寸超过100k样本，确保了训练的充分性。
To further improve the overall performance of the model, enabling it to fully understand human intentions, reduce "hallucinations" and unsafe outputs, we conducted Human-Feedback Training (HFT) based on the model fine-tuned with instructions. In the training process, we used a variety of methods, including human feedback reinforcement learning (RM, PPO), combined with other methods such as Human-Feedback Fine-tuning (HFFT), Chain-of-Hindsight Fine-tuning (COHFT), AI feedback, and Rule-based Reward System (RBRS), to supplement the shortcomings of the PPO method and accelerate training.
We implemented the HFT training process on an internally developed framework, which can use a minimum of 8 40GB A100 GPUs to complete the full parameter training of Ziya-LLaMA-13B-v1. In the PPO training, we did not limit the length of the generated samples to ensure the accuracy of rewards for long-text tasks. The total experience pool size for each training exceeded 100k samples, ensuring the sufficiency of the training.
### 效果评估 Performance
使用 Usage
--------
引用 Citation
-----------
如果您在您的工作中使用了我们的模型，可以引用我们的论文：
If you are using the resource for your work, please cite the our paper:
You can also cite our website:
欢迎引用我们的网站:
| [
"### 继续预训练 Continual pretraining\n\n\n原始数据包含英文和中文，其中英文数据来自openwebtext、Books、Wikipedia和Code，中文数据来自清洗后的悟道数据集、自建的中文数据集。在对原始数据进行去重、模型打分、数据分桶、规则过滤、敏感主题过滤和数据评估后，最终得到125B tokens的有效数据。\n\n\n为了解决LLaMA原生分词对中文编解码效率低下的问题，我们在LLaMA词表的基础上增加了8k个常见中文字，通过和LLaMA原生的词表去重，最终得到一个39410大小的词表，并通过复用Transformers里LlamaTokenizer来实现了这一效果。\n\n\n在增量训练过程中，我们使用了160张40GB的A100，采用2.6M tokens的训练集样本数量和FP 16的混合精度，吞吐量达到118 TFLOP per GPU per second。因此我们能够在8天的时间里在原生的LLaMA-13B模型基础上，增量训练110B tokens的数据。\n\n\n训练期间，虽然遇到了机器宕机、底层框架bug、loss spike等各种问题，但我们通过快速调整，保证了增量训练的稳定性。我们也放出训练过程的loss曲线，让大家了解可能出现的问题。\n\n\nThe original data contains both English and Chinese, with English data from openwebtext, Books, Wikipedia, and Code, and Chinese data from the cleaned Wudao dataset and self-built Chinese dataset. After deduplication, model scoring, data bucketing, rule filtering, sensitive topic filtering, and data evaluation, we finally obtained 125 billion tokens of valid data.\n\n\nTo address the issue of low efficiency in Chinese encoding and decoding caused by the native word segmentation of LLaMa, we added 8,000 commonly used Chinese characters to the LLaMa vocabulary. By removing duplicates with the original LLaMa vocabulary, we finally obtained a vocabulary of size 39,410. We achieved this by reusing the LlamaTokenizer in Transformers.\n\n\nDuring the incremental training process, we used 160 A100s with a total of 40GB memory, using a training dataset with 2.6 million tokens and mixed precision of FP16. The throughput reached 118 TFLOP per GPU per second. As a result, we were able to incrementally train 110 billion tokens of data on top of the native LLaMa-13B model in just 8 days.\n\n\nThroughout the training process, we encountered various issues such as machine crashes, underlying framework bugs, and loss spikes. However, we ensured the stability of the incremental training by making rapid adjustments. We have also released the loss curve during the training process to help everyone understand the potential issues that may arise.\n\n\n<img src=\"URL width=1000 height=600>",
"### 多任务有监督微调 Supervised finetuning\n\n\n在多任务有监督微调阶段，采用了课程学习（curiculum learning）和增量训练（continual learning）的策略，用大模型辅助划分已有的数据难度，然后通过“Easy To Hard”的方式，分多个阶段进行SFT训练。\n\n\nSFT训练数据包含多个高质量的数据集，均经过人工筛选和校验：\n\n\n* Self-Instruct构造的数据（约2M）：BELLE、Alpaca、Alpaca-GPT4等多个数据集\n* 内部收集Code数据（300K）：包含leetcode、多种Code任务形式\n* 内部收集推理/逻辑相关数据（500K）：推理、申论、数学应用题、数值计算等\n* 中英平行语料（2M）：中英互译语料、COT类型翻译语料、古文翻译语料等\n* 多轮对话语料（500K）：Self-Instruct生成、任务型多轮对话、Role-Playing型多轮对话等\n\n\nDuring the supervised fine-tuning (SFT) phase of multitask learning, we used a strategy of curriculum learning and incremental training. We used the large model to assist in partitioning the existing data by difficulty and then conducted SFT training in multiple stages using the \"easy to hard\" approach.\n\n\nThe SFT training data consists of multiple high-quality datasets that have been manually selected and verified, including approximately 2 million samples from datasets such as BELLE, Alpaca, and Alpaca-GPT4, 300,000 samples of internally collected code data including LeetCode and various code tasks, 500,000 samples of internally collected inference/logic-related data such as reasoning, argumentative essays, mathematical application questions, and numerical calculations, 2 million samples of Chinese-English parallel corpora including translation, COT-type translation, and classical Chinese translation, and 500,000 samples of multi-turn dialogue corpora including self-instructed generation, task-oriented multi-turn dialogue, and role-playing multi-turn dialogue.",
"### 人类反馈学习 Human-Feedback training\n\n\n为了进一步提升模型的综合表现，使其能够充分理解人类意图、减少“幻觉”和不安全的输出，基于指令微调后的模型，进行了人类反馈训练（Human-Feedback Training，HFT）。在训练中，我们采用了以人类反馈强化学习（RM、PPO）为主，结合多种其他手段联合训练的方法，手段包括人类反馈微调（Human-Feedback Fine-tuning，HFFT）、后见链微调（Chain-of-Hindsight Fine-tuning，COHFT）、AI反馈（AI Feedback）和基于规则的奖励系统（Rule-based Reward System，RBRS）等，用来弥补PPO方法的短板，加速训练。\n\n\n我们在内部自研的框架上实现了HFT的训练流程，该框架可以利用最少8张40G的A100显卡完成Ziya-LLaMA-13B-v1的全参数训练。在PPO训练中，我们没有限制生成样本的长度，以确保长文本任务的奖励准确性。每次训练的总经验池尺寸超过100k样本，确保了训练的充分性。\n\n\nTo further improve the overall performance of the model, enabling it to fully understand human intentions, reduce \"hallucinations\" and unsafe outputs, we conducted Human-Feedback Training (HFT) based on the model fine-tuned with instructions. In the training process, we used a variety of methods, including human feedback reinforcement learning (RM, PPO), combined with other methods such as Human-Feedback Fine-tuning (HFFT), Chain-of-Hindsight Fine-tuning (COHFT), AI feedback, and Rule-based Reward System (RBRS), to supplement the shortcomings of the PPO method and accelerate training.\n\n\nWe implemented the HFT training process on an internally developed framework, which can use a minimum of 8 40GB A100 GPUs to complete the full parameter training of Ziya-LLaMA-13B-v1. In the PPO training, we did not limit the length of the generated samples to ensure the accuracy of rewards for long-text tasks. The total experience pool size for each training exceeded 100k samples, ensuring the sufficiency of the training.",
"### 效果评估 Performance\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\nYou can also cite our website:\n\n\n欢迎引用我们的网站:"
]
| [
"TAGS\n#language-English #language-Chinese #license-apache-2.0 #arxiv-2210.08590 #region-us \n",
"### 继续预训练 Continual pretraining\n\n\n原始数据包含英文和中文，其中英文数据来自openwebtext、Books、Wikipedia和Code，中文数据来自清洗后的悟道数据集、自建的中文数据集。在对原始数据进行去重、模型打分、数据分桶、规则过滤、敏感主题过滤和数据评估后，最终得到125B tokens的有效数据。\n\n\n为了解决LLaMA原生分词对中文编解码效率低下的问题，我们在LLaMA词表的基础上增加了8k个常见中文字，通过和LLaMA原生的词表去重，最终得到一个39410大小的词表，并通过复用Transformers里LlamaTokenizer来实现了这一效果。\n\n\n在增量训练过程中，我们使用了160张40GB的A100，采用2.6M tokens的训练集样本数量和FP 16的混合精度，吞吐量达到118 TFLOP per GPU per second。因此我们能够在8天的时间里在原生的LLaMA-13B模型基础上，增量训练110B tokens的数据。\n\n\n训练期间，虽然遇到了机器宕机、底层框架bug、loss spike等各种问题，但我们通过快速调整，保证了增量训练的稳定性。我们也放出训练过程的loss曲线，让大家了解可能出现的问题。\n\n\nThe original data contains both English and Chinese, with English data from openwebtext, Books, Wikipedia, and Code, and Chinese data from the cleaned Wudao dataset and self-built Chinese dataset. After deduplication, model scoring, data bucketing, rule filtering, sensitive topic filtering, and data evaluation, we finally obtained 125 billion tokens of valid data.\n\n\nTo address the issue of low efficiency in Chinese encoding and decoding caused by the native word segmentation of LLaMa, we added 8,000 commonly used Chinese characters to the LLaMa vocabulary. By removing duplicates with the original LLaMa vocabulary, we finally obtained a vocabulary of size 39,410. We achieved this by reusing the LlamaTokenizer in Transformers.\n\n\nDuring the incremental training process, we used 160 A100s with a total of 40GB memory, using a training dataset with 2.6 million tokens and mixed precision of FP16. The throughput reached 118 TFLOP per GPU per second. As a result, we were able to incrementally train 110 billion tokens of data on top of the native LLaMa-13B model in just 8 days.\n\n\nThroughout the training process, we encountered various issues such as machine crashes, underlying framework bugs, and loss spikes. However, we ensured the stability of the incremental training by making rapid adjustments. We have also released the loss curve during the training process to help everyone understand the potential issues that may arise.\n\n\n<img src=\"URL width=1000 height=600>",
"### 多任务有监督微调 Supervised finetuning\n\n\n在多任务有监督微调阶段，采用了课程学习（curiculum learning）和增量训练（continual learning）的策略，用大模型辅助划分已有的数据难度，然后通过“Easy To Hard”的方式，分多个阶段进行SFT训练。\n\n\nSFT训练数据包含多个高质量的数据集，均经过人工筛选和校验：\n\n\n* Self-Instruct构造的数据（约2M）：BELLE、Alpaca、Alpaca-GPT4等多个数据集\n* 内部收集Code数据（300K）：包含leetcode、多种Code任务形式\n* 内部收集推理/逻辑相关数据（500K）：推理、申论、数学应用题、数值计算等\n* 中英平行语料（2M）：中英互译语料、COT类型翻译语料、古文翻译语料等\n* 多轮对话语料（500K）：Self-Instruct生成、任务型多轮对话、Role-Playing型多轮对话等\n\n\nDuring the supervised fine-tuning (SFT) phase of multitask learning, we used a strategy of curriculum learning and incremental training. We used the large model to assist in partitioning the existing data by difficulty and then conducted SFT training in multiple stages using the \"easy to hard\" approach.\n\n\nThe SFT training data consists of multiple high-quality datasets that have been manually selected and verified, including approximately 2 million samples from datasets such as BELLE, Alpaca, and Alpaca-GPT4, 300,000 samples of internally collected code data including LeetCode and various code tasks, 500,000 samples of internally collected inference/logic-related data such as reasoning, argumentative essays, mathematical application questions, and numerical calculations, 2 million samples of Chinese-English parallel corpora including translation, COT-type translation, and classical Chinese translation, and 500,000 samples of multi-turn dialogue corpora including self-instructed generation, task-oriented multi-turn dialogue, and role-playing multi-turn dialogue.",
"### 人类反馈学习 Human-Feedback training\n\n\n为了进一步提升模型的综合表现，使其能够充分理解人类意图、减少“幻觉”和不安全的输出，基于指令微调后的模型，进行了人类反馈训练（Human-Feedback Training，HFT）。在训练中，我们采用了以人类反馈强化学习（RM、PPO）为主，结合多种其他手段联合训练的方法，手段包括人类反馈微调（Human-Feedback Fine-tuning，HFFT）、后见链微调（Chain-of-Hindsight Fine-tuning，COHFT）、AI反馈（AI Feedback）和基于规则的奖励系统（Rule-based Reward System，RBRS）等，用来弥补PPO方法的短板，加速训练。\n\n\n我们在内部自研的框架上实现了HFT的训练流程，该框架可以利用最少8张40G的A100显卡完成Ziya-LLaMA-13B-v1的全参数训练。在PPO训练中，我们没有限制生成样本的长度，以确保长文本任务的奖励准确性。每次训练的总经验池尺寸超过100k样本，确保了训练的充分性。\n\n\nTo further improve the overall performance of the model, enabling it to fully understand human intentions, reduce \"hallucinations\" and unsafe outputs, we conducted Human-Feedback Training (HFT) based on the model fine-tuned with instructions. In the training process, we used a variety of methods, including human feedback reinforcement learning (RM, PPO), combined with other methods such as Human-Feedback Fine-tuning (HFFT), Chain-of-Hindsight Fine-tuning (COHFT), AI feedback, and Rule-based Reward System (RBRS), to supplement the shortcomings of the PPO method and accelerate training.\n\n\nWe implemented the HFT training process on an internally developed framework, which can use a minimum of 8 40GB A100 GPUs to complete the full parameter training of Ziya-LLaMA-13B-v1. In the PPO training, we did not limit the length of the generated samples to ensure the accuracy of rewards for long-text tasks. The total experience pool size for each training exceeded 100k samples, ensuring the sufficiency of the training.",
"### 效果评估 Performance\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\nYou can also cite our website:\n\n\n欢迎引用我们的网站:"
]
| [
32,
648,
484,
521,
62
]
| [
"passage: TAGS\n#language-English #language-Chinese #license-apache-2.0 #arxiv-2210.08590 #region-us \n",
"passage: ### 继续预训练 Continual pretraining\n\n\n原始数据包含英文和中文，其中英文数据来自openwebtext、Books、Wikipedia和Code，中文数据来自清洗后的悟道数据集、自建的中文数据集。在对原始数据进行去重、模型打分、数据分桶、规则过滤、敏感主题过滤和数据评估后，最终得到125B tokens的有效数据。\n\n\n为了解决LLaMA原生分词对中文编解码效率低下的问题，我们在LLaMA词表的基础上增加了8k个常见中文字，通过和LLaMA原生的词表去重，最终得到一个39410大小的词表，并通过复用Transformers里LlamaTokenizer来实现了这一效果。\n\n\n在增量训练过程中，我们使用了160张40GB的A100，采用2.6M tokens的训练集样本数量和FP 16的混合精度，吞吐量达到118 TFLOP per GPU per second。因此我们能够在8天的时间里在原生的LLaMA-13B模型基础上，增量训练110B tokens的数据。\n\n\n训练期间，虽然遇到了机器宕机、底层框架bug、loss spike等各种问题，但我们通过快速调整，保证了增量训练的稳定性。我们也放出训练过程的loss曲线，让大家了解可能出现的问题。\n\n\nThe original data contains both English and Chinese, with English data from openwebtext, Books, Wikipedia, and Code, and Chinese data from the cleaned Wudao dataset and self-built Chinese dataset. After deduplication, model scoring, data bucketing, rule filtering, sensitive topic filtering, and data evaluation, we finally obtained 125 billion tokens of valid data.\n\n\nTo address the issue of low efficiency in Chinese encoding and decoding caused by the native word segmentation of LLaMa, we added 8,000 commonly used Chinese characters to the LLaMa vocabulary. By removing duplicates with the original LLaMa vocabulary, we finally obtained a vocabulary of size 39,410. We achieved this by reusing the LlamaTokenizer in Transformers.\n\n\nDuring the incremental training process, we used 160 A100s with a total of 40GB memory, using a training dataset with 2.6 million tokens and mixed precision of FP16. The throughput reached 118 TFLOP per GPU per second. As a result, we were able to incrementally train 110 billion tokens of data on top of the native LLaMa-13B model in just 8 days.\n\n\nThroughout the training process, we encountered various issues such as machine crashes, underlying framework bugs, and loss spikes. However, we ensured the stability of the incremental training by making rapid adjustments. We have also released the loss curve during the training process to help everyone understand the potential issues that may arise.\n\n\n<img src=\"URL width=1000 height=600>### 多任务有监督微调 Supervised finetuning\n\n\n在多任务有监督微调阶段，采用了课程学习（curiculum learning）和增量训练（continual learning）的策略，用大模型辅助划分已有的数据难度，然后通过“Easy To Hard”的方式，分多个阶段进行SFT训练。\n\n\nSFT训练数据包含多个高质量的数据集，均经过人工筛选和校验：\n\n\n* Self-Instruct构造的数据（约2M）：BELLE、Alpaca、Alpaca-GPT4等多个数据集\n* 内部收集Code数据（300K）：包含leetcode、多种Code任务形式\n* 内部收集推理/逻辑相关数据（500K）：推理、申论、数学应用题、数值计算等\n* 中英平行语料（2M）：中英互译语料、COT类型翻译语料、古文翻译语料等\n* 多轮对话语料（500K）：Self-Instruct生成、任务型多轮对话、Role-Playing型多轮对话等\n\n\nDuring the supervised fine-tuning (SFT) phase of multitask learning, we used a strategy of curriculum learning and incremental training. We used the large model to assist in partitioning the existing data by difficulty and then conducted SFT training in multiple stages using the \"easy to hard\" approach.\n\n\nThe SFT training data consists of multiple high-quality datasets that have been manually selected and verified, including approximately 2 million samples from datasets such as BELLE, Alpaca, and Alpaca-GPT4, 300,000 samples of internally collected code data including LeetCode and various code tasks, 500,000 samples of internally collected inference/logic-related data such as reasoning, argumentative essays, mathematical application questions, and numerical calculations, 2 million samples of Chinese-English parallel corpora including translation, COT-type translation, and classical Chinese translation, and 500,000 samples of multi-turn dialogue corpora including self-instructed generation, task-oriented multi-turn dialogue, and role-playing multi-turn dialogue."
]
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|
1beb69cc23abe12032f6e7b597d6b18eba4a90a5 | This Huggingface dataset hosts the Allocentric Scene Perception benchmark (ASP).
We have three different versions of the benchmark:
- ASP_FixedSun
- ASP_RandomSunPerScene
- ASP_RandomSunPerSnapshot
which are distinguished by the position of the sun. ASP_FixedSun will have the sun fixed at (0,0,10), while ASP_RandomSunPerScene and ASP_RandomSunPerSnapshot have random sun positions for each scene and snapshot respectively.
Each file consists of 6000 scenes, with randomly arranged objects (10% of the scenes contain 1 object, 20% 2 objects, 30% 3 objects and 40% of the scenes contain 4 objects)
 | CYHSM/asp | [
"license:mit",
"region:us"
]
| 2023-03-03T09:36:23+00:00 | {"license": "mit"} | 2023-03-14T18:59:47+00:00 | []
| []
| TAGS
#license-mit #region-us
| This Huggingface dataset hosts the Allocentric Scene Perception benchmark (ASP).
We have three different versions of the benchmark:
- ASP_FixedSun
- ASP_RandomSunPerScene
- ASP_RandomSunPerSnapshot
which are distinguished by the position of the sun. ASP_FixedSun will have the sun fixed at (0,0,10), while ASP_RandomSunPerScene and ASP_RandomSunPerSnapshot have random sun positions for each scene and snapshot respectively.
Each file consists of 6000 scenes, with randomly arranged objects (10% of the scenes contain 1 object, 20% 2 objects, 30% 3 objects and 40% of the scenes contain 4 objects)
!asp_gif.gif | []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
]
| [
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|
47b6b283b699771fcf2e01e6595e62555bc10eb6 | # AutoTrain Dataset for project: test7-2644pc-linearregr
## Dataset Description
This dataset has been automatically processed by AutoTrain for project test7-2644pc-linearregr.
