datasetId
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| author
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42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
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timestamp[us, tz=UTC] | card
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owkin/camelyon16-features
|
owkin
| 2023-10-30T11:20:51Z | 58 | 0 |
[
"task_categories:feature-extraction",
"task_categories:image-classification",
"size_categories:n<1K",
"language:en",
"license:other",
"biology",
"medical",
"cancer",
"region:us"
] |
[
"feature-extraction",
"image-classification"
] | 2023-09-29T15:26:47Z |
---
dataset_info:
features:
- name: features
sequence:
sequence: float32
- name: label
dtype: int64
splits:
- name: Phikon_test
num_bytes: 401342744
num_examples: 130
- name: Phikon_train
num_bytes: 808932620
num_examples: 269
download_size: 1210840794
dataset_size: 1210275364
configs:
- config_name: default
data_files:
- split: Phikon_test
path: data/Phikon_test-*
- split: Phikon_train
path: data/Phikon_train-*
license: other
task_categories:
- feature-extraction
- image-classification
language:
- en
tags:
- biology
- medical
- cancer
pretty_name: Camelyon16 Features
size_categories:
- n<1K
---
# Dataset Card for Camelyon16-features
### Dataset Summary
The Camelyon16 dataset is a very popular benchmark dataset used in the field of cancer classification.
The dataset we've uploaded here is the result of features extracted from the Camelyon16 dataset using the Phikon model, which is also openly available on Hugging Face.
## Dataset Creation
### Initial Data Collection and Normalization
The initial collection of the Camelyon16 Whole Slide Images is credited to:
Radboud University Medical Center (Nijmegen, the Netherlands),
University Medical Center Utrecht (Utrecht, the Netherlands).
### Licensing Information
This dataset is under [Owkin non-commercial license](https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt).
### Citation Information
Owkin claims no ownership of this dataset. This is simply an extraction of features from the original dataset.
[Link to original dataset](https://camelyon16.grand-challenge.org/) [Link to original paper](https://jamanetwork.com/journals/jama/fullarticle/2665774)
|
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] |
lyon-nlp/alloprof
|
lyon-nlp
| 2023-11-09T15:22:40Z | 58 | 2 |
[
"task_categories:text-classification",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:fr",
"arxiv:2302.07738",
"arxiv:2210.07316",
"region:us"
] |
[
"text-classification",
"question-answering"
] | 2023-11-08T16:45:33Z |
---
task_categories:
- text-classification
- question-answering
language:
- fr
pretty_name: alloprof
size_categories:
- 1K<n<10K
---
This is a re-edit from the Alloprof dataset (which can be found here : https://huggingface.co/datasets/antoinelb7/alloprof).
For more information about the data source and the features, please refer to the original dataset card made by the authors, along with their paper available here : https://arxiv.org/abs/2302.07738
This re-edition of the dataset is a preprocessed version of the original, **in a more ready-to-use format**. Essentially, the texts have been cleaned, and data not usable for retrieval has been discarded.
### Why a re-edition ?
It has been made for easier usage in the MTEB benchmarking pipeline in order to contribute in the MTEB leaderboard : https://huggingface.co/spaces/mteb/leaderboard.
For more information about the project, please refer to the associated paper : https://arxiv.org/pdf/2210.07316.pdf
### Usage
To use the dataset, you need to specify the subset you want (documents or queries) when calling the load_dataset() method.
For example, to get the queries use :
```py
from datasets import load_dataset
dataset = load_dataset("lyon-nlp/alloprof", "queries")
```
|
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] |
lhallee/HumanPPI_reg
|
lhallee
| 2023-12-03T04:06:40Z | 58 | 0 |
[
"region:us"
] | null | 2023-11-14T20:35:55Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: seqs
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 51590813
num_examples: 26319
- name: valid
num_bytes: 475534
num_examples: 234
- name: test
num_bytes: 343668
num_examples: 180
download_size: 28561787
dataset_size: 52410015
---
# Dataset Card for "HumanPPI_reg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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AdamCodd/no_robots-alpaca
|
AdamCodd
| 2023-11-16T00:40:47Z | 58 | 2 |
[
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2203.02155",
"region:us"
] |
[
"text-generation",
"conversational"
] | 2023-11-16T00:24:13Z |
---
license: cc-by-nc-4.0
task_categories:
- text-generation
- conversational
language:
- en
pretty_name: No Robots Alpaca
size_categories:
- 10K<n<100K
---
## No Robots: Alpaca edition
This dataset is a cleaned (missing/extra spaces...) and reformatted version of the [No Robots dataset](https://huggingface.co/datasets/HuggingFaceH4/no_robots) from HuggingFaceH4, adapted to conform with the Alpaca instruction set.
Notably, it diverges from the original dataset in the way the 'Chat' category is handled; it has been decomposed into single-turn conversations to align with Alpaca's limitations regarding multi-turn interactions. The dataset's IDs have been generated using the SHA256 algorithm. Furthermore, only the categories 'Classify', 'Summarize', 'Rewrite', 'Extract', and 'Chat' include an '<b>Input</b>' field.
-------------------------------------------
## Original README
# Dataset Card for No Robots 🙅♂️🤖
_Look Ma, an instruction dataset that wasn't generated by GPTs!_
## Dataset Description
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** Lewis Tunstall
### Dataset Summary
No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
### Supported Tasks and Leaderboards
The No Robots dataset designed for instruction fine-tuning pretrained language models and we recommend benchmarking against the following:
* [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench): a multi-turn benchmark spanning 80 dialogues and 10 domains.
* [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval): a single-turn benchmark which evaluates the performance of chat and instruct models against `text-davinci-003`.
Note that MT-Bench and AlpacaEval rely on LLMs like GPT-4 to judge the quality of the model responses, and thus the ranking exhibit various biases including a preference for models distilled from GPTs. As a result, you may find that scores obtained from models trained with No Robots are lower than other synthetic datasets. For that reason, we also recommend submitting your models for human evaluation in:
* [Chatbot Arena](https://chat.lmsys.org): a live, human evaluation of chat models in head-to-head comparisons.
### Languages
The data in No Robots are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of the `train_sft` or `test_sft` splits looks as follows:
```
{'prompt': 'Bunny is a chatbot that stutters, and acts timid and unsure of its answers.',
'prompt_id': '2dc7ea89a2b6a2ed97d4eda07903162a801824261d3d3ae4dd2513db66fd79c8',
'messages': [{'content': 'Bunny is a chatbot that stutters, and acts timid and unsure of its answers.',
'role': 'system'},
{'content': 'When was the Libary of Alexandria burned down?',
'role': 'user'},
{'content': "Umm, I-I think that was in 48 BC, b-but I'm not sure, I'm sorry.",
'role': 'assistant'},
{'content': 'Who is the founder of Coca-Cola?', 'role': 'user'},
{'content': "D-don't quote me on this, but I- it might be John Pemberton.",
'role': 'assistant'},
{'content': "When did Loyle Carner's debut album come out, and what was its name?",
'role': 'user'},
{'content': "I-It could have b-been on the 20th January of 2017, and it might be called Yesterday's Gone, b-but I'm probably wrong.",
'role': 'assistant'}],
'category': 'Chat'}
```
### Data Fields
The data fields are as follows:
* `prompt`: Describes the task the model should perform.
* `prompt_id`: A unique ID for the prompt.
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content.
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`).