### 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
[
{
"feat_7800.00000002, 94.9238939621, 2441.53322511; 7800.00000000, 127.960861494, 1684.86226632; 7800.00000000, 261.682389404, 1712.25453458; 7800.00000000, 319.656077938, 1723.60243999; 7800.00000000, 565.026201539, 1769.40887871; 7800.00000000, 624.022787646, 1780.01755304; 7800.00000000, 735.227253344, 1799.67601898; 7800.00000000, 764.775277230, 1804.83636630; 7800.00000000, 862.570272591, 1821.70376322; 7800.00000000, 920.784971860, 1831.60977726; 7800.00000000, 1160.86119679, 1871.43407710; 7800.00000000, 1219.12830106, 1880.84760331; 7800.00000000, 1458.15808074, 1918.02878546; 7800.00000000, 1516.56547419, 1926.59499253; 7800.00000000, 1635.13187766, 1943.13091879; 7800.00000000, 1664.87088003, 1947.07950093; 7800.00000000, 1755.35964407, 1958.55749414; 7800.00000000, 1795.00000000, 2003.90933326; 7800.00000001, 1795.00000000, 2227.09792389; 7800.00000001, 1795.00000000, 2285.59792389; 7800.00000002, 1795.00000000, 2661.5; 7800.00000002, 1795.00000000, 2720.00000000; 7800.00000003, 1795.00000000, 2961.50000000; 7800.00000003, 1795.00000000, 3020.00000000; 7800.00000003, 1795.00000000, 3261.5; 7800.00000004, 1795.00000000, 3320.00000000; 7800.00000004, 1795.00000000, 3575.00000000; 7800.00000004, 1770.00000000, 3600.00000000; 7800.00000005, 765.0, 3600.00000000; 7800.00000005, 735.0, 3600.00000000; 7800.00000005, 667.000070530, 3600.00000000; 7800.00000005, 527.000070531, 3600.00000000; 7800.00000005, -527.000070531, 3600.00000000; 7800.00000005, -667.000070530, 3600.00000000; 7800.00000005, -735.000000000, 3600.00000000; 7800.00000005, -765.000000000, 3600.00000000; 7800.00000005, -1770.00000000, 3600.00000000; 7800.00000005, -1795.00000000, 3575.00000000; 7800.00000004, -1795.00000000, 3320.00000000; 7800.00000004, -1795.00000000, 3261.50000000; 7800.00000004, -1795.00000000, 3020.00000000; 7800.00000003, -1795.00000000, 2961.50000000; 7800.00000003, -1795.00000000, 2720.0; 7800.00000003, -1795.00000000, 2661.5; 7800.00000002, -1795.00000000, 2285.59792389; 7800.00000001, -1795.00000000, 2227.09792389; 7800.00000001, -1795.00000000, 2003.90933326; 7800.00000001, -1755.35964407, 1958.55749414; 7800.00000001, -1516.56547419, 1926.59499253; 7800.00000001, -1458.15808074, 1918.02878546; 7800.00000000, -1219.12830106, 1880.84760331; 7800.00000000, -1160.86119679, 1871.43407710; 7800.00000000, -920.784971860, 1831.60977726; 7800.00000000, -862.570272591, 1821.70376322; 7800.00000000, -764.775277230, 1804.83636630; 7800.00000000, -735.227253440, 1799.67601843; 7800.00000000, -624.022787646, 1780.01755247; 7800.00000000, -565.026201539, 1769.40887814; 7800.00000000, -319.656078024, 1723.60243939; 7800.00000000, -261.682389490, 1712.25453398; 7800.00000000, -127.960861526, 1684.86226558; 7800.00000002, -94.9238939616, 2441.53322511; 7805.00000002, 94.9238939621, 2441.53322511; 7805.00000000, 127.960861494, 1684.86226632; 7805.00000000, 261.682389404, 1712.25453458; 7805.00000000, 319.656077938, 1723.60243999; 7805.00000000, 565.026201539, 1769.40887871; 7805.00000000, 624.022787646, 1780.01755304; 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7805.00000005, 735.000000000, 3600.00000000; 7805.00000005, 667.000070530, 3600.00000000; 7805.00000005, 527.000070531, 3600.00000000; 7805.00000005, -527.000070531, 3600.00000000; 7805.00000005, -667.000070530, 3600.00000000; 7805.00000005, -735.000000000, 3600.00000000; 7805.00000005, -765.000000000, 3600.00000000; 7805.00000005, -1770.00000000, 3600.00000000; 7805.00000005, -1795.00000000, 3575.00000000; 7805.00000004, -1795.00000000, 3320.00000000; 7805.00000004, -1795.00000000, 3261.50000000; 7805.00000004, -1795.00000000, 3020.00000000; 7805.00000003, -1795.00000000, 2961.50000000; 7805.00000003, -1795.00000000, 2720.00000000; 7805.00000003, -1795.00000000, 2661.50000000; 7805.00000002, -1795.00000000, 2285.59792389; 7805.00000001, -1795.00000000, 2227.09792389; 7805.00000001, -1795.00000000, 2003.90933326; 7805.00000001, -1755.35964407, 1958.55749414; 7805.00000001, -1516.56547419, 1926.59499253; 7805.00000001, -1458.15808074, 1918.02878546; 7805.00000000, -1219.12830106, 1880.84760331; 7805.00000000, -1160.86119679, 1871.43407710; 7805.00000000, -920.784971859, 1831.60977726; 7805.00000000, -862.570272591, 1821.70376322; 7805.00000000, -764.775277230, 1804.83636630; 7805.00000000, -735.227253440, 1799.67601843; 7805.00000000, -624.022787646, 1780.01755247; 7805.00000000, -565.026201539, 1769.40887814; 7805.00000000, -319.656078024, 1723.60243939; 7805.00000000, -261.682389490, 1712.25453398; 7805.00000000, -127.960861526, 1684.86226558; 7805.00000002, -94.9238939616, 2441.53322511;": "7804.99999816, -4194.00011116, 3520.00000159; 7804.99999816, -4194.00007053, 3390.00000159; 7824.99999816, -4194.00007053, 3390.00000159; 7954.99999816, -4194.00007053, 3520.00000159; 7954.99999816, -4194.00007053, 3540.00000159; 7824.99999816, -4194.00007053, 3540.00000159; 7805.00000000, -4200.00011116, 3520.00000000; 7805.00000000, -4200.00007665, 3390.0; 7825.00000000, -4200.00007053, 3390.0; 7955.00000000, -4200.00006522, 3520.00000000; 7955.00000000, -4200.00007053, 3540.00000000; 7825.00000000, -4200.00011034, 3540.00000000;",
"target": 0.6000000238418579
},
{
"feat_7800.00000002, 94.9238939621, 2441.53322511; 7800.00000000, 127.960861494, 1684.86226632; 7800.00000000, 261.682389404, 1712.25453458; 7800.00000000, 319.656077938, 1723.60243999; 7800.00000000, 565.026201539, 1769.40887871; 7800.00000000, 624.022787646, 1780.01755304; 7800.00000000, 735.227253344, 1799.67601898; 7800.00000000, 764.775277230, 1804.83636630; 7800.00000000, 862.570272591, 1821.70376322; 7800.00000000, 920.784971860, 1831.60977726; 7800.00000000, 1160.86119679, 1871.43407710; 7800.00000000, 1219.12830106, 1880.84760331; 7800.00000000, 1458.15808074, 1918.02878546; 7800.00000000, 1516.56547419, 1926.59499253; 7800.00000000, 1635.13187766, 1943.13091879; 7800.00000000, 1664.87088003, 1947.07950093; 7800.00000000, 1755.35964407, 1958.55749414; 7800.00000000, 1795.00000000, 2003.90933326; 7800.00000001, 1795.00000000, 2227.09792389; 7800.00000001, 1795.00000000, 2285.59792389; 7800.00000002, 1795.00000000, 2661.5; 7800.00000002, 1795.00000000, 2720.00000000; 7800.00000003, 1795.00000000, 2961.50000000; 7800.00000003, 1795.00000000, 3020.00000000; 7800.00000003, 1795.00000000, 3261.5; 7800.00000004, 1795.00000000, 3320.00000000; 7800.00000004, 1795.00000000, 3575.00000000; 7800.00000004, 1770.00000000, 3600.00000000; 7800.00000005, 765.0, 3600.00000000; 7800.00000005, 735.0, 3600.00000000; 7800.00000005, 667.000070530, 3600.00000000; 7800.00000005, 527.000070531, 3600.00000000; 7800.00000005, -527.000070531, 3600.00000000; 7800.00000005, -667.000070530, 3600.00000000; 7800.00000005, -735.000000000, 3600.00000000; 7800.00000005, -765.000000000, 3600.00000000; 7800.00000005, -1770.00000000, 3600.00000000; 7800.00000005, -1795.00000000, 3575.00000000; 7800.00000004, -1795.00000000, 3320.00000000; 7800.00000004, -1795.00000000, 3261.50000000; 7800.00000004, -1795.00000000, 3020.00000000; 7800.00000003, -1795.00000000, 2961.50000000; 7800.00000003, -1795.00000000, 2720.0; 7800.00000003, -1795.00000000, 2661.5; 7800.00000002, -1795.00000000, 2285.59792389; 7800.00000001, -1795.00000000, 2227.09792389; 7800.00000001, -1795.00000000, 2003.90933326; 7800.00000001, -1755.35964407, 1958.55749414; 7800.00000001, -1516.56547419, 1926.59499253; 7800.00000001, -1458.15808074, 1918.02878546; 7800.00000000, -1219.12830106, 1880.84760331; 7800.00000000, -1160.86119679, 1871.43407710; 7800.00000000, -920.784971860, 1831.60977726; 7800.00000000, -862.570272591, 1821.70376322; 7800.00000000, -764.775277230, 1804.83636630; 7800.00000000, -735.227253440, 1799.67601843; 7800.00000000, -624.022787646, 1780.01755247; 7800.00000000, -565.026201539, 1769.40887814; 7800.00000000, -319.656078024, 1723.60243939; 7800.00000000, -261.682389490, 1712.25453398; 7800.00000000, -127.960861526, 1684.86226558; 7800.00000002, -94.9238939616, 2441.53322511; 7805.00000002, 94.9238939621, 2441.53322511; 7805.00000000, 127.960861494, 1684.86226632; 7805.00000000, 261.682389404, 1712.25453458; 7805.00000000, 319.656077938, 1723.60243999; 7805.00000000, 565.026201539, 1769.40887871; 7805.00000000, 624.022787646, 1780.01755304; 7805.00000000, 735.227253344, 1799.67601898; 7805.00000000, 764.775277230, 1804.83636630; 7805.00000000, 862.570272591, 1821.70376322; 7805.00000000, 920.784971860, 1831.60977726; 7805.00000000, 1160.86119679, 1871.43407710; 7805.00000000, 1219.12830106, 1880.84760331; 7805.00000000, 1458.15808074, 1918.02878546; 7805.00000000, 1516.56547419, 1926.59499253; 7805.00000000, 1635.13187766, 1943.13091879; 7805.00000000, 1664.87088003, 1947.07950093; 7805.00000000, 1755.35964407, 1958.55749414; 7805.00000000, 1795.00000000, 2003.90933326; 7805.00000001, 1795.00000000, 2227.09792389; 7805.00000001, 1795.00000000, 2285.59792389; 7805.00000002, 1795.00000000, 2661.50000000; 7805.00000002, 1795.00000000, 2720.00000000; 7805.00000003, 1795.00000000, 2961.50000000; 7805.00000003, 1795.00000000, 3020.00000000; 7805.00000003, 1795.00000000, 3261.50000000; 7805.00000004, 1795.00000000, 3320.00000000; 7805.00000004, 1795.00000000, 3575.00000000; 7805.00000004, 1770.00000000, 3600.00000000; 7805.00000005, 765.000000000, 3600.00000000; 7805.00000005, 735.000000000, 3600.00000000; 7805.00000005, 667.000070530, 3600.00000000; 7805.00000005, 527.000070531, 3600.00000000; 7805.00000005, -527.000070531, 3600.00000000; 7805.00000005, -667.000070530, 3600.00000000; 7805.00000005, -735.000000000, 3600.00000000; 7805.00000005, -765.000000000, 3600.00000000; 7805.00000005, -1770.00000000, 3600.00000000; 7805.00000005, -1795.00000000, 3575.00000000; 7805.00000004, -1795.00000000, 3320.00000000; 7805.00000004, -1795.00000000, 3261.50000000; 7805.00000004, -1795.00000000, 3020.00000000; 7805.00000003, -1795.00000000, 2961.50000000; 7805.00000003, -1795.00000000, 2720.00000000; 7805.00000003, -1795.00000000, 2661.50000000; 7805.00000002, -1795.00000000, 2285.59792389; 7805.00000001, -1795.00000000, 2227.09792389; 7805.00000001, -1795.00000000, 2003.90933326; 7805.00000001, -1755.35964407, 1958.55749414; 7805.00000001, -1516.56547419, 1926.59499253; 7805.00000001, -1458.15808074, 1918.02878546; 7805.00000000, -1219.12830106, 1880.84760331; 7805.00000000, -1160.86119679, 1871.43407710; 7805.00000000, -920.784971859, 1831.60977726; 7805.00000000, -862.570272591, 1821.70376322; 7805.00000000, -764.775277230, 1804.83636630; 7805.00000000, -735.227253440, 1799.67601843; 7805.00000000, -624.022787646, 1780.01755247; 7805.00000000, -565.026201539, 1769.40887814; 7805.00000000, -319.656078024, 1723.60243939; 7805.00000000, -261.682389490, 1712.25453398; 7805.00000000, -127.960861526, 1684.86226558; 7805.00000002, -94.9238939616, 2441.53322511;": "18300.0000003, 5397.38734591, 1375.21804970; 18300.0000003, 5110.91654501, 1253.74141186; 18300.0000003, 5086.16013667, 1267.14799650; 18300.0000003, 5069.24096063, 1325.02019760; 18300.0000003, 5382.43313234, 1435.59267926; 18300.0000003, 5403.61548643, 1402.54333077; 18305.0000003, 5397.38734591, 1375.21804970; 18305.0000003, 5110.91654501, 1253.74141186; 18305.0000003, 5086.16013667, 1267.14799650; 18305.0000003, 5069.24096063, 1325.02019760; 18305.0000003, 5382.43313234, 1435.59267926; 18305.0000003, 5403.61548643, 1402.54333077;",
"target": 0.5
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
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"target": "Value(dtype='float32', 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 | 2113 |
| valid | 530 |
| farouk97/autotrain-data-test7-2644pc-linearregr | [
"region:us"
]
| 2023-03-03T09:41:33+00:00 | {} | 2023-03-03T09:41:53+00:00 | []
| []
| TAGS
#region-us
| AutoTrain Dataset for project: test7-2644pc-linearregr
======================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project test7-2644pc-linearregr.
### 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#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:"
]
| [
6,
27,
17,
23,
27
]
| [
"passage: TAGS\n#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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|
ae2e0b22166b6369dd67e516f751b05cbf314e9b |
# Dataset Card for Helpful Instructions
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact: Lewis Tunstall**
### Dataset Summary
Helpful Instructions is a dataset of `(instruction, completion)` pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:
```python
from datasets import load_dataset
# Load all subsets
helpful_instructions = load_dataset("HuggingFaceH4/helpful_instructions", name="all")
# Load a single subset
helpful_instructions_subset = load_dataset("HuggingFaceH4/helpful_instructions", name="self_instruct")
```
### Supported Tasks and Leaderboards
This dataset can be used to fine-tune pretrained language models to follow instructions.
### Changelog
* March 5, 2023: `v1.0.0` release, with subsets from `HuggingFaceH4/self_instruct` (`self_instruct`, `super_natural_instructions`, `prompt_source`) | HuggingFaceH4/helpful_instructions | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"instruct",
"human-feedback",
"region:us"
]
| 2023-03-03T10:08:01+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "Helpful Instructions", "tags": ["instruct", "human-feedback"], "dataset_info": [{"config_name": "self_instruct", "features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "meta", "struct": [{"name": "source", "dtype": "string"}, {"name": "config", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 24378246, "num_examples": 82612}], "download_size": 12589487, "dataset_size": 24378246}, {"config_name": "super_natural_instructions", "features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "meta", "struct": [{"name": "source", "dtype": "string"}, {"name": "config", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 43352923, "num_examples": 50000}], "download_size": 22605900, "dataset_size": 43352923}, {"config_name": "prompt_source", "features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "meta", "struct": [{"name": "source", "dtype": "string"}, {"name": "config", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 59843768, "num_examples": 52657}], "download_size": 23607134, "dataset_size": 59843768}, {"config_name": "all", "features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "meta", "struct": [{"name": "source", "dtype": "string"}, {"name": "config", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 127574937, "num_examples": 185269}], "download_size": 58901460, "dataset_size": 127574937}]} | 2023-03-27T21:25:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #language-English #license-apache-2.0 #instruct #human-feedback #region-us
|
# Dataset Card for Helpful Instructions
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: Lewis Tunstall
### Dataset Summary
Helpful Instructions is a dataset of '(instruction, completion)' pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:
### Supported Tasks and Leaderboards
This dataset can be used to fine-tune pretrained language models to follow instructions.
### Changelog
* March 5, 2023: 'v1.0.0' release, with subsets from 'HuggingFaceH4/self_instruct' ('self_instruct', 'super_natural_instructions', 'prompt_source') | [
"# Dataset Card for Helpful Instructions",
"## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact: Lewis Tunstall",
"### Dataset Summary\n\nHelpful Instructions is a dataset of '(instruction, completion)' pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are \"helpful\", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:",
"### Supported Tasks and Leaderboards\n\nThis dataset can be used to fine-tune pretrained language models to follow instructions.",
"### Changelog\n\n* March 5, 2023: 'v1.0.0' release, with subsets from 'HuggingFaceH4/self_instruct' ('self_instruct', 'super_natural_instructions', 'prompt_source')"
]
| [
"TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #instruct #human-feedback #region-us \n",
"# Dataset Card for Helpful Instructions",
"## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact: Lewis Tunstall",
"### Dataset Summary\n\nHelpful Instructions is a dataset of '(instruction, completion)' pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are \"helpful\", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:",
"### Supported Tasks and Leaderboards\n\nThis dataset can be used to fine-tune pretrained language models to follow instructions.",
"### Changelog\n\n* March 5, 2023: 'v1.0.0' release, with subsets from 'HuggingFaceH4/self_instruct' ('self_instruct', 'super_natural_instructions', 'prompt_source')"
]
| [
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"passage: TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #instruct #human-feedback #region-us \n# Dataset Card for Helpful Instructions## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact: Lewis Tunstall### Dataset Summary\n\nHelpful Instructions is a dataset of '(instruction, completion)' pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are \"helpful\", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:### Supported Tasks and Leaderboards\n\nThis dataset can be used to fine-tune pretrained language models to follow instructions.### Changelog\n\n* March 5, 2023: 'v1.0.0' release, with subsets from 'HuggingFaceH4/self_instruct' ('self_instruct', 'super_natural_instructions', 'prompt_source')"
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|
bc37848adb9141e3fadc3e7f2cbf6bf9ddc749b4 | # Dataset Card for Pashto Corpus
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### 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
[More Information Needed]
### Contributions
[More Information Needed] | ijazulhaq/pashto_corpus | [
"size_categories:1M<n<10M",
"language:ps",
"license:mit",
"pashto corpus",
"pashto pos tagging",
"pashto word segmentation",
"pashto tokenization",
"pashto ner",
"sentiment analysis in pashto",
"region:us"
]
| 2023-03-03T10:11:21+00:00 | {"language": ["ps"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "The Pashto Corpus", "tags": ["pashto corpus", "pashto pos tagging", "pashto word segmentation", "pashto tokenization", "pashto ner", "sentiment analysis in pashto"]} | 2023-03-03T10:19:42+00:00 | []
| [
"ps"
]
| TAGS
#size_categories-1M<n<10M #language-Pushto #license-mit #pashto corpus #pashto pos tagging #pashto word segmentation #pashto tokenization #pashto ner #sentiment analysis in pashto #region-us
| # Dataset Card for Pashto Corpus
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### 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
### Contributions
| [
"# Dataset Card for Pashto Corpus",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### 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",
"### Contributions"
]
| [
"TAGS\n#size_categories-1M<n<10M #language-Pushto #license-mit #pashto corpus #pashto pos tagging #pashto word segmentation #pashto tokenization #pashto ner #sentiment analysis in pashto #region-us \n",
"# Dataset Card for Pashto Corpus",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### 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",
"### Contributions"
]
| [
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"passage: TAGS\n#size_categories-1M<n<10M #language-Pushto #license-mit #pashto corpus #pashto pos tagging #pashto word segmentation #pashto tokenization #pashto ner #sentiment analysis in pashto #region-us \n# Dataset Card for Pashto Corpus## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### 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### Contributions"
]
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|
ab955274dc4e9754874f5092608294b920f6f7e6 |
# Dataset Card for "DACSA"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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
- **Paper:** [DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles](https://aclanthology.org/2022.naacl-main.434/)
- **Point of Contact:** [Vicent Ahuir](mailto:[email protected])
### Dataset Summary
The Dataset for Automatic summarization of Catalan and Spanish newspaper
Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be
used to train summarization models for Catalan and Spanish. The data provides
pairs of news article and its summary from different newspapers for both, the
Catalan and the Spanish languages. Regarding the Catalan set, there are 725,184
sample pairs from 9 newspapers, regarding the Spanish set, the corpus provides
2,120,649 sample pairs from 21 newspapers.
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/xl-sum)
### Languages
- `catalan`
- `spanish`
## Dataset Structure
### Data Fields
- 'id': A string representing the article ID.
- 'summary': A string containing the article summary.
- 'article' : A string containing the article text.
### Data Splits
Four splits are provided for each language set
- **train**: samples for training models
- **validation**: samples for adjusting and validating models
- **test.i**: test samples from newspapers present in _train_ and _validation_ splits
- **test.ni**: test samples from newspapers not present in training and validation splits
The _validation_ and _test-i_ splits contain a uniform distribution of samples
from each newspaper source.
Languages | ISO 639-1 Code | Train | Val | Test.i | Test.ni | Total |
--------------|----------------|---------|-------|--------|---------|---------|
Catalan | ca | 636596 | 35376 | 35376 | 17836 | 725184 |
Spanish | es | 1802919 | 104052 | 104052 | 109626 | 2120649 |
## Dataset Creation
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/xl-sum)
### Source Data
Newspapers from Spain that publish news in Catalan or Spanish
#### Initial Data Collection and Normalization
[Detailed in the paper](https://aclanthology.org/2022.naacl-main.434/)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://aclanthology.org/2022.naacl-main.434/)
### Discussion of Biases
[More information needed](https://aclanthology.org/2022.naacl-main.434/)
### Other Known Limitations
[More information needed](https://aclanthology.org/2022.naacl-main.434/)
## Additional Information
### Dataset Curators
[More information needed](https://aclanthology.org/2022.naacl-main.434/)
### Licensing Information
These data are released under this licensing scheme.
We do not own any of the text from which these data has been extracted.
This DACSA dataset package is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
Should you consider that our data contains material that is owned by you
and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address,
telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and
information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources
from the next release of the corpus.
### Citation Information
If you use any of the datasets, models or code modules, please cite the following paper:
```
@inproceedings{segarra-soriano-etal-2022-dacsa,
title = "{DACSA}: A large-scale Dataset for Automatic summarization of {C}atalan and {S}panish newspaper Articles",
author = "Segarra Soriano, Encarnaci{\'o}n and
Ahuir, Vicent and
Hurtado, Llu{\'\i}s-F. and
Gonz{\'a}lez, Jos{\'e}",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.434",
doi = "10.18653/v1/2022.naacl-main.434",
pages = "5931--5943",
abstract = "The application of supervised methods to automatic summarization requires the availability of adequate corpora consisting of a set of document-summary pairs. As in most Natural Language Processing tasks, the great majority of available datasets for summarization are in English, making it difficult to develop automatic summarization models for other languages. Although Spanish is gradually forming part of some recent summarization corpora, it is not the same for minority languages such as Catalan.In this work, we describe the construction of a corpus of Catalan and Spanish newspapers, the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be used to train summarization models for Catalan and Spanish.We have carried out an analysis of the corpus, both in terms of the style of the summaries and the difficulty of the summarization task. In particular, we have used a set of well-known metrics in the summarization field in order to characterize the corpus. Additionally, for benchmarking purposes, we have evaluated the performances of some extractive and abstractive summarization systems on the DACSA corpus.",
}
```
| ELiRF/dacsa | [
"task_categories:text2text-generation",
"task_ids:news-articles-summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:es",
"license:odbl",
"region:us"
]
| 2023-03-03T10:16:33+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ca", "es"], "license": ["odbl"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": ["news-articles-summarization"], "paperswithcode_id": "dacsa", "pretty_name": "DACSA"} | 2023-03-25T09:58:52+00:00 | []
| [
"ca",
"es"
]
| TAGS
#task_categories-text2text-generation #task_ids-news-articles-summarization #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Catalan #language-Spanish #license-odbl #region-us
| Dataset Card for "DACSA"
========================
Table of Contents
-----------------
* Dataset Card Creation Guide
+ 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
* 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
- Citation Information
- Contributions
Dataset Description
-------------------
* Paper: DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles
* Point of Contact: Vicent Ahuir
### Dataset Summary
The Dataset for Automatic summarization of Catalan and Spanish newspaper
Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be
used to train summarization models for Catalan and Spanish. The data provides
pairs of news article and its summary from different newspapers for both, the
Catalan and the Spanish languages. Regarding the Catalan set, there are 725,184
sample pairs from 9 newspapers, regarding the Spanish set, the corpus provides
2,120,649 sample pairs from 21 newspapers.