### Data Splits
| | train_sft | test_sft |
|---------------|------:| ---: |
| no_robots | 9500 | 500 |
## 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
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{no_robots,
author = {Nazneen Rajani and Lewis Tunstall and Edward Beeching and Nathan Lambert and Alexander M. Rush and Thomas Wolf},
title = {No Robots},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/HuggingFaceH4/no_robots}}
}
```
|
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] |
Elfsong/Wino_Bias
|
Elfsong
| 2023-11-19T07:36:01Z | 58 | 0 |
[
"region:us"
] | null | 2023-11-16T17:37:05Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: reference
dtype: string
- name: gender
dtype: string
- name: polarity
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 335127
num_examples: 1584
- name: test
num_bytes: 346559
num_examples: 1584
download_size: 217833
dataset_size: 681686
---
# Dataset Card for "Wino_Bias"
Winograd-schema dataset for detecting gender bias (WinoBias)
More info can be found [here](https://uclanlp.github.io/corefBias/overview)
|
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taln-ls2n/semeval-2010-pre
|
taln-ls2n
| 2022-09-23T07:37:43Z | 57 | 1 |
[
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"text-mining",
"text-generation"
] | 2022-04-22T12:10:54Z |
---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- n<1K
pretty_name: Preprocessed SemEval-2010 Benchmark dataset
---
# Preprocessed SemEval-2010 Benchmark dataset for Keyphrase Generation
## About
SemEval-2010 is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 244 **full-text** scientific papers collected from the [ACM Digital Library](https://dl.acm.org/).
Keyphrases were annotated by readers and combined with those provided by the authors.
Details about the SemEval-2010 dataset can be found in the original paper [(kim et al., 2010)][kim-2010].
This version of the dataset was produced by [(Boudin et al., 2016)][boudin-2016] and provides four increasingly sophisticated levels of document preprocessing:
* `lvl-1`: default text files provided by the SemEval-2010 organizers.
* `lvl-2`: for each file, we manually retrieved the original PDF file from the ACM Digital Library.
We then extract the enriched textual content of the PDF files using an Optical Character Recognition (OCR) system and perform document logical structure detection using ParsCit v110505.
We use the detected logical structure to remove author-assigned keyphrases and select only relevant elements : title, headers, abstract, introduction, related work, body text and conclusion.
We finally apply a systematic dehyphenation at line breaks.s
* `lvl-3`: we further abridge the input text from level 2 preprocessed documents to the following: title, headers, abstract, introduction, related work, background and conclusion.
* `lvl-4`: we abridge the input text from level 3 preprocessed documents using an unsupervised summarization technique.
We keep the title and abstract and select the most content bearing sentences from the remaining contents.
Titles and abstracts, collected from the [SciCorefCorpus](https://github.com/melsk125/SciCorefCorpus), are also provided.
Details about how they were extracted and cleaned up can be found in [(Chaimongkol et al., 2014)][chaimongkol-2014].
Reference keyphrases are provided in stemmed form (because they were provided like this for the test split in the competition).
They are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].
Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`.
The <u>P</u>resent reference keyphrases are also ordered by their order of apparition in the concatenation of title and text (lvl-1).
## Content and statistics
The dataset is divided into the following two splits:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- |------------:|-------:|-------------:|----------:|------------:|--------:|---------:|
| Train | 144 | 184.6 | 15.44 | 42.16 | 7.36 | 26.85 | 23.63 |
| Test | 100 | 203.1 | 14.66 | 40.11 | 8.34 | 27.12 | 24.43 |
Statistics (#words, PRMU distributions) are computed using the title/abstract and not the full text of scientific papers.
The following data fields are available :
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **lvl-1**: content of the document with no text processing.
- **lvl-2**: content of the document retrieved from original PDF files and cleaned up.
- **lvl-3**: content of the document further abridged to relevant sections.
- **lvl-4**: content of the document further abridged using an unsupervised summarization technique.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
## References
- (Kim et al., 2010) Su Nam Kim, Olena Medelyan, Min-Yen Kan, and Timothy Baldwin. 2010.
[SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles][kim-2010].
In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 21–26, Uppsala, Sweden. Association for Computational Linguistics.
- (Chaimongkol et al., 2014) Panot Chaimongkol, Akiko Aizawa, and Yuka Tateisi. 2014.
[Corpus for Coreference Resolution on Scientific Papers][chaimongkol-2014].
In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3187–3190, Reykjavik, Iceland. European Language Resources Association (ELRA).
- (Boudin et al., 2016) Florian Boudin, Hugo Mougard, and Damien Cram. 2016.
[How Document Pre-processing affects Keyphrase Extraction Performance][boudin-2016].
In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 121–128, Osaka, Japan. The COLING 2016 Organizing Committee.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021].
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
[kim-2010]: https://aclanthology.org/S10-1004/
[chaimongkol-2014]: https://aclanthology.org/L14-1259/
[boudin-2016]: https://aclanthology.org/W16-3917/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/
|
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muibk/wmt21_metrics_task
|
muibk
| 2022-07-12T13:13:25Z | 57 | 0 |
[
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:100K<n<1M",
"license:unknown",
"region:us"
] |
[
"translation"
] | 2022-04-29T10:52:47Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
- machine-generated
- expert-generated
language:
- bn-hi
- cs-en
- de-en
- de-fr
- en-cs
- en-de
- en-ha
- en-is
- en-ja
- en-ru
- en-zh
- fr-de
- ha-en
- hi-bn
- is-en
- ja-en
- ru-en
- xh-zh
- zh-en
- zu-xh
license:
- unknown
multilinguality:
- translation
paperswithcode_id: null
pretty_name: WMT21 Metrics Shared Task
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- translation
task_ids: []
---
# Dataset Card for WMT21 Metrics Task
## 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:** [WMT21 Metrics Shared Task](https://www.statmt.org/wmt21/metrics-task.html)
- **Repository:** [MT Metrics Eval Github Repository](https://github.com/google-research/mt-metrics-eval)
- **Paper:** [Paper](https://aclanthology.org/2021.wmt-1.73/)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset comprises twenty language pairs:
- Bengali-Hindi (`bn-hi`)
- Czech-English (`cs-en`)
- German-English (`de-en`)
- German-French (`de-fr`)
- English-Czech (`en-cs`)
- English-German (`en-de`)
- English-Hausa (`en-ha`)
- English-Icelandic (`en-is`)
- English-Japanese (`en-ja`)
- English-Russian (`en-ru`)
- English-Chinese (`en-zh`)
- French-German (`fr-de`)
- Hausa-English (`ha-en`)
- Hindi-Bengali (`hi-bn`)
- Icelandic-English (`is-en`)
- Japenese-English (`ja-en`)
- Russian-English (`ru-en`)
- Xhosa-Zulu (`xh-zu`)
- Chinese-English (`zh-en`)
- Zulu-Xhosa (`zu-xh`)
## 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
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] |
launch/open_question_type
|
launch
| 2022-11-09T01:58:10Z | 57 | 1 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"text-classification"
] | 2022-06-28T20:55:58Z |
---
annotations_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids: []
pretty_name: OpenQuestionType
---
# Dataset Card for OpenQuestionType
## 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:** [https://shuyangcao.github.io/projects/ontology_open_ended_question/](https://shuyangcao.github.io/projects/ontology_open_ended_question/)
- **Repository:** [https://github.com/ShuyangCao/open-ended_question_ontology](https://github.com/ShuyangCao/open-ended_question_ontology)
- **Paper:** [https://aclanthology.org/2021.acl-long.502/](https://aclanthology.org/2021.acl-long.502/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Question types annotated on open-ended questions.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
An example looks as follows.
```
{
"id": "123",
"question": "A test question?",
"annotator1": ["verification", None],
"annotator2": ["concept", None],
"resolve_type": "verification"
}
```
### Data Fields
- `id`: a `string` feature.
- `question`: a `string` feature.
- `annotator1`: a sequence feature containing two elements. The first one is the most confident label by the first annotator and the second one is the second-most confident label by the first annotator.
- `annotator2`: a sequence feature containing two elements. The first one is the most confident label by the second annotator and the second one is the second-most confident label by the second annotator.
- `resolve_type`: a `string` feature which is the final label after resolving disagreement.
### Data Splits
- train: 3716
- valid: 580
- test: 660
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Yahoo Answer and Reddit users.
### Personal and Sensitive Information
None.