### Supported Tasks and Leaderboards
More information needed
### Languages
* 'catalan'
* 'spanish'
Dataset Structure
-----------------
### Data Fields
* 'id': A string representing the article ID.
* 'summary': A string containing the article summary.
* 'article' : A string containing the article text.
### Data Splits
Four splits are provided for each language set
* train: samples for training models
* validation: samples for adjusting and validating models
* test.i: test samples from newspapers present in *train* and *validation* splits
* URL: test samples from newspapers not present in training and validation splits
The *validation* and *test-i* splits contain a uniform distribution of samples
from each newspaper source.
Dataset Creation
----------------
### Curation Rationale
More information needed
### Source Data
Newspapers from Spain that publish news in Catalan or Spanish
#### Initial Data Collection and Normalization
Detailed in the paper
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
These data are released under this licensing scheme.
We do not own any of the text from which these data has been extracted.
This DACSA dataset package is made available under the Open Database License: URL Any rights in individual contents of the database are licensed under the Database Contents License: URL
Should you consider that our data contains material that is owned by you
and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address,
telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and
information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources
from the next release of the corpus.
If you use any of the datasets, models or code modules, please cite the following paper:
| [
"### Dataset Summary\n\n\nThe Dataset for Automatic summarization of Catalan and Spanish newspaper\nArticles (DACSA) corpus. It is a high-quality large-scale corpus that can be\nused to train summarization models for Catalan and Spanish. The data provides\npairs of news article and its summary from different newspapers for both, the\nCatalan and the Spanish languages. Regarding the Catalan set, there are 725,184\nsample pairs from 9 newspapers, regarding the Spanish set, the corpus provides\n2,120,649 sample pairs from 21 newspapers.",
"### Supported Tasks and Leaderboards\n\n\nMore information needed",
"### Languages\n\n\n* 'catalan'\n* 'spanish'\n\n\nDataset Structure\n-----------------",
"### Data Fields\n\n\n* 'id': A string representing the article ID.\n* 'summary': A string containing the article summary.\n* 'article' : A string containing the article text.",
"### Data Splits\n\n\nFour splits are provided for each language set\n\n\n* train: samples for training models\n* validation: samples for adjusting and validating models\n* test.i: test samples from newspapers present in *train* and *validation* splits\n* URL: test samples from newspapers not present in training and validation splits\n\n\nThe *validation* and *test-i* splits contain a uniform distribution of samples\nfrom each newspaper source.\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nMore information needed",
"### Source Data\n\n\nNewspapers from Spain that publish news in Catalan or Spanish",
"#### Initial Data Collection and Normalization\n\n\nDetailed in the paper\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nMore information needed",
"### Discussion of Biases\n\n\nMore information needed",
"### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nMore information needed",
"### Licensing Information\n\n\nThese data are released under this licensing scheme.\nWe do not own any of the text from which these data has been extracted.\n\n\nThis DACSA dataset package is made available under the Open Database License: URL Any rights in individual contents of the database are licensed under the Database Contents License: URL\n\n\nShould you consider that our data contains material that is owned by you\nand should therefore not be reproduced here, please:\n\n\n* Clearly identify yourself, with detailed contact data such as an address,\ntelephone number or email address at which you can be contacted.\n* Clearly identify the copyrighted work claimed to be infringed.\n* Clearly identify the material that is claimed to be infringing and\ninformation reasonably sufficient to allow us to locate the material.\nWe will comply to legitimate requests by removing the affected sources\nfrom the next release of the corpus.\n\n\nIf you use any of the datasets, models or code modules, please cite the following paper:"
]
| [
"TAGS\n#task_categories-text2text-generation #task_ids-news-articles-summarization #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Catalan #language-Spanish #license-odbl #region-us \n",
"### Dataset Summary\n\n\nThe Dataset for Automatic summarization of Catalan and Spanish newspaper\nArticles (DACSA) corpus. It is a high-quality large-scale corpus that can be\nused to train summarization models for Catalan and Spanish. The data provides\npairs of news article and its summary from different newspapers for both, the\nCatalan and the Spanish languages. Regarding the Catalan set, there are 725,184\nsample pairs from 9 newspapers, regarding the Spanish set, the corpus provides\n2,120,649 sample pairs from 21 newspapers.",
"### Supported Tasks and Leaderboards\n\n\nMore information needed",
"### Languages\n\n\n* 'catalan'\n* 'spanish'\n\n\nDataset Structure\n-----------------",
"### Data Fields\n\n\n* 'id': A string representing the article ID.\n* 'summary': A string containing the article summary.\n* 'article' : A string containing the article text.",
"### Data Splits\n\n\nFour splits are provided for each language set\n\n\n* train: samples for training models\n* validation: samples for adjusting and validating models\n* test.i: test samples from newspapers present in *train* and *validation* splits\n* URL: test samples from newspapers not present in training and validation splits\n\n\nThe *validation* and *test-i* splits contain a uniform distribution of samples\nfrom each newspaper source.\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nMore information needed",
"### Source Data\n\n\nNewspapers from Spain that publish news in Catalan or Spanish",
"#### Initial Data Collection and Normalization\n\n\nDetailed in the paper\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nMore information needed",
"### Discussion of Biases\n\n\nMore information needed",
"### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nMore information needed",
"### Licensing Information\n\n\nThese data are released under this licensing scheme.\nWe do not own any of the text from which these data has been extracted.\n\n\nThis DACSA dataset package is made available under the Open Database License: URL Any rights in individual contents of the database are licensed under the Database Contents License: URL\n\n\nShould you consider that our data contains material that is owned by you\nand should therefore not be reproduced here, please:\n\n\n* Clearly identify yourself, with detailed contact data such as an address,\ntelephone number or email address at which you can be contacted.\n* Clearly identify the copyrighted work claimed to be infringed.\n* Clearly identify the material that is claimed to be infringing and\ninformation reasonably sufficient to allow us to locate the material.\nWe will comply to legitimate requests by removing the affected sources\nfrom the next release of the corpus.\n\n\nIf you use any of the datasets, models or code modules, please cite the following paper:"
]
| [
95,
122,
13,
21,
45,
110,
10,
15,
25,
10,
11,
17,
9,
216
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| [
"passage: TAGS\n#task_categories-text2text-generation #task_ids-news-articles-summarization #annotations_creators-found #language_creators-found #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Catalan #language-Spanish #license-odbl #region-us \n### Dataset Summary\n\n\nThe Dataset for Automatic summarization of Catalan and Spanish newspaper\nArticles (DACSA) corpus. It is a high-quality large-scale corpus that can be\nused to train summarization models for Catalan and Spanish. The data provides\npairs of news article and its summary from different newspapers for both, the\nCatalan and the Spanish languages. Regarding the Catalan set, there are 725,184\nsample pairs from 9 newspapers, regarding the Spanish set, the corpus provides\n2,120,649 sample pairs from 21 newspapers.### Supported Tasks and Leaderboards\n\n\nMore information needed### Languages\n\n\n* 'catalan'\n* 'spanish'\n\n\nDataset Structure\n-----------------### Data Fields\n\n\n* 'id': A string representing the article ID.\n* 'summary': A string containing the article summary.\n* 'article' : A string containing the article text.### Data Splits\n\n\nFour splits are provided for each language set\n\n\n* train: samples for training models\n* validation: samples for adjusting and validating models\n* test.i: test samples from newspapers present in *train* and *validation* splits\n* URL: test samples from newspapers not present in training and validation splits\n\n\nThe *validation* and *test-i* splits contain a uniform distribution of samples\nfrom each newspaper source.\n\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nMore information needed### Source Data\n\n\nNewspapers from Spain that publish news in Catalan or Spanish#### Initial Data Collection and Normalization\n\n\nDetailed in the paper\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nMore information needed### Discussion of Biases\n\n\nMore information needed### Other Known Limitations\n\n\nMore information needed\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\nMore information needed"
]
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|
51e0d66f7d87ef556d868270c0d68596b56149ba |
# Dataset Card for RixVox
## Dataset Description
- **Repository:** [Riksdagen anföranden repository](https://github.com/kb-labb/riksdagen_anforanden)
- **Paper:** ["RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates"](https://kb-labb.github.io/posts/2023-03-09-rixvox-a-swedish-speech-corpus/)
- **Point of Contact:** [KBLab](mailto:[email protected])
- **Total amount of disk used:** ca. 1.2 TB
### Dataset Summary
RixVox is a speech dataset comprised of speeches from the Riksdag (the Swedish Parliament). It covers speeches from debates during the period 2003-2023. Audio from speeches have been aligned, on the sentence level, with transcripts from written protocols using `aeneas`. An observation may consist of one or several concatenated sentences (up to 30 seconds in duration). Detailed speaker metadata is available for each observation, including the speaker's name, gender, political party, birth year and the electoral district they represent. The dataset contains a total of 5493 hours of speech with transcriptions.
## How to use & Supported Tasks
### Supported Tasks
Tasks are not supported by default (there are no label fields). The dataset may however be suited for:
- Automatic Speech Recognition (ASR).
- Speaker identification and verification.
- Creation of synthetic diarization datasets.
- Research on bias in ASR systems.
### How to use
To download and extract the files locally you can use `load_dataset()`. We recommend you set the `cache_dir` argument to point to a location that has plenty of disk space (1.2TB+). Here's how to download the `train` split:
```python
from datasets import load_dataset
# To download/load all splits at once, don't specify a split
rixvox = load_dataset("KBLab/rixvox", split="train", cache_dir="data_rixvox")
```
You can also stream the dataset. This is useful if you want to explore the dataset or if you don't have enough disk space to download the entire dataset. Here's how to stream the `train` split:
```python
from datasets import load_dataset
rixvox = load_dataset("KBLab/rixvox", cache_dir="data_rixvox", split="train", streaming=True)
print(next(iter(rixvox)))
# Grab 5 observations
rixvox_subset = rixvox.take(5)
for example in rixvox_subset:
print(example)
```
**Create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch)** with your dataset.
Local mode:
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
# Dataset is not pre-shuffled, recommend shuffling it before training.
rixvox = load_dataset("KBLab/rixvox", split="train", cache_dir="data_rixvox")
batch_sampler = BatchSampler(RandomSampler(rixvox), batch_size=32, drop_last=False)
dataloader = DataLoader(rixvox, batch_sampler=batch_sampler)
```
Streaming mode:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
rixvox = load_dataset("KBLab/rixvox", split="train", cache_dir="data_rixvox")
dataloader = DataLoader(rixvox, batch_size=32)
```
See Huggingface's guide on [streaming datasets](https://huggingface.co/docs/datasets/v1.11.0/dataset_streaming.html) for more information on how to shuffle in streaming mode.
### Languages
- Swedish. The BCP 47 language tag for Swedish is `sv`.
## Dataset Structure
### Data Instances
There are a total of `835044` observations from `1194` different speakers. Each observation can be up to 30 seconds in duration. An observation belongs to a debate (`dokid`), is extratected from a speech (`anforande_nummer`), and is numbered according to its order within the speech (`observation_nr`). Here is an example of an observation:
```
{'dokid': 'GR01BOU3',
'anforande_nummer': 191,
'observation_nr': 0,
'audio': {'path': 'GR01BOU3/2442210220028601121_anf191_1_25.wav',
'array': array([0.01171875, 0.01242065, 0.01071167, ..., 0.00689697, 0.00918579,
0.00650024]),
'sampling_rate': 16000},
'text': 'Kristdemokraterna står bakom alla reservationer med kristdemokratiska förtecken, men jag nöjer mig med att yrka bifall till reservation 1. Jag ska i det här inlägget beröra några av de åtta punkter som är föremål för reservationer från kristdemokratiskt håll, i vissa fall tillsammans med andra partier.',
'debatedate': datetime.datetime(2003, 12, 4, 0, 0),
'speaker': 'Göran Hägglund',
'party': 'KD',
'gender': 'male',
'birth_year': 1959,
'electoral_district': 'Hallands län',
'intressent_id': '0584659199514',
'speaker_from_id': True,
'speaker_audio_meta': 'Göran Hägglund (Kd)',
'start': 1.4,
'end': 24.96,
'duration': 23.560000000000002,
'bleu_score': 0.7212783273624307,
'filename': 'GR01BOU3/2442210220028601121_anf191_1_25.wav',
'path': 'GR01BOU3/2442210220028601121_anf191_1_25.wav',
'speaker_total_hours': 30.621333333333332}
```
See more examples in the [dataset viewer](https://huggingface.co/datasets/KBLab/rixvox/viewer/default/train).
### Data Fields
* `dokid`: Document id for the debate used by the Riksdag. This is the same for all speeches in a debate.
* `anforande_nummer`: Speech number within the debate, or within the debate sessions on a particular day. Should create a unique primary key for a speech in combination with `dokid` (sometimes there are duplicates, but we removed them from this dataset).
* `observation_nr`: Observation number within the speech. Creates a unique identifier for an observation in combination with `dokid` and `anforande_nummer`.
* `text`: The text transcript from written protocols. The transcripts are not always verbatim. Transcribers have to different degrees adjusted sentence ordering, words and phrasing when they deemed it appropriate.
* `debatedate`: The date of the debate.
* `start`: The start time of the observation within a speech (in seconds).
* `end`: The end time of the observation within a speech (in seconds).
* `duration`: The duration of the observation (`end` subtracted with `start`).
* `intressent_id`: Unique id for the speaker within the Riksdag's database (see [person.csv.zip](https://data.riksdagen.se/dataset/person/person.csv.zip) from the Riksdag).
* `speaker`: The speaker's name retrieved via the `intressent_id`.
* `party`: The speaker's party retrieved via the `intressent_id`.
* `gender`: The speaker's gender retrieved via the `intressent_id`.
* `birth_year`: The speaker's bith year retrieved via the `intressent_id`.
* `electoral_district`: The electoral district which the speaker represents if they are/were a member of parliament (retrieved via the `intressent_id`).
* `speaker_audio_meta`: The speaker's name and title as listed in the Riksdag's oroginal text format metadata (sometimes wrong and mismatched against `intressent_id`).
* `speaker_from_id`: Whether the speaker metadata was retrieved via the `intressent_id` or via the Riksdag's original metadata (for those speeches with a missing `intressent_id`).
* `bleu_score`: The BLEU score of the automatic speech recognition (ASR) transcript against the Riksdag's written protocol. Calculated on the entirity of the speech that an observation (30s snippet) is extracted from. A low number for a speech may indicate that either i) the ASR model had trouble transcribing the speaker's accent or dialect, or ii) the transcription took certain liberties in editing and rephrasing the speech.
* `speaker_total_hours`: The total number of hours of speech from the speaker in the RixVox dataset.
* `filename`: The filename of the observation in the compressed tar.gz files. Useful if you don't want to use Huggingface `datasets`, but would rather manually download and extract the files from the data shards.
* `path`: Dynamically created variable. Contains the local path to the observation's audio file after you download and extract the files via `load_dataset()` in the `datasets` library.
### Data Splits
Dataset splits were randomly sampled on the speaker level. That is, a speaker is only present in a single split. We sample speakers for each split until the following conditions are met:
- 98% of the total number of hours of speech are included in the train split.
- 1% of the total number of hours of speech are included in the validation split.
- 1% of the total number of hours of speech are included in the test split.
| Dataset Split | Observations | Total duration of speech (hours) | Average duration obs. (seconds) | Number of speakers |
| ------------- | ----------------: | -------------------------------: | ------------------------------: | -----------------: |
| Train | 818227 | 5383 | 23.69 | 1165 |
| Validation | 7933 | 52 | 23.50 | 18 |
| Test | 8884 | 59 | 23.74 | 11 |
## Dataset Creation
For more information about the creation of this dataset, see the article ["Finding Speeches in the Riksdag's Debates"](https://kb-labb.github.io/posts/2023-02-15-finding-speeches-in-the-riksdags-debates/) from our blog.
### Curation Rationale
Before RixVox, there was only a couple of hundred hours of transcribed speech available to train ASR models for Swedish. ASR models such as Whisper have shown that the performance of models can benefit significantly from adding more supervised data during pretraining or finetuning. Media from debates in the Riksdag are published openly on the web together with transcripts and other metadata. The open data initiatives of the Riksdag presented an opportunity to create a high quality open speech corpus for Swedish.
### Source Data
The Swedish Parliament.
- [Transcripts of speeches](https://data.riksdagen.se/data/anforanden/).
- Use the `rel_dok_id` of transcripts of speeches to query the Riksdag's media API (e.g. https://data.riksdagen.se/api/mhs-vodapi?H901FiU1 ) for available media and metadata.
#### Initial Data Collection and Normalization
For information on how the speeches were segmented and identified in debate audio files, see the article ["Finding Speeches in the Riksdag's Debates"](https://kb-labb.github.io/posts/2023-02-15-finding-speeches-in-the-riksdags-debates/).
For information on how the speech segmentations were used to create the final RixVox dataset, see the article ["RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates"](https://kb-labb.github.io/posts/2023-03-09-rixvox-a-swedish-speech-corpus/).
The code to replicate the creation of the dataset is open and available at the GitHub repository [KBLab/riksdagen_anforanden](https://github.com/kb-labb/riksdagen_anforanden). Processing everything can take 1-3 weeks on a workstation with consumer grade GPU.
#### Who are the source language producers?
The written protocols of speeches are manually produced by the Riksdag. Transcription is not always verbatim, but rather catches the intent of the speaker.
Segmenting speeches to determine when they start and end in a debate was done automatically. Sentence level alignment of the written protocols to the audio files was also done automatically using `aeneas`. See the articles in citation information for more details.
### Annotations
#### Annotation process
The process of aligning speech to written protocols was automatic. It followed the following general steps:
1. We used ASR to automatically transcribe the debate audio files and get word timestamps for the machine generated transcription.
2. We used fuzzy string matching to determine approximate start/end of a speech, matching the official written protocol of the speech to the machine generated transcription of the debate.
3. We perform speaker diarization using pyannote.audio.
4. We assign speaker diarization segments to speeches by the degree of overlap between approximate start/end from fuzzy string matching and the speaker diarization segments. The start and end of the diarization segment is used as our new adjusted start and end metadata of the speech.
5. Based on adjusted metadata of start/end of as speech, we split and extract the audio of speeches from the debates and then align the segmented speeches to the written protocol using `aeneas` (sentence-level alignment).
#### Who are the annotators?
No manual annotations.
### Personal and Sensitive Information
The speakers are members of parliament or ministers speaking publicly in the Riksdag. The Riksdag is a public institution and the speeches are publicly available on the web as open data.
## Considerations for Using the Data
### Social Impact of Dataset
We except the dataset primarily to be used in training ASR models for Swedish. The performance of Swedish text-to-speech in multillingual ASR models may also benefit from the availability of a large Swedish speech corpus. In turn, improved ASR models can serve to help increase accessibility of audio and video media content for people with hearing impairments.
The dataset can also be used to train models for other audio tasks such as speaker diarization, speaker verification, and speaker recognition.
Since metadata regarding the age, gender, and electoral district of the speaker is included, the dataset can possibly also be used to study bias in ASR models.
### Discussion of Biases
The dataset includes parliamentary speeches, which are often more formal than everyday speech.
During the creation of the dataset, we found that speech segmentations based on speaker diarization were more likely to fail when a preceding speaker, the speaker of the house, and the speaker of the following speech were all of the same gender. However, all in all, only a small number of speeches were filtered out of the final RixVox dataset. After quality filtering of the dataset, 5500 out of 5858 hours remained. We do not believe any significant systematic bias was introduced by this filtering.
Only minimal deduplication was performed to weed out commonly repeated phrases. For example, certain phrases such as "Fru talman!", "Herr Talman!", tend to be used a lot as a matter of formality. These phrases tend to be present at the beginning of most transcripts regardless whether it was uttered by the speaker or not. For this reason we have removed the first aligned sentence of each speech when creating RixVox. The aforementioned phrases are repeated frequently in speeches as well, though. As such it might be beneficial to perform more aggressive deduplication of the dataset before training models.
### Other Known Limitations
## Additional Information
### Dataset Curators
KBLab at the the National Library of Sweden.
### Future updates
There is a possiblity RixVox will be periodically, and irregularly, updated by including both older and newer speeches. Older recordings of parliamentary debates from 1966 to 2002 do exist, but they are not yet part of the Riksdag's open data. KBLab are exploring the possibility of adding metadata to these recordings by applying the existing speech segmentation and alignment pipeline to them.
Each year also brings new parliamentary debates, with recent years adding 400-500 hours of speech per year.
### Licensing Information
[CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)
Cite the Swedish Parliament.
To reference RixVox, feel free to cite KBLab blog posts in the citation information below.
### Citation Information
```
@misc{rekathati2023rixvox:,
author = {Rekathati, Faton},
title = {The KBLab Blog: RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates},
url = {https://kb-labb.github.io/posts/2023-03-09-rixvox-a-swedish-speech-corpus/},
year = {2023}
}
```
```
@misc{rekathati2023finding,
author = {Rekathati, Faton},
title = {The KBLab Blog: Finding Speeches in the Riksdag's Debates},
url = {https://kb-labb.github.io/posts/2023-02-15-finding-speeches-in-the-riksdags-debates/},
year = {2023}
}
```
The Swedish Parliament.
### Contributions
Thanks to [@lhoestq](https://huggingface.co/lhoestq) for reviewing the dataset script. | KBLab/rixvox | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:sv",
"license:cc-by-4.0",
"audio",
"speech-recognition",
"region:us"
]
| 2023-03-03T11:07:18+00:00 | {"language": "sv", "license": "cc-by-4.0", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["automatic-speech-recognition"], "tags": ["audio", "speech-recognition"]} | 2023-08-17T09:26:47+00:00 | []
| [
"sv"
]
| TAGS
#task_categories-automatic-speech-recognition #multilinguality-monolingual #size_categories-100K<n<1M #language-Swedish #license-cc-by-4.0 #audio #speech-recognition #region-us
| Dataset Card for RixVox
=======================
Dataset Description
-------------------
* Repository: Riksdagen anföranden repository
* Paper: "RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates"
* Point of Contact: KBLab
* Total amount of disk used: ca. 1.2 TB
### Dataset Summary
RixVox is a speech dataset comprised of speeches from the Riksdag (the Swedish Parliament). It covers speeches from debates during the period 2003-2023. Audio from speeches have been aligned, on the sentence level, with transcripts from written protocols using 'aeneas'. An observation may consist of one or several concatenated sentences (up to 30 seconds in duration). Detailed speaker metadata is available for each observation, including the speaker's name, gender, political party, birth year and the electoral district they represent. The dataset contains a total of 5493 hours of speech with transcriptions.
How to use & Supported Tasks
----------------------------
### Supported Tasks
Tasks are not supported by default (there are no label fields). The dataset may however be suited for:
* Automatic Speech Recognition (ASR).