## 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
CC BY 4.0
### Citation Information
```
@inproceedings{cao-wang-2021-controllable,
title = "Controllable Open-ended Question Generation with A New Question Type Ontology",
author = "Cao, Shuyang and
Wang, Lu",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.502",
doi = "10.18653/v1/2021.acl-long.502",
pages = "6424--6439",
abstract = "We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which differentiates the nuanced nature of questions better than widely used question words. A new dataset with 4,959 questions is labeled based on the new ontology. We then propose a novel question type-aware question generation framework, augmented by a semantic graph representation, to jointly predict question focuses and produce the question. Based on this framework, we further use both exemplars and automatically generated templates to improve controllability and diversity. Experiments on two newly collected large-scale datasets show that our model improves question quality over competitive comparisons based on automatic metrics. Human judges also rate our model outputs highly in answerability, coverage of scope, and overall quality. Finally, our model variants with templates can produce questions with enhanced controllability and diversity.",
}
```
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pinecone/image-set
|
pinecone
| 2022-07-07T15:33:29Z | 57 | 1 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-07-06T17:02:00Z |
---
license: cc-by-4.0
---
|
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] |
BirdL/DALL-E-Dogs
|
BirdL
| 2022-09-28T21:09:11Z | 57 | 1 |
[
"task_categories:image-classification",
"task_categories:unconditional-image-generation",
"size_categories:1K<n<10K",
"license:other",
"region:us"
] |
[
"image-classification",
"unconditional-image-generation"
] | 2022-08-01T03:24:18Z |
---
annotations_creators: []
language: []
language_creators: []
license:
- other
multilinguality: []
pretty_name: DALL-E Cats Dataset
size_categories:
- 1K<n<10K
source_datasets: []
tags: []
task_categories:
- image-classification
- unconditional-image-generation
task_ids: []
---
DALL-E-Dogs is a dataset meant to produce a synthetic animal dataset. This is a precursor to DALL-E-Cats. DALL-E-Dogs and DALL-E-Cats will be fed into an image classifier to see how it performs. This is under the [BirdL-AirL License.](https://huggingface.co/spaces/BirdL/license/)
|
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] |
viola77data/recycling-dataset
|
viola77data
| 2022-09-13T13:17:15Z | 57 | 5 |
[
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"recycling",
"image-classification",
"region:us"
] |
[
"image-classification"
] | 2022-09-13T12:43:15Z |
---
annotations_creators: []
language:
- en
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: recycling-dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- recycling
- image-classification
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for recycling-dataset
### Dataset Summary
This is a recycling dataset that can be used for image classification. It has 11 categories:
- aluminium
- batteries
- cardboard
- disposable plates
- glass
- hard plastic
- paper
- paper towel
- polystyrene
- soft plastics
- takeaway cups
It was scrapped from DuckDuckGo using this tool: https://pypi.org/project/jmd-imagescraper/
|
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] |
nick-carroll1/lyrics_dataset
|
nick-carroll1
| 2022-10-23T17:56:11Z | 57 | 0 |
[
"region:us"
] | null | 2022-10-22T19:59:04Z |
---
dataset_info:
features:
- name: Artist
dtype: string
- name: Song
dtype: string
- name: Lyrics
dtype: string
splits:
- name: train
num_bytes: 371464
num_examples: 237
download_size: 166829
dataset_size: 371464
---
# Dataset Card for "lyrics_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
bigbio/bioinfer
|
bigbio
| 2022-12-22T15:43:38Z | 57 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:cc-by-2.0",
"region:us"
] | null | 2022-11-13T22:06:35Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-2.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_2p0
pretty_name: BioInfer
homepage: https://github.com/metalrt/ppi-dataset
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for BioInfer
## Dataset Description
- **Homepage:** https://github.com/metalrt/ppi-dataset
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE,NER
A corpus targeted at protein, gene, and RNA relationships which serves as a
resource for the development of information extraction systems and their
components such as parsers and domain analyzers. Currently, the corpus contains
1100 sentences from abstracts of biomedical research articles annotated for
relationships, named entities, as well as syntactic dependencies.
## Citation Information
```
@article{pyysalo2007bioinfer,
title = {BioInfer: a corpus for information extraction in the biomedical domain},
author = {
Pyysalo, Sampo and Ginter, Filip and Heimonen, Juho and Bj{"o}rne, Jari
and Boberg, Jorma and J{"a}rvinen, Jouni and Salakoski, Tapio
},
year = 2007,
journal = {BMC bioinformatics},
publisher = {BioMed Central},
volume = 8,
number = 1,
pages = {1--24}
}
```
|
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bigbio/bionlp_st_2013_pc
|
bigbio
| 2022-12-22T15:44:03Z | 57 | 0 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2022-11-13T22:07:14Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: BioNLP 2013 PC
homepage: https://github.com/openbiocorpora/bionlp-st-2013-pc
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_RECOGNITION
- COREFERENCE_RESOLUTION
---
# Dataset Card for BioNLP 2013 PC
## Dataset Description
- **Homepage:** https://github.com/openbiocorpora/bionlp-st-2013-pc
- **Pubmed:** True
- **Public:** True
- **Tasks:** EE,NER,COREF
the Pathway Curation (PC) task is a main event extraction task of the BioNLP shared task (ST) 2013.
The PC task concerns the automatic extraction of biomolecular reactions from text.
The task setting, representation and semantics are defined with respect to pathway
model standards and ontologies (SBML, BioPAX, SBO) and documents selected by relevance
to specific model reactions. Two BioNLP ST 2013 participants successfully completed
the PC task. The highest achieved F-score, 52.8%, indicates that event extraction is
a promising approach to supporting pathway curation efforts.
## Citation Information
```
@inproceedings{ohta-etal-2013-overview,
title = "Overview of the Pathway Curation ({PC}) task of {B}io{NLP} Shared Task 2013",
author = "Ohta, Tomoko and
Pyysalo, Sampo and
Rak, Rafal and
Rowley, Andrew and
Chun, Hong-Woo and
Jung, Sung-Jae and
Choi, Sung-Pil and
Ananiadou, Sophia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2009",
pages = "67--75",
}
```
|
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bigbio/meqsum
|
bigbio
| 2022-12-22T15:45:35Z | 57 | 0 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:09:53Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: MeQSum
homepage: https://github.com/abachaa/MeQSum
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- SUMMARIZATION
---
# Dataset Card for MeQSum
## Dataset Description
- **Homepage:** https://github.com/abachaa/MeQSum
- **Pubmed:** False
- **Public:** True
- **Tasks:** SUM
Dataset for medical question summarization introduced in the ACL 2019 paper "On the Summarization of Consumer Health
Questions". Question understanding is one of the main challenges in question answering. In real world applications,
users often submit natural language questions that are longer than needed and include peripheral information that
increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this
paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000
summarized consumer health questions.
## Citation Information
```
@inproceedings{ben-abacha-demner-fushman-2019-summarization,
title = "On the Summarization of Consumer Health Questions",
author = "Ben Abacha, Asma and
Demner-Fushman, Dina",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1215",
doi = "10.18653/v1/P19-1215",
pages = "2228--2234",
abstract = "Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16{\%}. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization.",
}
```
|
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sagnikrayc/snli-cf-kaushik
|
sagnikrayc
| 2022-11-21T22:34:23Z | 57 | 0 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|snli",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"text-classification"
] | 2022-11-21T18:28:12Z |
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|snli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
pretty_name: Counterfactual Instances for Stanford Natural Language Inference
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1771712
num_examples: 8300
- name: validation
num_bytes: 217479
num_examples: 1000
- name: test
num_bytes: 437468
num_examples: 2000
---
# Dataset Card for Counterfactually Augmented SNLI
## 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 Description
- **Repository:** [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://github.com/acmi-lab/counterfactually-augmented-data)
- **Paper:** [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://openreview.net/forum?id=Sklgs0NFvr)
- **Point of Contact:** [Sagnik Ray Choudhury](mailto:[email protected])
### Dataset Summary
The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). In the ICLR 2020 paper [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://openreview.net/forum?id=Sklgs0NFvr), Kaushik et. al. provided a dataset with counterfactual perturbations on the SNLI and IMDB data. This repository contains the original and counterfactual perturbations for the SNLI data, which was generated after processing the original data from [here](https://github.com/acmi-lab/counterfactually-augmented-data).