* Speaker identification and verification.
* Creation of synthetic diarization datasets.
* Research on bias in ASR systems.
### How to use
To download and extract the files locally you can use 'load\_dataset()'. We recommend you set the 'cache\_dir' argument to point to a location that has plenty of disk space (1.2TB+). Here's how to download the 'train' split:
You can also stream the dataset. This is useful if you want to explore the dataset or if you don't have enough disk space to download the entire dataset. Here's how to stream the 'train' split:
Create a PyTorch dataloader with your dataset.
Local mode:
Streaming mode:
See Huggingface's guide on streaming datasets for more information on how to shuffle in streaming mode.
### Languages
* Swedish. The BCP 47 language tag for Swedish is 'sv'.
Dataset Structure
-----------------
### Data Instances
There are a total of '835044' observations from '1194' different speakers. Each observation can be up to 30 seconds in duration. An observation belongs to a debate ('dokid'), is extratected from a speech ('anforande\_nummer'), and is numbered according to its order within the speech ('observation\_nr'). Here is an example of an observation:
See more examples in the dataset viewer.
### Data Fields
* 'dokid': Document id for the debate used by the Riksdag. This is the same for all speeches in a debate.
* 'anforande\_nummer': Speech number within the debate, or within the debate sessions on a particular day. Should create a unique primary key for a speech in combination with 'dokid' (sometimes there are duplicates, but we removed them from this dataset).
* 'observation\_nr': Observation number within the speech. Creates a unique identifier for an observation in combination with 'dokid' and 'anforande\_nummer'.
* 'text': The text transcript from written protocols. The transcripts are not always verbatim. Transcribers have to different degrees adjusted sentence ordering, words and phrasing when they deemed it appropriate.
* 'debatedate': The date of the debate.
* 'start': The start time of the observation within a speech (in seconds).
* 'end': The end time of the observation within a speech (in seconds).
* 'duration': The duration of the observation ('end' subtracted with 'start').
* 'intressent\_id': Unique id for the speaker within the Riksdag's database (see URL from the Riksdag).
* 'speaker': The speaker's name retrieved via the 'intressent\_id'.
* 'party': The speaker's party retrieved via the 'intressent\_id'.
* 'gender': The speaker's gender retrieved via the 'intressent\_id'.
* 'birth\_year': The speaker's bith year retrieved via the 'intressent\_id'.
* 'electoral\_district': The electoral district which the speaker represents if they are/were a member of parliament (retrieved via the 'intressent\_id').
* 'speaker\_audio\_meta': The speaker's name and title as listed in the Riksdag's oroginal text format metadata (sometimes wrong and mismatched against 'intressent\_id').
* 'speaker\_from\_id': Whether the speaker metadata was retrieved via the 'intressent\_id' or via the Riksdag's original metadata (for those speeches with a missing 'intressent\_id').
* 'bleu\_score': The BLEU score of the automatic speech recognition (ASR) transcript against the Riksdag's written protocol. Calculated on the entirity of the speech that an observation (30s snippet) is extracted from. A low number for a speech may indicate that either i) the ASR model had trouble transcribing the speaker's accent or dialect, or ii) the transcription took certain liberties in editing and rephrasing the speech.
* 'speaker\_total\_hours': The total number of hours of speech from the speaker in the RixVox dataset.
* 'filename': The filename of the observation in the compressed URL files. Useful if you don't want to use Huggingface 'datasets', but would rather manually download and extract the files from the data shards.
* 'path': Dynamically created variable. Contains the local path to the observation's audio file after you download and extract the files via 'load\_dataset()' in the 'datasets' library.
### Data Splits
Dataset splits were randomly sampled on the speaker level. That is, a speaker is only present in a single split. We sample speakers for each split until the following conditions are met:
* 98% of the total number of hours of speech are included in the train split.
* 1% of the total number of hours of speech are included in the validation split.
* 1% of the total number of hours of speech are included in the test split.
Dataset Creation
----------------
For more information about the creation of this dataset, see the article "Finding Speeches in the Riksdag's Debates" from our blog.
### Curation Rationale
Before RixVox, there was only a couple of hundred hours of transcribed speech available to train ASR models for Swedish. ASR models such as Whisper have shown that the performance of models can benefit significantly from adding more supervised data during pretraining or finetuning. Media from debates in the Riksdag are published openly on the web together with transcripts and other metadata. The open data initiatives of the Riksdag presented an opportunity to create a high quality open speech corpus for Swedish.
### Source Data
The Swedish Parliament.
* Transcripts of speeches.
* Use the 'rel\_dok\_id' of transcripts of speeches to query the Riksdag's media API (e.g. URL ) for available media and metadata.
#### Initial Data Collection and Normalization
For information on how the speeches were segmented and identified in debate audio files, see the article "Finding Speeches in the Riksdag's Debates".
For information on how the speech segmentations were used to create the final RixVox dataset, see the article "RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates".
The code to replicate the creation of the dataset is open and available at the GitHub repository KBLab/riksdagen\_anforanden. Processing everything can take 1-3 weeks on a workstation with consumer grade GPU.
#### Who are the source language producers?
The written protocols of speeches are manually produced by the Riksdag. Transcription is not always verbatim, but rather catches the intent of the speaker.
Segmenting speeches to determine when they start and end in a debate was done automatically. Sentence level alignment of the written protocols to the audio files was also done automatically using 'aeneas'. See the articles in citation information for more details.
### Annotations
#### Annotation process
The process of aligning speech to written protocols was automatic. It followed the following general steps:
1. We used ASR to automatically transcribe the debate audio files and get word timestamps for the machine generated transcription.
2. We used fuzzy string matching to determine approximate start/end of a speech, matching the official written protocol of the speech to the machine generated transcription of the debate.
3. We perform speaker diarization using URL.
4. We assign speaker diarization segments to speeches by the degree of overlap between approximate start/end from fuzzy string matching and the speaker diarization segments. The start and end of the diarization segment is used as our new adjusted start and end metadata of the speech.
5. Based on adjusted metadata of start/end of as speech, we split and extract the audio of speeches from the debates and then align the segmented speeches to the written protocol using 'aeneas' (sentence-level alignment).
#### Who are the annotators?
No manual annotations.
### Personal and Sensitive Information
The speakers are members of parliament or ministers speaking publicly in the Riksdag. The Riksdag is a public institution and the speeches are publicly available on the web as open data.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
We except the dataset primarily to be used in training ASR models for Swedish. The performance of Swedish text-to-speech in multillingual ASR models may also benefit from the availability of a large Swedish speech corpus. In turn, improved ASR models can serve to help increase accessibility of audio and video media content for people with hearing impairments.
The dataset can also be used to train models for other audio tasks such as speaker diarization, speaker verification, and speaker recognition.
Since metadata regarding the age, gender, and electoral district of the speaker is included, the dataset can possibly also be used to study bias in ASR models.
### Discussion of Biases
The dataset includes parliamentary speeches, which are often more formal than everyday speech.
During the creation of the dataset, we found that speech segmentations based on speaker diarization were more likely to fail when a preceding speaker, the speaker of the house, and the speaker of the following speech were all of the same gender. However, all in all, only a small number of speeches were filtered out of the final RixVox dataset. After quality filtering of the dataset, 5500 out of 5858 hours remained. We do not believe any significant systematic bias was introduced by this filtering.
Only minimal deduplication was performed to weed out commonly repeated phrases. For example, certain phrases such as "Fru talman!", "Herr Talman!", tend to be used a lot as a matter of formality. These phrases tend to be present at the beginning of most transcripts regardless whether it was uttered by the speaker or not. For this reason we have removed the first aligned sentence of each speech when creating RixVox. The aforementioned phrases are repeated frequently in speeches as well, though. As such it might be beneficial to perform more aggressive deduplication of the dataset before training models.
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
KBLab at the the National Library of Sweden.
### Future updates
There is a possiblity RixVox will be periodically, and irregularly, updated by including both older and newer speeches. Older recordings of parliamentary debates from 1966 to 2002 do exist, but they are not yet part of the Riksdag's open data. KBLab are exploring the possibility of adding metadata to these recordings by applying the existing speech segmentation and alignment pipeline to them.
Each year also brings new parliamentary debates, with recent years adding 400-500 hours of speech per year.
### Licensing Information
CC-BY 4.0
Cite the Swedish Parliament.
To reference RixVox, feel free to cite KBLab blog posts in the citation information below.
The Swedish Parliament.
### Contributions
Thanks to @lhoestq for reviewing the dataset script.
| [
"### Dataset Summary\n\n\nRixVox is a speech dataset comprised of speeches from the Riksdag (the Swedish Parliament). It covers speeches from debates during the period 2003-2023. Audio from speeches have been aligned, on the sentence level, with transcripts from written protocols using 'aeneas'. An observation may consist of one or several concatenated sentences (up to 30 seconds in duration). Detailed speaker metadata is available for each observation, including the speaker's name, gender, political party, birth year and the electoral district they represent. The dataset contains a total of 5493 hours of speech with transcriptions.\n\n\nHow to use & Supported Tasks\n----------------------------",
"### Supported Tasks\n\n\nTasks are not supported by default (there are no label fields). The dataset may however be suited for:\n\n\n* Automatic Speech Recognition (ASR).\n* Speaker identification and verification.\n* Creation of synthetic diarization datasets.\n* Research on bias in ASR systems.",
"### How to use\n\n\nTo download and extract the files locally you can use 'load\\_dataset()'. We recommend you set the 'cache\\_dir' argument to point to a location that has plenty of disk space (1.2TB+). Here's how to download the 'train' split:\n\n\nYou can also stream the dataset. This is useful if you want to explore the dataset or if you don't have enough disk space to download the entire dataset. Here's how to stream the 'train' split:\n\n\nCreate a PyTorch dataloader with your dataset.\n\n\nLocal mode:\n\n\nStreaming mode:\n\n\nSee Huggingface's guide on streaming datasets for more information on how to shuffle in streaming mode.",
"### Languages\n\n\n* Swedish. The BCP 47 language tag for Swedish is 'sv'.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThere are a total of '835044' observations from '1194' different speakers. Each observation can be up to 30 seconds in duration. An observation belongs to a debate ('dokid'), is extratected from a speech ('anforande\\_nummer'), and is numbered according to its order within the speech ('observation\\_nr'). Here is an example of an observation:\n\n\nSee more examples in the dataset viewer.",
"### Data Fields\n\n\n* 'dokid': Document id for the debate used by the Riksdag. This is the same for all speeches in a debate.\n* 'anforande\\_nummer': Speech number within the debate, or within the debate sessions on a particular day. Should create a unique primary key for a speech in combination with 'dokid' (sometimes there are duplicates, but we removed them from this dataset).\n* 'observation\\_nr': Observation number within the speech. Creates a unique identifier for an observation in combination with 'dokid' and 'anforande\\_nummer'.\n* 'text': The text transcript from written protocols. The transcripts are not always verbatim. Transcribers have to different degrees adjusted sentence ordering, words and phrasing when they deemed it appropriate.\n* 'debatedate': The date of the debate.\n* 'start': The start time of the observation within a speech (in seconds).\n* 'end': The end time of the observation within a speech (in seconds).\n* 'duration': The duration of the observation ('end' subtracted with 'start').\n* 'intressent\\_id': Unique id for the speaker within the Riksdag's database (see URL from the Riksdag).\n* 'speaker': The speaker's name retrieved via the 'intressent\\_id'.\n* 'party': The speaker's party retrieved via the 'intressent\\_id'.\n* 'gender': The speaker's gender retrieved via the 'intressent\\_id'.\n* 'birth\\_year': The speaker's bith year retrieved via the 'intressent\\_id'.\n* 'electoral\\_district': The electoral district which the speaker represents if they are/were a member of parliament (retrieved via the 'intressent\\_id').\n* 'speaker\\_audio\\_meta': The speaker's name and title as listed in the Riksdag's oroginal text format metadata (sometimes wrong and mismatched against 'intressent\\_id').\n* 'speaker\\_from\\_id': Whether the speaker metadata was retrieved via the 'intressent\\_id' or via the Riksdag's original metadata (for those speeches with a missing 'intressent\\_id').\n* 'bleu\\_score': The BLEU score of the automatic speech recognition (ASR) transcript against the Riksdag's written protocol. Calculated on the entirity of the speech that an observation (30s snippet) is extracted from. A low number for a speech may indicate that either i) the ASR model had trouble transcribing the speaker's accent or dialect, or ii) the transcription took certain liberties in editing and rephrasing the speech.\n* 'speaker\\_total\\_hours': The total number of hours of speech from the speaker in the RixVox dataset.\n* 'filename': The filename of the observation in the compressed URL files. Useful if you don't want to use Huggingface 'datasets', but would rather manually download and extract the files from the data shards.\n* 'path': Dynamically created variable. Contains the local path to the observation's audio file after you download and extract the files via 'load\\_dataset()' in the 'datasets' library.",
"### Data Splits\n\n\nDataset splits were randomly sampled on the speaker level. That is, a speaker is only present in a single split. We sample speakers for each split until the following conditions are met:\n\n\n* 98% of the total number of hours of speech are included in the train split.\n* 1% of the total number of hours of speech are included in the validation split.\n* 1% of the total number of hours of speech are included in the test split.\n\n\n\nDataset Creation\n----------------\n\n\nFor more information about the creation of this dataset, see the article \"Finding Speeches in the Riksdag's Debates\" from our blog.",
"### Curation Rationale\n\n\nBefore RixVox, there was only a couple of hundred hours of transcribed speech available to train ASR models for Swedish. ASR models such as Whisper have shown that the performance of models can benefit significantly from adding more supervised data during pretraining or finetuning. Media from debates in the Riksdag are published openly on the web together with transcripts and other metadata. The open data initiatives of the Riksdag presented an opportunity to create a high quality open speech corpus for Swedish.",
"### Source Data\n\n\nThe Swedish Parliament.\n\n\n* Transcripts of speeches.\n* Use the 'rel\\_dok\\_id' of transcripts of speeches to query the Riksdag's media API (e.g. URL ) for available media and metadata.",
"#### Initial Data Collection and Normalization\n\n\nFor information on how the speeches were segmented and identified in debate audio files, see the article \"Finding Speeches in the Riksdag's Debates\".\n\n\nFor information on how the speech segmentations were used to create the final RixVox dataset, see the article \"RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates\".\n\n\nThe code to replicate the creation of the dataset is open and available at the GitHub repository KBLab/riksdagen\\_anforanden. Processing everything can take 1-3 weeks on a workstation with consumer grade GPU.",
"#### Who are the source language producers?\n\n\nThe written protocols of speeches are manually produced by the Riksdag. Transcription is not always verbatim, but rather catches the intent of the speaker.\n\n\nSegmenting speeches to determine when they start and end in a debate was done automatically. Sentence level alignment of the written protocols to the audio files was also done automatically using 'aeneas'. See the articles in citation information for more details.",
"### Annotations",
"#### Annotation process\n\n\nThe process of aligning speech to written protocols was automatic. It followed the following general steps:\n\n\n1. We used ASR to automatically transcribe the debate audio files and get word timestamps for the machine generated transcription.\n2. We used fuzzy string matching to determine approximate start/end of a speech, matching the official written protocol of the speech to the machine generated transcription of the debate.\n3. We perform speaker diarization using URL.\n4. We assign speaker diarization segments to speeches by the degree of overlap between approximate start/end from fuzzy string matching and the speaker diarization segments. The start and end of the diarization segment is used as our new adjusted start and end metadata of the speech.\n5. Based on adjusted metadata of start/end of as speech, we split and extract the audio of speeches from the debates and then align the segmented speeches to the written protocol using 'aeneas' (sentence-level alignment).",
"#### Who are the annotators?\n\n\nNo manual annotations.",
"### Personal and Sensitive Information\n\n\nThe speakers are members of parliament or ministers speaking publicly in the Riksdag. The Riksdag is a public institution and the speeches are publicly available on the web as open data.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nWe except the dataset primarily to be used in training ASR models for Swedish. The performance of Swedish text-to-speech in multillingual ASR models may also benefit from the availability of a large Swedish speech corpus. In turn, improved ASR models can serve to help increase accessibility of audio and video media content for people with hearing impairments.\n\n\nThe dataset can also be used to train models for other audio tasks such as speaker diarization, speaker verification, and speaker recognition.\n\n\nSince metadata regarding the age, gender, and electoral district of the speaker is included, the dataset can possibly also be used to study bias in ASR models.",
"### Discussion of Biases\n\n\nThe dataset includes parliamentary speeches, which are often more formal than everyday speech.\n\n\nDuring the creation of the dataset, we found that speech segmentations based on speaker diarization were more likely to fail when a preceding speaker, the speaker of the house, and the speaker of the following speech were all of the same gender. However, all in all, only a small number of speeches were filtered out of the final RixVox dataset. After quality filtering of the dataset, 5500 out of 5858 hours remained. We do not believe any significant systematic bias was introduced by this filtering.\n\n\nOnly minimal deduplication was performed to weed out commonly repeated phrases. For example, certain phrases such as \"Fru talman!\", \"Herr Talman!\", tend to be used a lot as a matter of formality. These phrases tend to be present at the beginning of most transcripts regardless whether it was uttered by the speaker or not. For this reason we have removed the first aligned sentence of each speech when creating RixVox. The aforementioned phrases are repeated frequently in speeches as well, though. As such it might be beneficial to perform more aggressive deduplication of the dataset before training models.",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nKBLab at the the National Library of Sweden.",
"### Future updates\n\n\nThere is a possiblity RixVox will be periodically, and irregularly, updated by including both older and newer speeches. Older recordings of parliamentary debates from 1966 to 2002 do exist, but they are not yet part of the Riksdag's open data. KBLab are exploring the possibility of adding metadata to these recordings by applying the existing speech segmentation and alignment pipeline to them.\n\n\nEach year also brings new parliamentary debates, with recent years adding 400-500 hours of speech per year.",
"### Licensing Information\n\n\nCC-BY 4.0\n\n\nCite the Swedish Parliament.\n\n\nTo reference RixVox, feel free to cite KBLab blog posts in the citation information below.\n\n\nThe Swedish Parliament.",
"### Contributions\n\n\nThanks to @lhoestq for reviewing the dataset script."
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #multilinguality-monolingual #size_categories-100K<n<1M #language-Swedish #license-cc-by-4.0 #audio #speech-recognition #region-us \n",
"### Dataset Summary\n\n\nRixVox is a speech dataset comprised of speeches from the Riksdag (the Swedish Parliament). It covers speeches from debates during the period 2003-2023. Audio from speeches have been aligned, on the sentence level, with transcripts from written protocols using 'aeneas'. An observation may consist of one or several concatenated sentences (up to 30 seconds in duration). Detailed speaker metadata is available for each observation, including the speaker's name, gender, political party, birth year and the electoral district they represent. The dataset contains a total of 5493 hours of speech with transcriptions.\n\n\nHow to use & Supported Tasks\n----------------------------",
"### Supported Tasks\n\n\nTasks are not supported by default (there are no label fields). The dataset may however be suited for:\n\n\n* Automatic Speech Recognition (ASR).\n* Speaker identification and verification.\n* Creation of synthetic diarization datasets.\n* Research on bias in ASR systems.",
"### How to use\n\n\nTo download and extract the files locally you can use 'load\\_dataset()'. We recommend you set the 'cache\\_dir' argument to point to a location that has plenty of disk space (1.2TB+). Here's how to download the 'train' split:\n\n\nYou can also stream the dataset. This is useful if you want to explore the dataset or if you don't have enough disk space to download the entire dataset. Here's how to stream the 'train' split:\n\n\nCreate a PyTorch dataloader with your dataset.\n\n\nLocal mode:\n\n\nStreaming mode:\n\n\nSee Huggingface's guide on streaming datasets for more information on how to shuffle in streaming mode.",
"### Languages\n\n\n* Swedish. The BCP 47 language tag for Swedish is 'sv'.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThere are a total of '835044' observations from '1194' different speakers. Each observation can be up to 30 seconds in duration. An observation belongs to a debate ('dokid'), is extratected from a speech ('anforande\\_nummer'), and is numbered according to its order within the speech ('observation\\_nr'). Here is an example of an observation:\n\n\nSee more examples in the dataset viewer.",
"### Data Fields\n\n\n* 'dokid': Document id for the debate used by the Riksdag. This is the same for all speeches in a debate.\n* 'anforande\\_nummer': Speech number within the debate, or within the debate sessions on a particular day. Should create a unique primary key for a speech in combination with 'dokid' (sometimes there are duplicates, but we removed them from this dataset).\n* 'observation\\_nr': Observation number within the speech. Creates a unique identifier for an observation in combination with 'dokid' and 'anforande\\_nummer'.\n* 'text': The text transcript from written protocols. The transcripts are not always verbatim. Transcribers have to different degrees adjusted sentence ordering, words and phrasing when they deemed it appropriate.\n* 'debatedate': The date of the debate.\n* 'start': The start time of the observation within a speech (in seconds).\n* 'end': The end time of the observation within a speech (in seconds).\n* 'duration': The duration of the observation ('end' subtracted with 'start').\n* 'intressent\\_id': Unique id for the speaker within the Riksdag's database (see URL from the Riksdag).\n* 'speaker': The speaker's name retrieved via the 'intressent\\_id'.\n* 'party': The speaker's party retrieved via the 'intressent\\_id'.\n* 'gender': The speaker's gender retrieved via the 'intressent\\_id'.\n* 'birth\\_year': The speaker's bith year retrieved via the 'intressent\\_id'.\n* 'electoral\\_district': The electoral district which the speaker represents if they are/were a member of parliament (retrieved via the 'intressent\\_id').\n* 'speaker\\_audio\\_meta': The speaker's name and title as listed in the Riksdag's oroginal text format metadata (sometimes wrong and mismatched against 'intressent\\_id').\n* 'speaker\\_from\\_id': Whether the speaker metadata was retrieved via the 'intressent\\_id' or via the Riksdag's original metadata (for those speeches with a missing 'intressent\\_id').\n* 'bleu\\_score': The BLEU score of the automatic speech recognition (ASR) transcript against the Riksdag's written protocol. Calculated on the entirity of the speech that an observation (30s snippet) is extracted from. A low number for a speech may indicate that either i) the ASR model had trouble transcribing the speaker's accent or dialect, or ii) the transcription took certain liberties in editing and rephrasing the speech.\n* 'speaker\\_total\\_hours': The total number of hours of speech from the speaker in the RixVox dataset.\n* 'filename': The filename of the observation in the compressed URL files. Useful if you don't want to use Huggingface 'datasets', but would rather manually download and extract the files from the data shards.\n* 'path': Dynamically created variable. Contains the local path to the observation's audio file after you download and extract the files via 'load\\_dataset()' in the 'datasets' library.",
"### Data Splits\n\n\nDataset splits were randomly sampled on the speaker level. That is, a speaker is only present in a single split. We sample speakers for each split until the following conditions are met:\n\n\n* 98% of the total number of hours of speech are included in the train split.\n* 1% of the total number of hours of speech are included in the validation split.\n* 1% of the total number of hours of speech are included in the test split.\n\n\n\nDataset Creation\n----------------\n\n\nFor more information about the creation of this dataset, see the article \"Finding Speeches in the Riksdag's Debates\" from our blog.",
"### Curation Rationale\n\n\nBefore RixVox, there was only a couple of hundred hours of transcribed speech available to train ASR models for Swedish. ASR models such as Whisper have shown that the performance of models can benefit significantly from adding more supervised data during pretraining or finetuning. Media from debates in the Riksdag are published openly on the web together with transcripts and other metadata. The open data initiatives of the Riksdag presented an opportunity to create a high quality open speech corpus for Swedish.",
"### Source Data\n\n\nThe Swedish Parliament.\n\n\n* Transcripts of speeches.\n* Use the 'rel\\_dok\\_id' of transcripts of speeches to query the Riksdag's media API (e.g. URL ) for available media and metadata.",
"#### Initial Data Collection and Normalization\n\n\nFor information on how the speeches were segmented and identified in debate audio files, see the article \"Finding Speeches in the Riksdag's Debates\".\n\n\nFor information on how the speech segmentations were used to create the final RixVox dataset, see the article \"RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates\".\n\n\nThe code to replicate the creation of the dataset is open and available at the GitHub repository KBLab/riksdagen\\_anforanden. Processing everything can take 1-3 weeks on a workstation with consumer grade GPU.",
"#### Who are the source language producers?\n\n\nThe written protocols of speeches are manually produced by the Riksdag. Transcription is not always verbatim, but rather catches the intent of the speaker.\n\n\nSegmenting speeches to determine when they start and end in a debate was done automatically. Sentence level alignment of the written protocols to the audio files was also done automatically using 'aeneas'. See the articles in citation information for more details.",
"### Annotations",
"#### Annotation process\n\n\nThe process of aligning speech to written protocols was automatic. It followed the following general steps:\n\n\n1. We used ASR to automatically transcribe the debate audio files and get word timestamps for the machine generated transcription.\n2. We used fuzzy string matching to determine approximate start/end of a speech, matching the official written protocol of the speech to the machine generated transcription of the debate.\n3. We perform speaker diarization using URL.\n4. We assign speaker diarization segments to speeches by the degree of overlap between approximate start/end from fuzzy string matching and the speaker diarization segments. The start and end of the diarization segment is used as our new adjusted start and end metadata of the speech.\n5. Based on adjusted metadata of start/end of as speech, we split and extract the audio of speeches from the debates and then align the segmented speeches to the written protocol using 'aeneas' (sentence-level alignment).",
"#### Who are the annotators?\n\n\nNo manual annotations.",
"### Personal and Sensitive Information\n\n\nThe speakers are members of parliament or ministers speaking publicly in the Riksdag. The Riksdag is a public institution and the speeches are publicly available on the web as open data.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nWe except the dataset primarily to be used in training ASR models for Swedish. The performance of Swedish text-to-speech in multillingual ASR models may also benefit from the availability of a large Swedish speech corpus. In turn, improved ASR models can serve to help increase accessibility of audio and video media content for people with hearing impairments.\n\n\nThe dataset can also be used to train models for other audio tasks such as speaker diarization, speaker verification, and speaker recognition.\n\n\nSince metadata regarding the age, gender, and electoral district of the speaker is included, the dataset can possibly also be used to study bias in ASR models.",
"### Discussion of Biases\n\n\nThe dataset includes parliamentary speeches, which are often more formal than everyday speech.\n\n\nDuring the creation of the dataset, we found that speech segmentations based on speaker diarization were more likely to fail when a preceding speaker, the speaker of the house, and the speaker of the following speech were all of the same gender. However, all in all, only a small number of speeches were filtered out of the final RixVox dataset. After quality filtering of the dataset, 5500 out of 5858 hours remained. We do not believe any significant systematic bias was introduced by this filtering.\n\n\nOnly minimal deduplication was performed to weed out commonly repeated phrases. For example, certain phrases such as \"Fru talman!\", \"Herr Talman!\", tend to be used a lot as a matter of formality. These phrases tend to be present at the beginning of most transcripts regardless whether it was uttered by the speaker or not. For this reason we have removed the first aligned sentence of each speech when creating RixVox. The aforementioned phrases are repeated frequently in speeches as well, though. As such it might be beneficial to perform more aggressive deduplication of the dataset before training models.",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nKBLab at the the National Library of Sweden.",
"### Future updates\n\n\nThere is a possiblity RixVox will be periodically, and irregularly, updated by including both older and newer speeches. Older recordings of parliamentary debates from 1966 to 2002 do exist, but they are not yet part of the Riksdag's open data. KBLab are exploring the possibility of adding metadata to these recordings by applying the existing speech segmentation and alignment pipeline to them.\n\n\nEach year also brings new parliamentary debates, with recent years adding 400-500 hours of speech per year.",
"### Licensing Information\n\n\nCC-BY 4.0\n\n\nCite the Swedish Parliament.\n\n\nTo reference RixVox, feel free to cite KBLab blog posts in the citation information below.\n\n\nThe Swedish Parliament.",
"### Contributions\n\n\nThanks to @lhoestq for reviewing the dataset script."
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"passage: TAGS\n#task_categories-automatic-speech-recognition #multilinguality-monolingual #size_categories-100K<n<1M #language-Swedish #license-cc-by-4.0 #audio #speech-recognition #region-us \n### Dataset Summary\n\n\nRixVox is a speech dataset comprised of speeches from the Riksdag (the Swedish Parliament). It covers speeches from debates during the period 2003-2023. Audio from speeches have been aligned, on the sentence level, with transcripts from written protocols using 'aeneas'. An observation may consist of one or several concatenated sentences (up to 30 seconds in duration). Detailed speaker metadata is available for each observation, including the speaker's name, gender, political party, birth year and the electoral district they represent. The dataset contains a total of 5493 hours of speech with transcriptions.\n\n\nHow to use & Supported Tasks\n----------------------------### Supported Tasks\n\n\nTasks are not supported by default (there are no label fields). The dataset may however be suited for:\n\n\n* Automatic Speech Recognition (ASR).\n* Speaker identification and verification.\n* Creation of synthetic diarization datasets.\n* Research on bias in ASR systems.### How to use\n\n\nTo download and extract the files locally you can use 'load\\_dataset()'. We recommend you set the 'cache\\_dir' argument to point to a location that has plenty of disk space (1.2TB+). Here's how to download the 'train' split:\n\n\nYou can also stream the dataset. This is useful if you want to explore the dataset or if you don't have enough disk space to download the entire dataset. Here's how to stream the 'train' split:\n\n\nCreate a PyTorch dataloader with your dataset.\n\n\nLocal mode:\n\n\nStreaming mode:\n\n\nSee Huggingface's guide on streaming datasets for more information on how to shuffle in streaming mode.### Languages\n\n\n* Swedish. The BCP 47 language tag for Swedish is 'sv'.\n\n\nDataset Structure\n-----------------",
"passage: ### Data Instances\n\n\nThere are a total of '835044' observations from '1194' different speakers. Each observation can be up to 30 seconds in duration. An observation belongs to a debate ('dokid'), is extratected from a speech ('anforande\\_nummer'), and is numbered according to its order within the speech ('observation\\_nr'). Here is an example of an observation:\n\n\nSee more examples in the dataset viewer.",
"passage: ### Data Fields\n\n\n* 'dokid': Document id for the debate used by the Riksdag. This is the same for all speeches in a debate.\n* 'anforande\\_nummer': Speech number within the debate, or within the debate sessions on a particular day. Should create a unique primary key for a speech in combination with 'dokid' (sometimes there are duplicates, but we removed them from this dataset).\n* 'observation\\_nr': Observation number within the speech. Creates a unique identifier for an observation in combination with 'dokid' and 'anforande\\_nummer'.\n* 'text': The text transcript from written protocols. The transcripts are not always verbatim. Transcribers have to different degrees adjusted sentence ordering, words and phrasing when they deemed it appropriate.\n* 'debatedate': The date of the debate.\n* 'start': The start time of the observation within a speech (in seconds).\n* 'end': The end time of the observation within a speech (in seconds).\n* 'duration': The duration of the observation ('end' subtracted with 'start').\n* 'intressent\\_id': Unique id for the speaker within the Riksdag's database (see URL from the Riksdag).\n* 'speaker': The speaker's name retrieved via the 'intressent\\_id'.\n* 'party': The speaker's party retrieved via the 'intressent\\_id'.\n* 'gender': The speaker's gender retrieved via the 'intressent\\_id'.\n* 'birth\\_year': The speaker's bith year retrieved via the 'intressent\\_id'.\n* 'electoral\\_district': The electoral district which the speaker represents if they are/were a member of parliament (retrieved via the 'intressent\\_id').\n* 'speaker\\_audio\\_meta': The speaker's name and title as listed in the Riksdag's oroginal text format metadata (sometimes wrong and mismatched against 'intressent\\_id').\n* 'speaker\\_from\\_id': Whether the speaker metadata was retrieved via the 'intressent\\_id' or via the Riksdag's original metadata (for those speeches with a missing 'intressent\\_id').\n* 'bleu\\_score': The BLEU score of the automatic speech recognition (ASR) transcript against the Riksdag's written protocol. Calculated on the entirity of the speech that an observation (30s snippet) is extracted from. A low number for a speech may indicate that either i) the ASR model had trouble transcribing the speaker's accent or dialect, or ii) the transcription took certain liberties in editing and rephrasing the speech.\n* 'speaker\\_total\\_hours': The total number of hours of speech from the speaker in the RixVox dataset.\n* 'filename': The filename of the observation in the compressed URL files. Useful if you don't want to use Huggingface 'datasets', but would rather manually download and extract the files from the data shards.\n* 'path': Dynamically created variable. Contains the local path to the observation's audio file after you download and extract the files via 'load\\_dataset()' in the 'datasets' library.### Data Splits\n\n\nDataset splits were randomly sampled on the speaker level. That is, a speaker is only present in a single split. We sample speakers for each split until the following conditions are met:\n\n\n* 98% of the total number of hours of speech are included in the train split.\n* 1% of the total number of hours of speech are included in the validation split.\n* 1% of the total number of hours of speech are included in the test split.\n\n\n\nDataset Creation\n----------------\n\n\nFor more information about the creation of this dataset, see the article \"Finding Speeches in the Riksdag's Debates\" from our blog.### Curation Rationale\n\n\nBefore RixVox, there was only a couple of hundred hours of transcribed speech available to train ASR models for Swedish. ASR models such as Whisper have shown that the performance of models can benefit significantly from adding more supervised data during pretraining or finetuning. Media from debates in the Riksdag are published openly on the web together with transcripts and other metadata. The open data initiatives of the Riksdag presented an opportunity to create a high quality open speech corpus for Swedish.### Source Data\n\n\nThe Swedish Parliament.\n\n\n* Transcripts of speeches.\n* Use the 'rel\\_dok\\_id' of transcripts of speeches to query the Riksdag's media API (e.g. URL ) for available media and metadata.#### Initial Data Collection and Normalization\n\n\nFor information on how the speeches were segmented and identified in debate audio files, see the article \"Finding Speeches in the Riksdag's Debates\".\n\n\nFor information on how the speech segmentations were used to create the final RixVox dataset, see the article \"RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates\".\n\n\nThe code to replicate the creation of the dataset is open and available at the GitHub repository KBLab/riksdagen\\_anforanden. Processing everything can take 1-3 weeks on a workstation with consumer grade GPU.",
"passage: #### Who are the source language producers?\n\n\nThe written protocols of speeches are manually produced by the Riksdag. Transcription is not always verbatim, but rather catches the intent of the speaker.\n\n\nSegmenting speeches to determine when they start and end in a debate was done automatically. Sentence level alignment of the written protocols to the audio files was also done automatically using 'aeneas'. See the articles in citation information for more details.### Annotations#### Annotation process\n\n\nThe process of aligning speech to written protocols was automatic. It followed the following general steps:\n\n\n1. We used ASR to automatically transcribe the debate audio files and get word timestamps for the machine generated transcription.\n2. We used fuzzy string matching to determine approximate start/end of a speech, matching the official written protocol of the speech to the machine generated transcription of the debate.\n3. We perform speaker diarization using URL.\n4. We assign speaker diarization segments to speeches by the degree of overlap between approximate start/end from fuzzy string matching and the speaker diarization segments. The start and end of the diarization segment is used as our new adjusted start and end metadata of the speech.\n5. Based on adjusted metadata of start/end of as speech, we split and extract the audio of speeches from the debates and then align the segmented speeches to the written protocol using 'aeneas' (sentence-level alignment).#### Who are the annotators?\n\n\nNo manual annotations.### Personal and Sensitive Information\n\n\nThe speakers are members of parliament or ministers speaking publicly in the Riksdag. The Riksdag is a public institution and the speeches are publicly available on the web as open data.\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nWe except the dataset primarily to be used in training ASR models for Swedish. The performance of Swedish text-to-speech in multillingual ASR models may also benefit from the availability of a large Swedish speech corpus. In turn, improved ASR models can serve to help increase accessibility of audio and video media content for people with hearing impairments.\n\n\nThe dataset can also be used to train models for other audio tasks such as speaker diarization, speaker verification, and speaker recognition.\n\n\nSince metadata regarding the age, gender, and electoral district of the speaker is included, the dataset can possibly also be used to study bias in ASR models."
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|
597e99ef70598227472981677a77b6daadd228a9 | # Dataset Card for "cnn_dailymail_nor_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jkorsvik/cnn_dailymail_nor_v2 | [
"region:us"
]
| 2023-03-03T11:17:35+00:00 | {"dataset_info": {"features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 49310937, "num_examples": 11490}, {"name": "train", "num_bytes": 123625984, "num_examples": 31500}, {"name": "validation", "num_bytes": 56993804, "num_examples": 13368}], "download_size": 78083731, "dataset_size": 229930725}} | 2023-03-03T13:36:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cnn_dailymail_nor_v2"
More Information needed | [
"# Dataset Card for \"cnn_dailymail_nor_v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cnn_dailymail_nor_v2\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cnn_dailymail_nor_v2\"\n\nMore Information needed"
]
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|
6a1f725fc3160417d83418c301e1c3e3a2635040 | # Dataset Card for "spectrogram_data_max_music_dataset-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | deepak-newzera/spectrogram_data_max_music_dataset-1 | [
"region:us"
]
| 2023-03-03T11:52:50+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio_file", "dtype": "string"}, {"name": "slice", "dtype": "int16"}], "splits": [{"name": "train", "num_bytes": 705907835.375, "num_examples": 15949}], "download_size": 703640547, "dataset_size": 705907835.375}} | 2023-03-03T11:56:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "spectrogram_data_max_music_dataset-1"
More Information needed | [
"# Dataset Card for \"spectrogram_data_max_music_dataset-1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"spectrogram_data_max_music_dataset-1\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"spectrogram_data_max_music_dataset-1\"\n\nMore Information needed"
]
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0.09624361991882324,
-0.1275368481874466,
0.018647463992238045,
-0.0484091117978096,
0.06855285167694092,
-0.02908059023320675,
0.06700003147125244,
-0.07072420418262482,
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0.04852969944477081,
-0.005916583817452192,
-0.050462864339351654,
-0.1120389774441719
]
|
56d24f98ef02f336b9f1a0f2fb8a85c96d7d0b1f |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage and Repository:** https://github.com/epfl-dlab/SynthIE
- **Paper:** https://arxiv.org/abs/2303.04132
### Dataset Summary
[Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction](https://arxiv.org/abs/2303.04132) builds on the idea that even for hard tasks of interest (with input X and Y) -- for which human-annotation is not practical and high-quality annotated data is not available -- by reversing the task (from Y to X), useful data can be synthetically generated even when that original task cannot be solved directly by the LLM. This process enables the creation of a high-quality dataset of X-Y pairs that will enable the training/fine-tuning of models for the original task of interest.
In particular, the paper studies the idea in the context of closed information extraction (IE), where a model is tasked with extracting the exhaustive set of facts expressed in natural language text. The synthetic data generation pipeline proposed in the paper comprises three primary components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure, see the [paper](https://arxiv.org/abs/2303.04132).
We used this pipeline to generate two large high-quality datasets:<br>
**SynthIE-code**: consisting of around 1.8M training, 10K validation, and 50K test samples generated with [code-davinci-002](https://platform.openai.com/docs/models/gpt-3-5) <br>
**SynthIE-text**: consisting of 10K validation and 50K test samples generated with [text-davinci-003](https://platform.openai.com/docs/models/gpt-3-5) <br>
The text for the validation and test data points in SynthIE-code and SynthIE-text corresponds to the same triplet sets.
The resulting data is then used to train [SynthIE](https://github.com/epfl-dlab/SynthIE), a series of T5-based versions of [GenIE](https://github.com/epfl-dlab/GenIE) -- a recently proposed autoregressive closed IE system; as well as to enable a more accurate evaluation. As a baseline, T5 versions of GenIE are trained on the same dataset, [REBEL](https://aclanthology.org/2021.findings-emnlp.204.pdf), as the original work. The (processed) version of this dataset, suitable for closed IE and used in the paper's experiments, is provided in this repository.
According to the human evaluation conducted in the paper, the synthetically generated data is substantially more faithful than the distantly supervised REBEL and contains around 15\% false negative (opposed to REBEL's 70\%) and 22\% false positive (opposed to REBEL's 56\%) annotations while uniformly covering all relations (see the paper for more details).
### Languages
To stay comparable to GenIE, [SynthIE](https://github.com/epfl-dlab/SynthIE) considers only English. Therefore, the text in SynthIE-code and SynthIE-text is generated in English only. However, the triplets' constituents come from WikiData and are language invariant. Therefore, triplet sets with labels for many languages can easily be obtained.
## Dataset Structure
The SynthIE meta-dataset actually comprises 3 datasets:
- **SynthIE-code** (`synthie_code`)
- **SynthIE-text** (`synthie_text`)
- **REBEL** (`rebel`)
**SynCode**
The samples in this dataset were generated with `code-davinci-002`.
| | Train | Valid | Test |
| ----- | ----- | ----- | ----- |
| Data Points | 1,815,378 | 10,000 | 50,286 |
| Triplets | 6,055,911 | 34,262 | 172,991 |
| Entities | 1,806,126 | 27,553 | 105,176 |
| Relations | 888 | 883 | 888 |
**SynthIE-text**
The samples in this dataset were generated with `text-davinci-003`.
| | Train | Valid | Test |
| ----- | ----- | ----- | ----- |
| Data Points | -- | 10,000 | 50,286 |
| Triplets | -- | 34,262 | 172,991 |
| Entities | -- | 27,553 | 105,176 |
| Relations | -- | 883 | 888 |
**REBEL**
The samples in this dataset are processed and further annotated from the already existing [REBEL](https://huggingface.co/datasets/Babelscape/rebel-dataset) dataset.
| | Train | Valid | Test |
| ----- | ----- | ----- | ----- |
| Data Points | 2,813,210 | 155,926 | 156,449 |
| Triplets | 7,187,915 | 397,326 | 398,252 |
| Entities | 2,038,741 | 205,080 | 205,549 |
| Relations | 1071 | 691 | 690 |
Note that REBEL is substantially more skewed than SynCode and SynthIE-text. Here are the relation frequency (in terms of data points) statistics for REBEL and SynCode.
| | min | 1st quantile | median | 3rd quantile | max |
| ----- | ----- | ----- | ----- | ----- | ----- |
| SynCode | 61 | 1043 | 1691 | 3944 | 499,783 |
| REBEL | 1 | 7 | 47 | 625 | 1,202,489 |
**SynCode/SynthIE-text/REBEL processed**
Additionally, we provide a processed version (that was used in the paper) of each dataset. The processing consists of pre-computations/pre-processing that were run to speed the data loading for the experiments. The key difference is that in the processed version of SynthIE-code and SynthIE-text, the target triplets are consistently ordered according to a heuristic detecting the constituent entities' appearance position in the text, with triplets corresponding to entities appearing earlier in the output linearization (cf. paper). The triplets for REBEL are ordered even in the "unprocessed version". To load the processed version of the dataset, add the suffix "_pc" to the original identifier (i.e., synthie_code_pc, synthie_text_pc, rebel_pc). The processing is performed by applying [this](https://github.com/epfl-dlab/SynthIE/blob/main/scripts/pre_computing.py) script on the original data.
### Data Fields
All of the datasets share the same schema. Here is a list of the fields paired with a description.
- `id`: A unique numeric identifier, starting from 0 for each dataset.
- `text`: A string expressing the text corresponding to this sample.
- `triplets`: A list of triplets that are expressed in the text. Each triplet corresponds to a dictionary
- `subject`: The subject refers to an entity. It is a dictionary of:
- `surfaceform`: A textual label corresponding to the title of the entity's English Wikipedia page
- `uri`: A string corresponding to the entity's WikiData identifier
- `relation`: The relation refers to a relation. It is a dictionary of:
- `surfaceform`: The textual label assigned to the WikiData item corresponding to the given relation.
- `uri`: A string corresponding to the relation's WikiData identifier
- `object`: Same as the subject, the object refers to an entity and corresponds to a dictionary with the same structure.
- `entities`: A list comprising all the entities expressed in the text (appearing as a subject or an object in any of the triplets). Each entity is expressed as a dictionary following the same structure as the `subject` and `object` entities in the triplet list.
- `relations`: A list comprising all the relations expressed in the text (appearing as the relation in any of the triplets). Each relation is expressed as a dictionary following the same structure as the `relation` in the triplet list.
Here is an example of a data point:
```
{'id': 1,
'text': 'The Journal of Colloid and Interface Science is a bibliographic '
'review indexed in Scopus and published by Elsevier. Its main subject '
'is chemical engineering, and it is written in the English language. '
'It is based in the United States, and is owned by Elsevier, the same '
'company that owns Scopus.',
'triplets': [{'subject': "{'surfaceform': "
"'Journal_of_Colloid_and_Interface_Science', 'uri': "
"'Q3902043'}",
'predicate': "{'surfaceform': 'indexed in bibliographic "
"review', 'uri': 'P8875'}",
'object': "{'surfaceform': 'Scopus', 'uri': 'Q371467'}"},
{'subject': "{'surfaceform': "
"'Journal_of_Colloid_and_Interface_Science', 'uri': "
"'Q3902043'}",
'predicate': "{'surfaceform': 'main subject', 'uri': 'P921'}",
'object': "{'surfaceform': 'Chemical_engineering', 'uri': "
"'Q83588'}"},
{'subject': "{'surfaceform': "
"'Journal_of_Colloid_and_Interface_Science', 'uri': "
"'Q3902043'}",
'predicate': "{'surfaceform': 'language of work or name', "
"'uri': 'P407'}",
'object': "{'surfaceform': 'English_language', 'uri': 'Q1860'}"},
{'subject': "{'surfaceform': "
"'Journal_of_Colloid_and_Interface_Science', 'uri': "
"'Q3902043'}",
'predicate': "{'surfaceform': 'publisher', 'uri': 'P123'}",
'object': "{'surfaceform': 'Elsevier', 'uri': 'Q746413'}"},
{'subject': "{'surfaceform': "
"'Journal_of_Colloid_and_Interface_Science', 'uri': "
"'Q3902043'}",
'predicate': "{'surfaceform': 'country of origin', 'uri': "
"'P495'}",
'object': "{'surfaceform': 'United_States', 'uri': 'Q30'}"},
{'subject': "{'surfaceform': 'Scopus', 'uri': 'Q371467'}",
'predicate': "{'surfaceform': 'owned by', 'uri': 'P127'}",
'object': "{'surfaceform': 'Elsevier', 'uri': 'Q746413'}"}],
'entities': [{'surfaceform': 'Journal_of_Colloid_and_Interface_Science',
'uri': 'Q3902043'},
{'surfaceform': 'Scopus', 'uri': 'Q371467'},
{'surfaceform': 'Chemical_engineering', 'uri': 'Q83588'},
{'surfaceform': 'English_language', 'uri': 'Q1860'},
{'surfaceform': 'Elsevier', 'uri': 'Q746413'},
{'surfaceform': 'United_States', 'uri': 'Q30'}],
'relations': [{'surfaceform': 'indexed in bibliographic review',
'uri': 'P8875'},
{'surfaceform': 'main subject', 'uri': 'P921'},
{'surfaceform': 'language of work or name', 'uri': 'P407'},
{'surfaceform': 'publisher', 'uri': 'P123'},
{'surfaceform': 'country of origin', 'uri': 'P495'},
{'surfaceform': 'owned by', 'uri': 'P127'}]}
```
### Data Splits
Each dataset (except SynthIE-text, which does not have a train set) has the same 4 splits:
- `train`
- `validation`
- `test`
- `test_small`
The first three are self-explanatory; the `test_small` split corresponds to a randomly sampled subset of the `test` split in which the IDs of the data points are kept the same as in the test set from which they were sampled (i.e., after the sampling IDs are not reset to 0 and resigned).
## Dataset Creation
Collecting datasets for the closed IE task is time-consuming, expensive, and even hardly feasible, as it requires annotators to know the entire entity and relation catalogs and reason about all possible facts expressed in the text. As a result, only small or noisy datasets exist. The only large dataset available, REBEL, suffers from several problems: (i) Noise: it is constructed based on distant supervision, and for many data points, the target set does not contain all the facts expressed in the text or is partially incorrect; (ii) Skewness: most relations appear only a few times in the dataset, resulting in models that ignore most of the information when used for training and poor estimates of performance when used for evaluation.
This dataset is constructed using a synthetic data generation pipeline, proposed in the paper [Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction](https://arxiv.org/abs/2303.04132), and serves as a use case for a task for which (i) high-quality annotated data is not available; (ii) human-annotation is not practical; (iii) the direct task (closed IE) is challenging for an LLM. Concretely, by reversing the task and generating the data in the opposite direction -- going from triplets to text -- high-quality useful data can be generated. The pipeline used to construct the dataset comprises three components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure and considerations for using the data, see the "Synthetic Data Generation", "Discussion", and "Limitations" sections of the [paper](https://arxiv.org/abs/2303.04132).
## Additional Information
### Licensing Information
The dataset is licensed under the terms of the MIT license.
### Citation Information
```
@article{josifoski2023exploiting,
title={Exploiting Asymmetry for Synthetic Training Data Generation: {S}ynth{IE} and The Case of Information Extraction},
author={Josifoski, Martin and Sakota, Marija and Peyrard, Maxime and West, Robert},
journal={arXiv preprint arXiv:2303.04132},
year={2023}
}
```
| martinjosifoski/SynthIE | [
"language:en",
"license:mit",
"arxiv:2303.04132",
"region:us"
]
| 2023-03-03T12:15:35+00:00 | {"language": ["en"], "license": "mit", "pretty_name": "SynthIE"} | 2023-03-06T21:59:52+00:00 | [
"2303.04132"
]
| [
"en"
]
| TAGS
#language-English #license-mit #arxiv-2303.04132 #region-us
| Dataset Card for Dataset Name
=============================
Dataset Description
-------------------
* Homepage and Repository: URL
* Paper: URL
### Dataset Summary
Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction builds on the idea that even for hard tasks of interest (with input X and Y) -- for which human-annotation is not practical and high-quality annotated data is not available -- by reversing the task (from Y to X), useful data can be synthetically generated even when that original task cannot be solved directly by the LLM. This process enables the creation of a high-quality dataset of X-Y pairs that will enable the training/fine-tuning of models for the original task of interest.
In particular, the paper studies the idea in the context of closed information extraction (IE), where a model is tasked with extracting the exhaustive set of facts expressed in natural language text. The synthetic data generation pipeline proposed in the paper comprises three primary components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure, see the paper.
We used this pipeline to generate two large high-quality datasets:
SynthIE-code: consisting of around 1.8M training, 10K validation, and 50K test samples generated with code-davinci-002
SynthIE-text: consisting of 10K validation and 50K test samples generated with text-davinci-003
The text for the validation and test data points in SynthIE-code and SynthIE-text corresponds to the same triplet sets.
The resulting data is then used to train SynthIE, a series of T5-based versions of GenIE -- a recently proposed autoregressive closed IE system; as well as to enable a more accurate evaluation. As a baseline, T5 versions of GenIE are trained on the same dataset, REBEL, as the original work. The (processed) version of this dataset, suitable for closed IE and used in the paper's experiments, is provided in this repository.
According to the human evaluation conducted in the paper, the synthetically generated data is substantially more faithful than the distantly supervised REBEL and contains around 15% false negative (opposed to REBEL's 70%) and 22% false positive (opposed to REBEL's 56%) annotations while uniformly covering all relations (see the paper for more details).
### Languages
To stay comparable to GenIE, SynthIE considers only English. Therefore, the text in SynthIE-code and SynthIE-text is generated in English only. However, the triplets' constituents come from WikiData and are language invariant. Therefore, triplet sets with labels for many languages can easily be obtained.
Dataset Structure
-----------------
The SynthIE meta-dataset actually comprises 3 datasets:
* SynthIE-code ('synthie\_code')
* SynthIE-text ('synthie\_text')
* REBEL ('rebel')
SynCode
The samples in this dataset were generated with 'code-davinci-002'.
SynthIE-text
The samples in this dataset were generated with 'text-davinci-003'.
REBEL
The samples in this dataset are processed and further annotated from the already existing REBEL dataset.
Note that REBEL is substantially more skewed than SynCode and SynthIE-text. Here are the relation frequency (in terms of data points) statistics for REBEL and SynCode.
SynCode/SynthIE-text/REBEL processed
Additionally, we provide a processed version (that was used in the paper) of each dataset. The processing consists of pre-computations/pre-processing that were run to speed the data loading for the experiments. The key difference is that in the processed version of SynthIE-code and SynthIE-text, the target triplets are consistently ordered according to a heuristic detecting the constituent entities' appearance position in the text, with triplets corresponding to entities appearing earlier in the output linearization (cf. paper). The triplets for REBEL are ordered even in the "unprocessed version". To load the processed version of the dataset, add the suffix "\_pc" to the original identifier (i.e., synthie\_code\_pc, synthie\_text\_pc, rebel\_pc). The processing is performed by applying this script on the original data.
### Data Fields
All of the datasets share the same schema. Here is a list of the fields paired with a description.
* 'id': A unique numeric identifier, starting from 0 for each dataset.
* 'text': A string expressing the text corresponding to this sample.
* 'triplets': A list of triplets that are expressed in the text. Each triplet corresponds to a dictionary
+ 'subject': The subject refers to an entity. It is a dictionary of:
- 'surfaceform': A textual label corresponding to the title of the entity's English Wikipedia page
- 'uri': A string corresponding to the entity's WikiData identifier
+ 'relation': The relation refers to a relation. It is a dictionary of:
- 'surfaceform': The textual label assigned to the WikiData item corresponding to the given relation.
- 'uri': A string corresponding to the relation's WikiData identifier
+ 'object': Same as the subject, the object refers to an entity and corresponds to a dictionary with the same structure.
* 'entities': A list comprising all the entities expressed in the text (appearing as a subject or an object in any of the triplets). Each entity is expressed as a dictionary following the same structure as the 'subject' and 'object' entities in the triplet list.
* 'relations': A list comprising all the relations expressed in the text (appearing as the relation in any of the triplets). Each relation is expressed as a dictionary following the same structure as the 'relation' in the triplet list.
Here is an example of a data point:
### Data Splits
Each dataset (except SynthIE-text, which does not have a train set) has the same 4 splits:
* 'train'
* 'validation'
* 'test'
* 'test\_small'
The first three are self-explanatory; the 'test\_small' split corresponds to a randomly sampled subset of the 'test' split in which the IDs of the data points are kept the same as in the test set from which they were sampled (i.e., after the sampling IDs are not reset to 0 and resigned).
Dataset Creation
----------------
Collecting datasets for the closed IE task is time-consuming, expensive, and even hardly feasible, as it requires annotators to know the entire entity and relation catalogs and reason about all possible facts expressed in the text. As a result, only small or noisy datasets exist. The only large dataset available, REBEL, suffers from several problems: (i) Noise: it is constructed based on distant supervision, and for many data points, the target set does not contain all the facts expressed in the text or is partially incorrect; (ii) Skewness: most relations appear only a few times in the dataset, resulting in models that ignore most of the information when used for training and poor estimates of performance when used for evaluation.
This dataset is constructed using a synthetic data generation pipeline, proposed in the paper Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction, and serves as a use case for a task for which (i) high-quality annotated data is not available; (ii) human-annotation is not practical; (iii) the direct task (closed IE) is challenging for an LLM. Concretely, by reversing the task and generating the data in the opposite direction -- going from triplets to text -- high-quality useful data can be generated. The pipeline used to construct the dataset comprises three components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure and considerations for using the data, see the "Synthetic Data Generation", "Discussion", and "Limitations" sections of the paper.
Additional Information
----------------------
### Licensing Information
The dataset is licensed under the terms of the MIT license.
| [
"### Dataset Summary\n\n\nExploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction builds on the idea that even for hard tasks of interest (with input X and Y) -- for which human-annotation is not practical and high-quality annotated data is not available -- by reversing the task (from Y to X), useful data can be synthetically generated even when that original task cannot be solved directly by the LLM. This process enables the creation of a high-quality dataset of X-Y pairs that will enable the training/fine-tuning of models for the original task of interest.\nIn particular, the paper studies the idea in the context of closed information extraction (IE), where a model is tasked with extracting the exhaustive set of facts expressed in natural language text. The synthetic data generation pipeline proposed in the paper comprises three primary components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure, see the paper.\n\n\nWe used this pipeline to generate two large high-quality datasets: \n\nSynthIE-code: consisting of around 1.8M training, 10K validation, and 50K test samples generated with code-davinci-002 \n\nSynthIE-text: consisting of 10K validation and 50K test samples generated with text-davinci-003 \n\nThe text for the validation and test data points in SynthIE-code and SynthIE-text corresponds to the same triplet sets.\n\n\nThe resulting data is then used to train SynthIE, a series of T5-based versions of GenIE -- a recently proposed autoregressive closed IE system; as well as to enable a more accurate evaluation. As a baseline, T5 versions of GenIE are trained on the same dataset, REBEL, as the original work. The (processed) version of this dataset, suitable for closed IE and used in the paper's experiments, is provided in this repository.\nAccording to the human evaluation conducted in the paper, the synthetically generated data is substantially more faithful than the distantly supervised REBEL and contains around 15% false negative (opposed to REBEL's 70%) and 22% false positive (opposed to REBEL's 56%) annotations while uniformly covering all relations (see the paper for more details).",
"### Languages\n\n\nTo stay comparable to GenIE, SynthIE considers only English. Therefore, the text in SynthIE-code and SynthIE-text is generated in English only. However, the triplets' constituents come from WikiData and are language invariant. Therefore, triplet sets with labels for many languages can easily be obtained.\n\n\nDataset Structure\n-----------------\n\n\nThe SynthIE meta-dataset actually comprises 3 datasets:\n\n\n* SynthIE-code ('synthie\\_code')\n* SynthIE-text ('synthie\\_text')\n* REBEL ('rebel')\n\n\nSynCode\n\n\nThe samples in this dataset were generated with 'code-davinci-002'.\n\n\n\nSynthIE-text\n\n\nThe samples in this dataset were generated with 'text-davinci-003'.\n\n\n\nREBEL\n\n\nThe samples in this dataset are processed and further annotated from the already existing REBEL dataset.\n\n\n\nNote that REBEL is substantially more skewed than SynCode and SynthIE-text. Here are the relation frequency (in terms of data points) statistics for REBEL and SynCode.\n\n\n\nSynCode/SynthIE-text/REBEL processed\n\n\nAdditionally, we provide a processed version (that was used in the paper) of each dataset. The processing consists of pre-computations/pre-processing that were run to speed the data loading for the experiments. The key difference is that in the processed version of SynthIE-code and SynthIE-text, the target triplets are consistently ordered according to a heuristic detecting the constituent entities' appearance position in the text, with triplets corresponding to entities appearing earlier in the output linearization (cf. paper). The triplets for REBEL are ordered even in the \"unprocessed version\". To load the processed version of the dataset, add the suffix \"\\_pc\" to the original identifier (i.e., synthie\\_code\\_pc, synthie\\_text\\_pc, rebel\\_pc). The processing is performed by applying this script on the original data.",
"### Data Fields\n\n\nAll of the datasets share the same schema. Here is a list of the fields paired with a description.\n\n\n* 'id': A unique numeric identifier, starting from 0 for each dataset.\n* 'text': A string expressing the text corresponding to this sample.\n* 'triplets': A list of triplets that are expressed in the text. Each triplet corresponds to a dictionary\n\t+ 'subject': The subject refers to an entity. It is a dictionary of:\n\t\t- 'surfaceform': A textual label corresponding to the title of the entity's English Wikipedia page\n\t\t- 'uri': A string corresponding to the entity's WikiData identifier\n\t+ 'relation': The relation refers to a relation. It is a dictionary of:\n\t\t- 'surfaceform': The textual label assigned to the WikiData item corresponding to the given relation.\n\t\t- 'uri': A string corresponding to the relation's WikiData identifier\n\t+ 'object': Same as the subject, the object refers to an entity and corresponds to a dictionary with the same structure.\n* 'entities': A list comprising all the entities expressed in the text (appearing as a subject or an object in any of the triplets). Each entity is expressed as a dictionary following the same structure as the 'subject' and 'object' entities in the triplet list.\n* 'relations': A list comprising all the relations expressed in the text (appearing as the relation in any of the triplets). Each relation is expressed as a dictionary following the same structure as the 'relation' in the triplet list.\n\n\nHere is an example of a data point:",
"### Data Splits\n\n\nEach dataset (except SynthIE-text, which does not have a train set) has the same 4 splits:\n\n\n* 'train'\n* 'validation'\n* 'test'\n* 'test\\_small'\n\n\nThe first three are self-explanatory; the 'test\\_small' split corresponds to a randomly sampled subset of the 'test' split in which the IDs of the data points are kept the same as in the test set from which they were sampled (i.e., after the sampling IDs are not reset to 0 and resigned).\n\n\nDataset Creation\n----------------\n\n\nCollecting datasets for the closed IE task is time-consuming, expensive, and even hardly feasible, as it requires annotators to know the entire entity and relation catalogs and reason about all possible facts expressed in the text. As a result, only small or noisy datasets exist. The only large dataset available, REBEL, suffers from several problems: (i) Noise: it is constructed based on distant supervision, and for many data points, the target set does not contain all the facts expressed in the text or is partially incorrect; (ii) Skewness: most relations appear only a few times in the dataset, resulting in models that ignore most of the information when used for training and poor estimates of performance when used for evaluation.\n\n\nThis dataset is constructed using a synthetic data generation pipeline, proposed in the paper Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction, and serves as a use case for a task for which (i) high-quality annotated data is not available; (ii) human-annotation is not practical; (iii) the direct task (closed IE) is challenging for an LLM. Concretely, by reversing the task and generating the data in the opposite direction -- going from triplets to text -- high-quality useful data can be generated. The pipeline used to construct the dataset comprises three components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure and considerations for using the data, see the \"Synthetic Data Generation\", \"Discussion\", and \"Limitations\" sections of the paper.\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThe dataset is licensed under the terms of the MIT license."
]
| [
"TAGS\n#language-English #license-mit #arxiv-2303.04132 #region-us \n",
"### Dataset Summary\n\n\nExploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction builds on the idea that even for hard tasks of interest (with input X and Y) -- for which human-annotation is not practical and high-quality annotated data is not available -- by reversing the task (from Y to X), useful data can be synthetically generated even when that original task cannot be solved directly by the LLM. This process enables the creation of a high-quality dataset of X-Y pairs that will enable the training/fine-tuning of models for the original task of interest.\nIn particular, the paper studies the idea in the context of closed information extraction (IE), where a model is tasked with extracting the exhaustive set of facts expressed in natural language text. The synthetic data generation pipeline proposed in the paper comprises three primary components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure, see the paper.\n\n\nWe used this pipeline to generate two large high-quality datasets: \n\nSynthIE-code: consisting of around 1.8M training, 10K validation, and 50K test samples generated with code-davinci-002 \n\nSynthIE-text: consisting of 10K validation and 50K test samples generated with text-davinci-003 \n\nThe text for the validation and test data points in SynthIE-code and SynthIE-text corresponds to the same triplet sets.\n\n\nThe resulting data is then used to train SynthIE, a series of T5-based versions of GenIE -- a recently proposed autoregressive closed IE system; as well as to enable a more accurate evaluation. As a baseline, T5 versions of GenIE are trained on the same dataset, REBEL, as the original work. The (processed) version of this dataset, suitable for closed IE and used in the paper's experiments, is provided in this repository.\nAccording to the human evaluation conducted in the paper, the synthetically generated data is substantially more faithful than the distantly supervised REBEL and contains around 15% false negative (opposed to REBEL's 70%) and 22% false positive (opposed to REBEL's 56%) annotations while uniformly covering all relations (see the paper for more details).",
"### Languages\n\n\nTo stay comparable to GenIE, SynthIE considers only English. Therefore, the text in SynthIE-code and SynthIE-text is generated in English only. However, the triplets' constituents come from WikiData and are language invariant. Therefore, triplet sets with labels for many languages can easily be obtained.\n\n\nDataset Structure\n-----------------\n\n\nThe SynthIE meta-dataset actually comprises 3 datasets:\n\n\n* SynthIE-code ('synthie\\_code')\n* SynthIE-text ('synthie\\_text')\n* REBEL ('rebel')\n\n\nSynCode\n\n\nThe samples in this dataset were generated with 'code-davinci-002'.\n\n\n\nSynthIE-text\n\n\nThe samples in this dataset were generated with 'text-davinci-003'.\n\n\n\nREBEL\n\n\nThe samples in this dataset are processed and further annotated from the already existing REBEL dataset.\n\n\n\nNote that REBEL is substantially more skewed than SynCode and SynthIE-text. Here are the relation frequency (in terms of data points) statistics for REBEL and SynCode.\n\n\n\nSynCode/SynthIE-text/REBEL processed\n\n\nAdditionally, we provide a processed version (that was used in the paper) of each dataset. The processing consists of pre-computations/pre-processing that were run to speed the data loading for the experiments. The key difference is that in the processed version of SynthIE-code and SynthIE-text, the target triplets are consistently ordered according to a heuristic detecting the constituent entities' appearance position in the text, with triplets corresponding to entities appearing earlier in the output linearization (cf. paper). The triplets for REBEL are ordered even in the \"unprocessed version\". To load the processed version of the dataset, add the suffix \"\\_pc\" to the original identifier (i.e., synthie\\_code\\_pc, synthie\\_text\\_pc, rebel\\_pc). The processing is performed by applying this script on the original data.",
"### Data Fields\n\n\nAll of the datasets share the same schema. Here is a list of the fields paired with a description.\n\n\n* 'id': A unique numeric identifier, starting from 0 for each dataset.\n* 'text': A string expressing the text corresponding to this sample.\n* 'triplets': A list of triplets that are expressed in the text. Each triplet corresponds to a dictionary\n\t+ 'subject': The subject refers to an entity. It is a dictionary of:\n\t\t- 'surfaceform': A textual label corresponding to the title of the entity's English Wikipedia page\n\t\t- 'uri': A string corresponding to the entity's WikiData identifier\n\t+ 'relation': The relation refers to a relation. It is a dictionary of:\n\t\t- 'surfaceform': The textual label assigned to the WikiData item corresponding to the given relation.\n\t\t- 'uri': A string corresponding to the relation's WikiData identifier\n\t+ 'object': Same as the subject, the object refers to an entity and corresponds to a dictionary with the same structure.\n* 'entities': A list comprising all the entities expressed in the text (appearing as a subject or an object in any of the triplets). Each entity is expressed as a dictionary following the same structure as the 'subject' and 'object' entities in the triplet list.\n* 'relations': A list comprising all the relations expressed in the text (appearing as the relation in any of the triplets). Each relation is expressed as a dictionary following the same structure as the 'relation' in the triplet list.\n\n\nHere is an example of a data point:",
"### Data Splits\n\n\nEach dataset (except SynthIE-text, which does not have a train set) has the same 4 splits:\n\n\n* 'train'\n* 'validation'\n* 'test'\n* 'test\\_small'\n\n\nThe first three are self-explanatory; the 'test\\_small' split corresponds to a randomly sampled subset of the 'test' split in which the IDs of the data points are kept the same as in the test set from which they were sampled (i.e., after the sampling IDs are not reset to 0 and resigned).\n\n\nDataset Creation\n----------------\n\n\nCollecting datasets for the closed IE task is time-consuming, expensive, and even hardly feasible, as it requires annotators to know the entire entity and relation catalogs and reason about all possible facts expressed in the text. As a result, only small or noisy datasets exist. The only large dataset available, REBEL, suffers from several problems: (i) Noise: it is constructed based on distant supervision, and for many data points, the target set does not contain all the facts expressed in the text or is partially incorrect; (ii) Skewness: most relations appear only a few times in the dataset, resulting in models that ignore most of the information when used for training and poor estimates of performance when used for evaluation.\n\n\nThis dataset is constructed using a synthetic data generation pipeline, proposed in the paper Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction, and serves as a use case for a task for which (i) high-quality annotated data is not available; (ii) human-annotation is not practical; (iii) the direct task (closed IE) is challenging for an LLM. Concretely, by reversing the task and generating the data in the opposite direction -- going from triplets to text -- high-quality useful data can be generated. The pipeline used to construct the dataset comprises three components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure and considerations for using the data, see the \"Synthetic Data Generation\", \"Discussion\", and \"Limitations\" sections of the paper.\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThe dataset is licensed under the terms of the MIT license."
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488,
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"passage: TAGS\n#language-English #license-mit #arxiv-2303.04132 #region-us \n",
"passage: ### Dataset Summary\n\n\nExploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction builds on the idea that even for hard tasks of interest (with input X and Y) -- for which human-annotation is not practical and high-quality annotated data is not available -- by reversing the task (from Y to X), useful data can be synthetically generated even when that original task cannot be solved directly by the LLM. This process enables the creation of a high-quality dataset of X-Y pairs that will enable the training/fine-tuning of models for the original task of interest.\nIn particular, the paper studies the idea in the context of closed information extraction (IE), where a model is tasked with extracting the exhaustive set of facts expressed in natural language text. The synthetic data generation pipeline proposed in the paper comprises three primary components: (i) construction of a knowledge graph containing the entities and relations of interest; (ii) sampling of coherent triplet sets from the KG with comprehensive coverage of the entities and relations, and (iii) generation of high-quality text, expressing the triplets without any supplementary information. For more details regarding the dataset construction procedure, see the paper.\n\n\nWe used this pipeline to generate two large high-quality datasets: \n\nSynthIE-code: consisting of around 1.8M training, 10K validation, and 50K test samples generated with code-davinci-002 \n\nSynthIE-text: consisting of 10K validation and 50K test samples generated with text-davinci-003 \n\nThe text for the validation and test data points in SynthIE-code and SynthIE-text corresponds to the same triplet sets.\n\n\nThe resulting data is then used to train SynthIE, a series of T5-based versions of GenIE -- a recently proposed autoregressive closed IE system; as well as to enable a more accurate evaluation. As a baseline, T5 versions of GenIE are trained on the same dataset, REBEL, as the original work. The (processed) version of this dataset, suitable for closed IE and used in the paper's experiments, is provided in this repository.\nAccording to the human evaluation conducted in the paper, the synthetically generated data is substantially more faithful than the distantly supervised REBEL and contains around 15% false negative (opposed to REBEL's 70%) and 22% false positive (opposed to REBEL's 56%) annotations while uniformly covering all relations (see the paper for more details).### Languages\n\n\nTo stay comparable to GenIE, SynthIE considers only English. Therefore, the text in SynthIE-code and SynthIE-text is generated in English only. However, the triplets' constituents come from WikiData and are language invariant. Therefore, triplet sets with labels for many languages can easily be obtained.\n\n\nDataset Structure\n-----------------\n\n\nThe SynthIE meta-dataset actually comprises 3 datasets:\n\n\n* SynthIE-code ('synthie\\_code')\n* SynthIE-text ('synthie\\_text')\n* REBEL ('rebel')\n\n\nSynCode\n\n\nThe samples in this dataset were generated with 'code-davinci-002'.\n\n\n\nSynthIE-text\n\n\nThe samples in this dataset were generated with 'text-davinci-003'.\n\n\n\nREBEL\n\n\nThe samples in this dataset are processed and further annotated from the already existing REBEL dataset.\n\n\n\nNote that REBEL is substantially more skewed than SynCode and SynthIE-text. Here are the relation frequency (in terms of data points) statistics for REBEL and SynCode.\n\n\n\nSynCode/SynthIE-text/REBEL processed\n\n\nAdditionally, we provide a processed version (that was used in the paper) of each dataset. The processing consists of pre-computations/pre-processing that were run to speed the data loading for the experiments. The key difference is that in the processed version of SynthIE-code and SynthIE-text, the target triplets are consistently ordered according to a heuristic detecting the constituent entities' appearance position in the text, with triplets corresponding to entities appearing earlier in the output linearization (cf. paper). The triplets for REBEL are ordered even in the \"unprocessed version\". To load the processed version of the dataset, add the suffix \"\\_pc\" to the original identifier (i.e., synthie\\_code\\_pc, synthie\\_text\\_pc, rebel\\_pc). The processing is performed by applying this script on the original data.",
"passage: ### Data Fields\n\n\nAll of the datasets share the same schema. Here is a list of the fields paired with a description.\n\n\n* 'id': A unique numeric identifier, starting from 0 for each dataset.\n* 'text': A string expressing the text corresponding to this sample.\n* 'triplets': A list of triplets that are expressed in the text. Each triplet corresponds to a dictionary\n\t+ 'subject': The subject refers to an entity. It is a dictionary of:\n\t\t- 'surfaceform': A textual label corresponding to the title of the entity's English Wikipedia page\n\t\t- 'uri': A string corresponding to the entity's WikiData identifier\n\t+ 'relation': The relation refers to a relation. It is a dictionary of:\n\t\t- 'surfaceform': The textual label assigned to the WikiData item corresponding to the given relation.\n\t\t- 'uri': A string corresponding to the relation's WikiData identifier\n\t+ 'object': Same as the subject, the object refers to an entity and corresponds to a dictionary with the same structure.\n* 'entities': A list comprising all the entities expressed in the text (appearing as a subject or an object in any of the triplets). Each entity is expressed as a dictionary following the same structure as the 'subject' and 'object' entities in the triplet list.\n* 'relations': A list comprising all the relations expressed in the text (appearing as the relation in any of the triplets). Each relation is expressed as a dictionary following the same structure as the 'relation' in the triplet list.\n\n\nHere is an example of a data point:"
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|
512349b5efea5df7b2a2d0d3f675337ab3efcc7b | # Dataset Card for "spectrogram_data_ultramax_music_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | deepak-newzera/spectrogram_data_ultramax_music_dataset | [
"region:us"
]
| 2023-03-03T12:35:41+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio_file", "dtype": "string"}, {"name": "slice", "dtype": "int16"}], "splits": [{"name": "train", "num_bytes": 1989273676.625, "num_examples": 45571}], "download_size": 1984371263, "dataset_size": 1989273676.625}} | 2023-03-03T12:46:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "spectrogram_data_ultramax_music_dataset"
More Information needed | [
"# Dataset Card for \"spectrogram_data_ultramax_music_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"spectrogram_data_ultramax_music_dataset\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"spectrogram_data_ultramax_music_dataset\"\n\nMore Information needed"
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|
4771b94ba2f5b14d7c3fbbd898da0c248115256c | 傻逼autodl上传一半就寄 | Wangs-official/sb | [
"region:us"
]
| 2023-03-03T12:45:43+00:00 | {} | 2023-04-02T12:52:16+00:00 | []
| []
| TAGS
#region-us
| 傻逼autodl上传一半就寄 | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
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|
522a4c303e6b2d7b3f6c65124704969cac66c64d | # This is an helper script to load an html enterprise dataset into a datasets object
## How to use
1. Download a NS0 dump at https://dumps.wikimedia.org/other/enterprise_html/runs/20230220/
2. Untar it
For example with:
```
mkdir enwiki-NS6-20230220-ENTERPRISE-HTML
tar -I pigz -vxf enwiki-NS6-20230220-ENTERPRISE-HTML.json.tar.gz -C enwiki-NS6-20230220-ENTERPRISE-HTML
```
3. Load it:
```python
from datasets import load_dataset
local_path=... # Path to directory where you extracted the NS0 dump
shard_id=...
ds = load_dataset(
"SaulLu/wikipedia_html_enterprise",
shard=shard_id,
data_dir=local_path
)
``` | SaulLu/wikipedia_html_enterprise | [
"region:us"
]
| 2023-03-03T13:08:06+00:00 | {} | 2023-03-03T13:37:16+00:00 | []
| []
| TAGS
#region-us
| # This is an helper script to load an html enterprise dataset into a datasets object
## How to use
1. Download a NS0 dump at URL
2. Untar it
For example with:
3. Load it:
| [
"# This is an helper script to load an html enterprise dataset into a datasets object",
"## How to use\n\n1. Download a NS0 dump at URL\n\n2. Untar it \n\nFor example with:\n\n\n3. Load it:"
]
| [
"TAGS\n#region-us \n",
"# This is an helper script to load an html enterprise dataset into a datasets object",
"## How to use\n\n1. Download a NS0 dump at URL\n\n2. Untar it \n\nFor example with:\n\n\n3. Load it:"
]
| [
6,
21,
26
]
| [
"passage: TAGS\n#region-us \n# This is an helper script to load an html enterprise dataset into a datasets object## How to use\n\n1. Download a NS0 dump at URL\n\n2. Untar it \n\nFor example with:\n\n\n3. Load it:"
]
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|
91533f8887f1cb50e27507d2ac3900329265fd02 |
# Dataset card for Instruct Me
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Lewis Tunstall
### Dataset summary
Instruct Me is a dataset of prompts and instruction dialogues between a human user and AI assistant. The prompts are derived from (prompt, completion) pairs in the [Helpful Instructions dataset](https://huggingface.co/datasets/HuggingFaceH4/helpful_instructions). The goal is to train a language model to that is "chatty" and can answer the kind of questions or tasks a human user might instruct an AI assistant to perform.
### Supported Tasks and Leaderboard
We provide 3 configs that can be used for training RLHF models:
#### instruction_tuning
Single-turn user/bot dialogues for instruction tuning.
#### reward_modeling
Prompts to generate model completions and collect human preference data
#### ppo
Prompts to generate model completions for optimization of the instruction-tuned model with techniques like PPO.
### Changelog
* March 6, 2023: `v1.1.0` release. Changed the `text` columns for the `reward_modeling` and `ppo` configs to `prompt` for consistency with our dataset schemas elsewhere.
* March 5, 2023: `v1.0.0` release. | HuggingFaceH4/instruct_me | [
"task_categories:conversational",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"human-feedback",
"instruct",
"reward-modeling",
"region:us"
]
| 2023-03-03T13:43:15+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["conversational", "text-generation"], "pretty_name": "Instruct Me", "dataset_info": [{"config_name": "instruction_tuning", "features": [{"name": "text", "dtype": "string"}, {"name": "meta", "struct": [{"name": "source", "dtype": "string"}, {"name": "config", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 29975565, "num_examples": 41685}, {"name": "test", "num_bytes": 3298059, "num_examples": 4632}], "download_size": 18425612, "dataset_size": 33273624}, {"config_name": "reward_modelling", "features": [{"name": "text", "dtype": "string"}, {"name": "meta", "struct": [{"name": "source", "dtype": "string"}, {"name": "config", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 25274204, "num_examples": 41685}, {"name": "test", "num_bytes": 2777314, "num_examples": 4632}], "download_size": 15636566, "dataset_size": 28051518}, {"config_name": "ppo", "features": [{"name": "prompt", "dtype": "string"}, {"name": "meta", "struct": [{"name": "source", "dtype": "string"}, {"name": "config", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 50787070, "num_examples": 83371}, {"name": "test", "num_bytes": 5715727, "num_examples": 9264}], "download_size": 31461165, "dataset_size": 56502797}, {"config_name": "reward_modeling", "features": [{"name": "prompt", "dtype": "string"}, {"name": "meta", "struct": [{"name": "source", "dtype": "string"}, {"name": "config", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 25274204, "num_examples": 41685}, {"name": "test", "num_bytes": 2777314, "num_examples": 4632}], "download_size": 15636838, "dataset_size": 28051518}], "tags": ["human-feedback", "instruct", "reward-modeling"]} | 2023-03-06T08:36:03+00:00 | []
| [
"en"
]
| TAGS
#task_categories-conversational #task_categories-text-generation #language-English #license-apache-2.0 #human-feedback #instruct #reward-modeling #region-us
|
# Dataset card for Instruct Me
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact: Lewis Tunstall
### Dataset summary
Instruct Me is a dataset of prompts and instruction dialogues between a human user and AI assistant. The prompts are derived from (prompt, completion) pairs in the Helpful Instructions dataset. The goal is to train a language model to that is "chatty" and can answer the kind of questions or tasks a human user might instruct an AI assistant to perform.
### Supported Tasks and Leaderboard
We provide 3 configs that can be used for training RLHF models:
#### instruction_tuning
Single-turn user/bot dialogues for instruction tuning.
#### reward_modeling
Prompts to generate model completions and collect human preference data
#### ppo
Prompts to generate model completions for optimization of the instruction-tuned model with techniques like PPO.
### Changelog
* March 6, 2023: 'v1.1.0' release. Changed the 'text' columns for the 'reward_modeling' and 'ppo' configs to 'prompt' for consistency with our dataset schemas elsewhere.
* March 5, 2023: 'v1.0.0' release. | [
"# Dataset card for Instruct Me",
"## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact: Lewis Tunstall",
"### Dataset summary\n\nInstruct Me is a dataset of prompts and instruction dialogues between a human user and AI assistant. The prompts are derived from (prompt, completion) pairs in the Helpful Instructions dataset. The goal is to train a language model to that is \"chatty\" and can answer the kind of questions or tasks a human user might instruct an AI assistant to perform.",
"### Supported Tasks and Leaderboard\n\nWe provide 3 configs that can be used for training RLHF models:",
"#### instruction_tuning\n\nSingle-turn user/bot dialogues for instruction tuning.",
"#### reward_modeling\n\nPrompts to generate model completions and collect human preference data",
"#### ppo\n\nPrompts to generate model completions for optimization of the instruction-tuned model with techniques like PPO.",
"### Changelog\n\n* March 6, 2023: 'v1.1.0' release. Changed the 'text' columns for the 'reward_modeling' and 'ppo' configs to 'prompt' for consistency with our dataset schemas elsewhere.\n* March 5, 2023: 'v1.0.0' release."
]
| [
"TAGS\n#task_categories-conversational #task_categories-text-generation #language-English #license-apache-2.0 #human-feedback #instruct #reward-modeling #region-us \n",
"# Dataset card for Instruct Me",
"## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact: Lewis Tunstall",
"### Dataset summary\n\nInstruct Me is a dataset of prompts and instruction dialogues between a human user and AI assistant. The prompts are derived from (prompt, completion) pairs in the Helpful Instructions dataset. The goal is to train a language model to that is \"chatty\" and can answer the kind of questions or tasks a human user might instruct an AI assistant to perform.",
"### Supported Tasks and Leaderboard\n\nWe provide 3 configs that can be used for training RLHF models:",
"#### instruction_tuning\n\nSingle-turn user/bot dialogues for instruction tuning.",
"#### reward_modeling\n\nPrompts to generate model completions and collect human preference data",
"#### ppo\n\nPrompts to generate model completions for optimization of the instruction-tuned model with techniques like PPO.",
"### Changelog\n\n* March 6, 2023: 'v1.1.0' release. Changed the 'text' columns for the 'reward_modeling' and 'ppo' configs to 'prompt' for consistency with our dataset schemas elsewhere.\n* March 5, 2023: 'v1.0.0' release."
]
| [
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8,
27,
92,
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22,
19,
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74
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| [
"passage: TAGS\n#task_categories-conversational #task_categories-text-generation #language-English #license-apache-2.0 #human-feedback #instruct #reward-modeling #region-us \n# Dataset card for Instruct Me## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact: Lewis Tunstall### Dataset summary\n\nInstruct Me is a dataset of prompts and instruction dialogues between a human user and AI assistant. The prompts are derived from (prompt, completion) pairs in the Helpful Instructions dataset. The goal is to train a language model to that is \"chatty\" and can answer the kind of questions or tasks a human user might instruct an AI assistant to perform.### Supported Tasks and Leaderboard\n\nWe provide 3 configs that can be used for training RLHF models:#### instruction_tuning\n\nSingle-turn user/bot dialogues for instruction tuning.#### reward_modeling\n\nPrompts to generate model completions and collect human preference data#### ppo\n\nPrompts to generate model completions for optimization of the instruction-tuned model with techniques like PPO.### Changelog\n\n* March 6, 2023: 'v1.1.0' release. Changed the 'text' columns for the 'reward_modeling' and 'ppo' configs to 'prompt' for consistency with our dataset schemas elsewhere.\n* March 5, 2023: 'v1.0.0' release."
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|
994806b8d7350e64dc222c31fc851254184b6dcb | # Dataset Card for "Adv-small-persian-QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Hamid-reza/Adv-small-persian-QA | [
"region:us"
]
| 2023-03-03T14:02:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 2728312, "num_examples": 1261}, {"name": "validation", "num_bytes": 276911, "num_examples": 130}], "download_size": 1500971, "dataset_size": 3005223}} | 2023-03-03T14:02:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Adv-small-persian-QA"
More Information needed | [
"# Dataset Card for \"Adv-small-persian-QA\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Adv-small-persian-QA\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Adv-small-persian-QA\"\n\nMore Information needed"
]
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|
541037ac9e8bfd08dc155f616080a4611773874e | # Dataset Card for "super-mario-bros-levels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | valashir/super-mario-bros-levels | [
"region:us"
]
| 2023-03-03T14:15:06+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "preprocessed_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1698876.75, "num_examples": 2098}], "download_size": 849061, "dataset_size": 1698876.75}} | 2023-03-03T14:20:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "super-mario-bros-levels"
More Information needed | [
"# Dataset Card for \"super-mario-bros-levels\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"super-mario-bros-levels\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"super-mario-bros-levels\"\n\nMore Information needed"
]
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70fe061e3f1cb2b53a5cb064c23cc0b3691b0602 | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### 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
[More Information Needed]
### Contributions
[More Information Needed] | Buckzor/GIT-TES-LEGENDS | [
"task_categories:text-classification",
"region:us"
]
| 2023-03-03T14:21:32+00:00 | {"task_categories": ["text-classification"]} | 2023-03-03T14:28:00+00:00 | []
| []
| TAGS
#task_categories-text-classification #region-us
| # Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### 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
### Contributions
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|
4476f578736c8906527dbc57fecb0a53e344a743 | # Dataset Card for "push_custom_splits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/push_custom_splits | [
"region:us"
]
| 2023-03-03T14:29:53+00:00 | {"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16000, "num_examples": 1000}, {"name": "random", "num_bytes": 1600, "num_examples": 100}], "download_size": 0, "dataset_size": 17600}, {"config_name": "v1", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 11200, "num_examples": 700}, {"name": "random", "num_bytes": 800, "num_examples": 50}], "download_size": 10425, "dataset_size": 12000}, {"config_name": "v2", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 160, "num_examples": 10}, {"name": "random", "num_bytes": 80, "num_examples": 5}], "download_size": 2688, "dataset_size": 240}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v1", "data_dir": "v1"}, {"config_name": "v2", "data_dir": "v2"}]} | 2023-03-03T14:47:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "push_custom_splits"
More Information needed | [
"# Dataset Card for \"push_custom_splits\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"push_custom_splits\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"push_custom_splits\"\n\nMore Information needed"
]
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|
8e52fee18a694d4351608ea0b51406c380b08153 |
# NLLB-200
This is the model card of NLLB-200's distilled 600M variant.
Here are the [metrics](https://tinyurl.com/nllb200densedst600mmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues
## Intended Use
- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.
- Primary intended users: Primary users are researchers and machine translation research community.
- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.
## Metrics
• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.
## Evaluation Data
- Datasets: Flores-200 dataset is described in Section 4
- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200
- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The
SentencePiece model is released along with NLLB-200.
## Training Data
• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.
## Ethical Considerations
• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).
## Caveats and Recommendations
• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.
## Carbon Footprint Details
• The carbon dioxide (CO2e) estimate is reported in Section 8.8. | vpermilp/nllb-200-distilled-600M-rust | [
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| TAGS
#task_categories-translation #size_categories-100K<n<1M #language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-nc-4.0 #nllb #translation #region-us
|
# NLLB-200
This is the model card of NLLB-200's distilled 600M variant.
Here are the metrics for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: URL
## Intended Use
- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.
- Primary intended users: Primary users are researchers and machine translation research community.
- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.
## Metrics
• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.
## Evaluation Data
- Datasets: Flores-200 dataset is described in Section 4
- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200
- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The
SentencePiece model is released along with NLLB-200.
## Training Data
• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.
## Ethical Considerations
• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).
## Caveats and Recommendations
• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.
## Carbon Footprint Details
• The carbon dioxide (CO2e) estimate is reported in Section 8.8. | [
"# NLLB-200\n\nThis is the model card of NLLB-200's distilled 600M variant.\n\nHere are the metrics for that particular checkpoint.\n\n- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.\n- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022\n- License: CC-BY-NC\n- Where to send questions or comments about the model: URL",
"## Intended Use\n- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.\n- Primary intended users: Primary users are researchers and machine translation research community.\n- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.",
"## Metrics\n• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.",
"## Evaluation Data\n- Datasets: Flores-200 dataset is described in Section 4\n- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200\n- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The\nSentencePiece model is released along with NLLB-200.",
"## Training Data\n• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.",
"## Ethical Considerations\n• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).",
"## Caveats and Recommendations\n• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.",
"## Carbon Footprint Details\n• The carbon dioxide (CO2e) estimate is reported in Section 8.8."
]
| [
"TAGS\n#task_categories-translation #size_categories-100K<n<1M #language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-nc-4.0 #nllb #translation #region-us \n",
"# NLLB-200\n\nThis is the model card of NLLB-200's distilled 600M variant.\n\nHere are the metrics for that particular checkpoint.\n\n- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.\n- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022\n- License: CC-BY-NC\n- Where to send questions or comments about the model: URL",
"## Intended Use\n- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.\n- Primary intended users: Primary users are researchers and machine translation research community.\n- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.",
"## Metrics\n• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.",
"## Evaluation Data\n- Datasets: Flores-200 dataset is described in Section 4\n- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200\n- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The\nSentencePiece model is released along with NLLB-200.",
"## Training Data\n• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.",
"## Ethical Considerations\n• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).",
"## Caveats and Recommendations\n• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.",
"## Carbon Footprint Details\n• The carbon dioxide (CO2e) estimate is reported in Section 8.8."
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"passage: ",
"passage: TAGS\n#task_categories-translation #size_categories-100K<n<1M #language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-nc-4.0 #nllb #translation #region-us \n# NLLB-200\n\nThis is the model card of NLLB-200's distilled 600M variant.\n\nHere are the metrics for that particular checkpoint.\n\n- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.\n- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022\n- License: CC-BY-NC\n- Where to send questions or comments about the model: URL## Intended Use\n- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.\n- Primary intended users: Primary users are researchers and machine translation research community.\n- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.## Metrics\n• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations."
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|
96a2695c9457613d8c85146739cf757c27c81c8b | # Dataset Card for "books_price_prediction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | james-burton/books_price_prediction | [
"region:us"
]
| 2023-03-03T14:46:06+00:00 | {"dataset_info": {"features": [{"name": "Synopsis", "dtype": "string"}, {"name": "Price", "dtype": "float64"}, {"name": "label", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "scaled_label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 10391810, "num_examples": 5051}, {"name": "validation", "num_bytes": 1158470, "num_examples": 562}, {"name": "test", "num_bytes": 1284512, "num_examples": 624}], "download_size": 8407666, "dataset_size": 12834792}} | 2023-03-08T11:22:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "books_price_prediction"
More Information needed | [
"# Dataset Card for \"books_price_prediction\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"books_price_prediction\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"books_price_prediction\"\n\nMore Information needed"
]
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|
1b988f1a9b6c5716d5888b93dcd2e4493f7cdd46 | # Dataset Card for "salvadoran-news-embedded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justinian336/salvadoran-news-embedded | [
"region:us"
]
| 2023-03-03T14:52:41+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "link", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 591030811, "num_examples": 102258}], "download_size": 537560743, "dataset_size": 591030811}} | 2023-03-03T14:54:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "salvadoran-news-embedded"
More Information needed | [
"# Dataset Card for \"salvadoran-news-embedded\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"salvadoran-news-embedded\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"salvadoran-news-embedded\"\n\nMore Information needed"
]
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|
93f9fac1a9804a2e648c321f5866daf4211e2806 | # Dataset Card for "push_backward_compatibility"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/push_backward_compatibility | [
"region:us"
]
| 2023-03-03T15:35:18+00:00 | {"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 160, "num_examples": 10}], "download_size": 1371, "dataset_size": 160}, {"config_name": "first", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 320, "num_examples": 20}], "download_size": 1469, "dataset_size": 320}], "configs_kwargs": {"config_name": "first", "data_dir": "first"}} | 2023-03-03T15:36:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "push_backward_compatibility"
More Information needed | [
"# Dataset Card for \"push_backward_compatibility\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"push_backward_compatibility\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"push_backward_compatibility\"\n\nMore Information needed"
]
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|
d9922426b78ec04e8bafde294da862add0431ec7 | # Dataset Card for "corpus-en-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | loresiensis/corpus-en-es | [
"language:en",
"language:es",
"region:us"
]
| 2023-03-03T15:53:24+00:00 | {"language": ["en", "es"], "dataset_info": {"features": [{"name": "EN", "dtype": "string"}, {"name": "ES", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3078318.88680555, "num_examples": 9439}, {"name": "test", "num_bytes": 342107.9046783581, "num_examples": 1049}], "download_size": 2125623, "dataset_size": 3420426.7914839084}} | 2023-10-04T07:41:35+00:00 | []
| [
"en",
"es"
]
| TAGS
#language-English #language-Spanish #region-us
| # Dataset Card for "corpus-en-es"
More Information needed | [
"# Dataset Card for \"corpus-en-es\"\n\nMore Information needed"
]
| [
"TAGS\n#language-English #language-Spanish #region-us \n",
"# Dataset Card for \"corpus-en-es\"\n\nMore Information needed"
]
| [
15,
16
]
| [
"passage: TAGS\n#language-English #language-Spanish #region-us \n# Dataset Card for \"corpus-en-es\"\n\nMore Information needed"
]
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|
8f65de8a4141f24c7a2099d21cb715cdb2b1b59a |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/text-to-code/codesearchnet/python in Semeru
# CodeXGLUE -- Code Search (AdvTest)
## Task Definition
Given a natural language, the task is to search source code that matches the natural language. To test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.
## Dataset
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
Besides, to test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **repo:** the owner/repo
- **path:** the full path to the original file
- **func_name:** the function or method name
- **original_string:** the raw string before tokenization or parsing
- **language:** the programming language
- **code/function:** the part of the `original_string` that is code
- **code_tokens/function_tokens:** tokenized version of `code`
- **docstring:** the top-level comment or docstring, if it exists in the original string
- **docstring_tokens:** tokenized version of `docstring`
- **url:** the url for the example (identify natural language)
- **idx**: the index of code (identify code)
### Data Statistics
Data statistics of the dataset are shown in the below table:
| | #Examples |
| ----- | :-------: |
| Train | 251,820 |
| Dev | 9,604 |
| Test | 19,210 |
### Example
Given a text-code file evaluator/test.jsonl:
```json
{"url": "url0", "docstring": "doc0","function": "fun0", "idx": 10}
{"url": "url1", "docstring": "doc1","function": "fun1", "idx": 11}
{"url": "url2", "docstring": "doc2","function": "fun2", "idx": 12}
{"url": "url3", "docstring": "doc3","function": "fun3", "idx": 13}
{"url": "url4", "docstring": "doc4","function": "fun4", "idx": 14}
```
### Input Predictions
For each url for natural language, descending sort candidate codes and return their idx in order. For example:
```json
{"url": "url0", "answers": [10,11,12,13,14]}
{"url": "url1", "answers": [10,12,11,13,14]}
{"url": "url2", "answers": [13,11,12,10,14]}
{"url": "url3", "answers": [10,14,12,13,11]}
{"url": "url4", "answers": [10,11,12,13,14]}
```
## Reference
<pre><code>@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}</code></pre> | semeru/Text-Code-CodeSearchNet-Python | [
"license:mit",
"arxiv:1909.09436",
"region:us"
]
| 2023-03-03T17:03:14+00:00 | {"license": "mit", "Programminglanguage": "Python", "version": "N/A", "Date": "Codesearchnet(Jun 2020 - paper release date)", "Contaminated": "Very Likely", "Size": "Standard Tokenizer (TreeSitter)"} | 2023-03-27T17:36:44+00:00 | [
"1909.09436"
]
| []
| TAGS
#license-mit #arxiv-1909.09436 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/text-to-code/codesearchnet/python in Semeru
CodeXGLUE -- Code Search (AdvTest)
==================================
Task Definition
---------------
Given a natural language, the task is to search source code that matches the natural language. To test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.
Dataset
-------
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
* Remove examples that codes cannot be parsed into an abstract syntax tree.
* Remove examples that #tokens of documents is < 3 or >256
* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
* Remove examples that documents are not English.
Besides, to test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* repo: the owner/repo
* path: the full path to the original file
* func\_name: the function or method name
* original\_string: the raw string before tokenization or parsing
* language: the programming language
* code/function: the part of the 'original\_string' that is code
* code\_tokens/function\_tokens: tokenized version of 'code'
* docstring: the top-level comment or docstring, if it exists in the original string
* docstring\_tokens: tokenized version of 'docstring'
* url: the url for the example (identify natural language)
* idx: the index of code (identify code)
### Data Statistics
Data statistics of the dataset are shown in the below table:
### Example
Given a text-code file evaluator/URL:
### Input Predictions
For each url for natural language, descending sort candidate codes and return their idx in order. For example:
Reference
---------
```
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/text-to-code/codesearchnet/python in Semeru\n\n\nCodeXGLUE -- Code Search (AdvTest)\n==================================\n\n\nTask Definition\n---------------\n\n\nGiven a natural language, the task is to search source code that matches the natural language. To test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.\n\n\nBesides, to test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'\n* url: the url for the example (identify natural language)\n* idx: the index of code (identify code)",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:",
"### Example\n\n\nGiven a text-code file evaluator/URL:",
"### Input Predictions\n\n\nFor each url for natural language, descending sort candidate codes and return their idx in order. For example:\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1909.09436 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/text-to-code/codesearchnet/python in Semeru\n\n\nCodeXGLUE -- Code Search (AdvTest)\n==================================\n\n\nTask Definition\n---------------\n\n\nGiven a natural language, the task is to search source code that matches the natural language. To test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.\n\n\nBesides, to test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'\n* url: the url for the example (identify natural language)\n* idx: the index of code (identify code)",
"### Data Statistics\n\n\nData statistics of the dataset are shown in the below table:",
"### Example\n\n\nGiven a text-code file evaluator/URL:",
"### Input Predictions\n\n\nFor each url for natural language, descending sort candidate codes and return their idx in order. For example:\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
19,
268,
216,
19,
16,
132
]
| [
"passage: TAGS\n#license-mit #arxiv-1909.09436 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/text-to-code/codesearchnet/python in Semeru\n\n\nCodeXGLUE -- Code Search (AdvTest)\n==================================\n\n\nTask Definition\n---------------\n\n\nGiven a natural language, the task is to search source code that matches the natural language. To test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.\n\n\nBesides, to test the generalization ability of a model, function names and variables in test sets are replaced by special tokens.### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'\n* url: the url for the example (identify natural language)\n* idx: the index of code (identify code)"
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|
54b79259e19ef0a7a456b6436a63470c63ba5b0f | # Dataset Card for "the_pile_books3_minus_gutenberg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | SaylorTwift/the_pile_books3_minus_gutenberg | [
"region:us"
]
| 2023-03-03T18:44:35+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "first_name", "dtype": "string"}, {"name": "last_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 106199627990.47722, "num_examples": 192661}], "download_size": 63006723975, "dataset_size": 106199627990.47722}} | 2023-03-03T19:46:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "the_pile_books3_minus_gutenberg"
More Information needed | [
"# Dataset Card for \"the_pile_books3_minus_gutenberg\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"the_pile_books3_minus_gutenberg\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"the_pile_books3_minus_gutenberg\"\n\nMore Information needed"
]
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|
2c1afab0635c0ca64e7fbe115601f244af6c0b85 | # Dataset Card for "test_verifications_checksums"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/test_verifications_checksums | [
"region:us"
]
| 2023-03-03T19:10:07+00:00 | {"dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 160, "num_examples": 1}, {"name": "test", "num_bytes": 8, "num_examples": 5}], "download_size": 2688, "dataset_size": 240}} | 2023-03-03T19:10:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_verifications_checksums"
More Information needed | [
"# Dataset Card for \"test_verifications_checksums\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_verifications_checksums\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_verifications_checksums\"\n\nMore Information needed"
]
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|
af8db7520cdac6106a39ef747c2c737c255b157d | # Dataset Card for "test_verifications_checksums2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/test_verifications_checksums2 | [
"region:us"
]
| 2023-03-03T19:11:56+00:00 | {"dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 160, "num_examples": 1}, {"name": "test", "num_bytes": 8, "num_examples": 5}], "download_size": 0, "dataset_size": 240}} | 2023-03-03T19:17:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_verifications_checksums2"
More Information needed | [
"# Dataset Card for \"test_verifications_checksums2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_verifications_checksums2\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_verifications_checksums2\"\n\nMore Information needed"
]
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|
15eddf5a195f3c7ba6b9fa8c24a82ddcf579e926 | # Dataset Card for "peurdunoir-200-annotated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | meric/peurdunoir-200-annotated | [
"region:us"
]
| 2023-03-03T20:00:06+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7518769.0, "num_examples": 169}], "download_size": 7510086, "dataset_size": 7518769.0}} | 2023-03-03T20:00:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "peurdunoir-200-annotated"
More Information needed | [
"# Dataset Card for \"peurdunoir-200-annotated\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"peurdunoir-200-annotated\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"peurdunoir-200-annotated\"\n\nMore Information needed"
]
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|
97d285267cc2329f6d430b75c9b03107c55a8109 | # Dataset Card for "miniwob_plusplus_T5_simple_1084"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwob_plusplus_T5_simple_1084 | [
"region:us"
]
| 2023-03-03T20:07:03+00:00 | {"dataset_info": {"features": [{"name": "snippet", "dtype": "string"}, {"name": "actions", "dtype": "string"}, {"name": "uterances", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10376727, "num_examples": 15210}], "download_size": 1482882, "dataset_size": 10376727}} | 2023-03-03T20:07:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwob_plusplus_T5_simple_1084"
More Information needed | [
"# Dataset Card for \"miniwob_plusplus_T5_simple_1084\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwob_plusplus_T5_simple_1084\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwob_plusplus_T5_simple_1084\"\n\nMore Information needed"
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|
1b837a6db5ccc2b28a3ff6c216374d2211ffbb48 | # Dataset Card for "miniwob_plusplus_T5_labeled_1084"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | LucasThil/miniwob_plusplus_T5_labeled_1084 | [
"region:us"
]
| 2023-03-03T20:11:27+00:00 | {"dataset_info": {"features": [{"name": "episodes", "dtype": "string"}, {"name": "target_actions", "dtype": "string"}, {"name": "target_refs", "dtype": "int64"}, {"name": "target_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 167162436, "num_examples": 237193}], "download_size": 12138892, "dataset_size": 167162436}} | 2023-03-03T21:33:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "miniwob_plusplus_T5_labeled_1084"
More Information needed | [
"# Dataset Card for \"miniwob_plusplus_T5_labeled_1084\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"miniwob_plusplus_T5_labeled_1084\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"miniwob_plusplus_T5_labeled_1084\"\n\nMore Information needed"
]
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|
e751623d36025193aa743a43139b24d5fa338f32 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: 96harsh56/bert_test1
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@endoftheworld](https://huggingface.co/endoftheworld) for evaluating this model. | autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-767bec-38719101848 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-03-03T21:28:33+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["adversarial_qa"], "eval_info": {"task": "extractive_question_answering", "model": "96harsh56/bert_test1", "metrics": [], "dataset_name": "adversarial_qa", "dataset_config": "adversarialQA", "dataset_split": "validation", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}} | 2023-03-03T21:29:28+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: Question Answering
* Model: 96harsh56/bert_test1
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @endoftheworld 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: Question Answering\n* Model: 96harsh56/bert_test1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @endoftheworld 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: Question Answering\n* Model: 96harsh56/bert_test1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @endoftheworld for evaluating this model."
]
| [
13,
89,
17
]
| [
"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: Question Answering\n* Model: 96harsh56/bert_test1\n* Dataset: adversarial_qa\n* Config: adversarialQA\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @endoftheworld for evaluating this model."
]
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|
90aa082637c33ffaf4db0d7ca40998aac0102065 | # Dataset Card for "word_label_0.8_32_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_32_Nf | [
"region:us"
]
| 2023-03-04T00:13:27+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21532062.856247686, "num_examples": 63196}, {"name": "validation", "num_bytes": 2392527.143752314, "num_examples": 7022}], "download_size": 5544055, "dataset_size": 23924590.0}} | 2023-03-04T00:34:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_32_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.8_32_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_32_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_32_Nf\"\n\nMore Information needed"
]
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|
f0492c8d7b82931393fe06cc1edbfbb4fd310d32 | # Dataset Card for "word_label_0.5_32_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_32_D | [
"region:us"
]
| 2023-03-04T00:13:44+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 23061771.018877786, "num_examples": 68890}, {"name": "validation", "num_bytes": 2562604.9811222157, "num_examples": 7655}], "download_size": 5737255, "dataset_size": 25624376.0}} | 2023-03-04T00:34:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_32_D"
More Information needed | [
"# Dataset Card for \"word_label_0.5_32_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_32_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_32_D\"\n\nMore Information needed"
]
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|
e1caaad1f6f0bc2c6be8e3e6e1e8ef513e687a07 | # Dataset Card for "word_label_0.8_96_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_96_Nf | [
"region:us"
]
| 2023-03-04T00:13:45+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}, {"name": "72", "dtype": "int64"}, {"name": "73", "dtype": "int64"}, {"name": "74", "dtype": "int64"}, {"name": "75", "dtype": "int64"}, {"name": "76", "dtype": "int64"}, {"name": "77", "dtype": "int64"}, {"name": "78", "dtype": "int64"}, {"name": "79", "dtype": "int64"}, {"name": "80", "dtype": "int64"}, {"name": "81", "dtype": "int64"}, {"name": "82", "dtype": "int64"}, {"name": "83", "dtype": "int64"}, {"name": "84", "dtype": "int64"}, {"name": "85", "dtype": "int64"}, {"name": "86", "dtype": "int64"}, {"name": "87", "dtype": "int64"}, {"name": "88", "dtype": "int64"}, {"name": "89", "dtype": "int64"}, {"name": "90", "dtype": "int64"}, {"name": "91", "dtype": "int64"}, {"name": "92", "dtype": "int64"}, {"name": "93", "dtype": "int64"}, {"name": "94", "dtype": "int64"}, {"name": "95", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 64568588.254601575, "num_examples": 71730}, {"name": "validation", "num_bytes": 7175187.745398426, "num_examples": 7971}], "download_size": 10767675, "dataset_size": 71743776.0}} | 2023-03-04T00:35:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_96_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.8_96_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_96_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_96_Nf\"\n\nMore Information needed"
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|
1ad4425ec848f8448ddaef36b941b339376af946 | # Dataset Card for "word_label_0.8_32_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_32_P | [
"region:us"
]
| 2023-03-04T00:13:48+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21904636.001111876, "num_examples": 63945}, {"name": "validation", "num_bytes": 2434190.9988881224, "num_examples": 7106}], "download_size": 5755330, "dataset_size": 24338827.0}} | 2023-03-04T00:33:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_32_P"
More Information needed | [
"# Dataset Card for \"word_label_0.8_32_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_32_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_32_P\"\n\nMore Information needed"
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|
ccb5a0100cb22c3ca4ffba410e3dcd0fd5cedca2 | # Dataset Card for "word_label_0.2_72_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_72_D | [
"region:us"
]
| 2023-03-04T00:14:02+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 48978649.8, "num_examples": 71901}, {"name": "validation", "num_bytes": 5442072.2, "num_examples": 7989}], "download_size": 8684772, "dataset_size": 54420722.0}} | 2023-03-04T00:35:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_72_D"
More Information needed | [
"# Dataset Card for \"word_label_0.2_72_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_72_D\"\n\nMore Information needed"
]
| [
6,
19
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_72_D\"\n\nMore Information needed"
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|
3af4c7c2a32ee6151ae77b229f0b4d122a5ff3fe | # Dataset Card for "word_label_0.8_16_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_16_Nf | [
"region:us"
]
| 2023-03-04T00:14:04+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8361296.526810192, "num_examples": 46946}, {"name": "validation", "num_bytes": 929171.4731898089, "num_examples": 5217}], "download_size": 2368652, "dataset_size": 9290468.0}} | 2023-03-04T00:36:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_16_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.8_16_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_16_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_16_Nf\"\n\nMore Information needed"
]
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cfcf50e6304a7cacf70ef2793b37d8a148aee932 | # Dataset Card for "word_label_0.2_64_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_64_P | [
"region:us"
]
| 2023-03-04T00:14:06+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 44875129.38256158, "num_examples": 70922}, {"name": "validation", "num_bytes": 4986617.6174384225, "num_examples": 7881}], "download_size": 9208926, "dataset_size": 49861747.0}} | 2023-03-04T00:34:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_64_P"
More Information needed | [
"# Dataset Card for \"word_label_0.2_64_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_64_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_64_P\"\n\nMore Information needed"
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|
c1d8a33fa141ef7059ea31f23ff9178ea58bffe7 | # Dataset Card for "word_label_0.8_32_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_32_D | [
"region:us"
]
| 2023-03-04T00:14:21+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 23110641.563503593, "num_examples": 68771}, {"name": "validation", "num_bytes": 2568110.4364964077, "num_examples": 7642}], "download_size": 5803704, "dataset_size": 25678752.0}} | 2023-03-04T00:36:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_32_D"
More Information needed | [
"# Dataset Card for \"word_label_0.8_32_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_32_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_32_D\"\n\nMore Information needed"
]
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|
bcb9e692a8cbd8a0952f74a4e56f1e80300307da | # Dataset Card for "word_mask_Nf_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_Nf_32 | [
"region:us"
]
| 2023-03-04T00:14:26+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7934257.279690487, "num_examples": 80487}, {"name": "validation", "num_bytes": 881682.7203095123, "num_examples": 8944}], "download_size": 6602823, "dataset_size": 8815940.0}} | 2023-03-04T00:36:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_Nf_32"
More Information needed | [
"# Dataset Card for \"word_mask_Nf_32\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_Nf_32\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_Nf_32\"\n\nMore Information needed"
]
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|
f7a4f74cedb5f683c39825cc2943b7858804cd50 | # Dataset Card for "word_label_0.8_72_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_72_P | [
"region:us"
]
| 2023-03-04T00:14:29+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 50161848.00342051, "num_examples": 71305}, {"name": "validation", "num_bytes": 5573694.996579492, "num_examples": 7923}], "download_size": 9878155, "dataset_size": 55735543.0}} | 2023-03-04T00:35:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_72_P"
More Information needed | [
"# Dataset Card for \"word_label_0.8_72_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_72_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_72_P\"\n\nMore Information needed"
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|
c6353fa22bab69c3dc486db256535d45aca6943b | # Dataset Card for "word_label_0.5_72_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_72_D | [
"region:us"
]
| 2023-03-04T00:14:44+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 49185486.78121479, "num_examples": 71893}, {"name": "validation", "num_bytes": 5465662.218785208, "num_examples": 7989}], "download_size": 8882450, "dataset_size": 54651149.0}} | 2023-03-04T00:37:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_72_D"
More Information needed | [
"# Dataset Card for \"word_label_0.5_72_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_72_D\"\n\nMore Information needed"
]
| [
6,
19
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_72_D\"\n\nMore Information needed"
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|
02c266681508c9a54285195536792dbebaf73d44 | # Dataset Card for "word_label_0.8_72_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_72_Nf | [
"region:us"
]
| 2023-03-04T00:14:45+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 49782185.134004176, "num_examples": 71104}, {"name": "validation", "num_bytes": 5531742.865995823, "num_examples": 7901}], "download_size": 9608024, "dataset_size": 55313928.0}} | 2023-03-04T00:37:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_72_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.8_72_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_72_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_72_Nf\"\n\nMore Information needed"
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|
89287259fb3aaef4b82a6e631eb2c183d24ea568 | # Dataset Card for "word_label_0.8_96_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_96_P | [
"region:us"
]
| 2023-03-04T00:14:47+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}, {"name": "72", "dtype": "int64"}, {"name": "73", "dtype": "int64"}, {"name": "74", "dtype": "int64"}, {"name": "75", "dtype": "int64"}, {"name": "76", "dtype": "int64"}, {"name": "77", "dtype": "int64"}, {"name": "78", "dtype": "int64"}, {"name": "79", "dtype": "int64"}, {"name": "80", "dtype": "int64"}, {"name": "81", "dtype": "int64"}, {"name": "82", "dtype": "int64"}, {"name": "83", "dtype": "int64"}, {"name": "84", "dtype": "int64"}, {"name": "85", "dtype": "int64"}, {"name": "86", "dtype": "int64"}, {"name": "87", "dtype": "int64"}, {"name": "88", "dtype": "int64"}, {"name": "89", "dtype": "int64"}, {"name": "90", "dtype": "int64"}, {"name": "91", "dtype": "int64"}, {"name": "92", "dtype": "int64"}, {"name": "93", "dtype": "int64"}, {"name": "94", "dtype": "int64"}, {"name": "95", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 64886319.180816665, "num_examples": 71809}, {"name": "validation", "num_bytes": 7209791.819183336, "num_examples": 7979}], "download_size": 11047905, "dataset_size": 72096111.0}} | 2023-03-04T00:36:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_96_P"
More Information needed | [
"# Dataset Card for \"word_label_0.8_96_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_96_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_96_P\"\n\nMore Information needed"
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|
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