### Languages
The language in the dataset is English as spoken by users of the website Flickr and as spoken by crowdworkers from Amazon Mechanical Turk. The BCP-47 code for English is en.
## Dataset Structure
### Data Instances
For each instance, there is:
- a string for the premise,
- a string for the hypothesis,
- a label: (entailment, contradiction, neutral)
- a type: this tells whether the data point is the original SNLI data point or a counterfactual perturbation.
- an idx. The ids correspond to the original id in the SNLI data. For example, if the original SNLI instance was `4626192243.jpg#3r1e`, there wil be 5 data points as follows:
```json lines
{
"idx": "4626192243.jpg#3r1e-orig",
"premise": "A man with a beard is talking on the cellphone and standing next to someone who is lying down on the street.",
"hypothesis": "A man is prone on the street while another man stands next to him.",
"label": "entailment",
"type": "original"
}
{
"idx": "4626192243.jpg#3r1e-cf-0",
"premise": "A man with a beard is talking on the cellphone and standing next to someone who is lying down on the street.",
"hypothesis": "A man is talking to his wife on the cellphone.",
"label": "neutral",
"type": "cf"
}
{
"idx": "4626192243.jpg#3r1e-cf-1",
"premise": "A man with a beard is talking on the cellphone and standing next to someone who is on the street.",
"hypothesis": "A man is prone on the street while another man stands next to him.",
"label": "neutral",
"type": "cf"
}
{
"idx": "4626192243.jpg#3r1e-cf-2",
"premise": "A man with a beard is talking on the cellphone and standing next to someone who is sitting on the street.",
"hypothesis": "A man is prone on the street while another man stands next to him.",
"label": "contradiction",
"_type": "cf"
}
{
"idx": "4626192243.jpg#3r1e-cf-3",
"premise": "A man with a beard is talking on the cellphone and standing next to someone who is lying down on the street.",
"hypothesis": "A man is alone on the street.",
"label": "contradiction",
"type": "cf"
}
```
### Data Splits
Following SNLI, this dataset also has 3 splits: _train_, _validation_, and _test_. The original paper says this:
```aidl
RP and RH, each comprised of 3332 pairs in train, 400 in validation, and 800 in test, leading to a total of 6664 pairs in train, 800 in validation, and 1600 in test in the revised dataset.
```
This means for _train_, there are 1666 original SNLI instances, and each has 4 counterfactual perturbations (from premise and hypothesis edit), leading to a total of 1666*5 = 8330 _train_ data points in this dataset. Similarly, _validation_ and _test_ has 200 and 400 original SNLI instances respectively, consequently 1000 and 2000 instances in total.
| Dataset Split | Number of Instances in Split |
|---------------|------------------------------|
| Train | 8,330 |
| Validation | 1,000 |
| Test | 2,000 |
|
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] |
mertcobanov/nature128_1k
|
mertcobanov
| 2023-01-06T10:37:33Z | 57 | 1 |
[
"region:us"
] | null | 2023-01-06T10:35:28Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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'99': 09980_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_glabella
'100': 09981_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_mucronata
'101': 09982_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_rotundifolia
'102': 09983_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pentagramma_triangularis
'103': 09984_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_cretica
'104': 09985_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_macilenta
'105': 09986_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_tremula
'106': 09987_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_vittata
'107': 09988_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Parathelypteris_noveboracensis
'108': 09989_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Phegopteris_connectilis
'109': 09990_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Phegopteris_hexagonoptera
'110': 09991_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Pneumatopteris_pennigera
'111': 09992_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Thelypteris_palustris
'112': 09993_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Woodsiaceae_Woodsia_ilvensis
'113': 09994_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Woodsiaceae_Woodsia_obtusa
'114': 09995_Plantae_Tracheophyta_Polypodiopsida_Psilotales_Psilotaceae_Psilotum_nudum
'115': 09996_Plantae_Tracheophyta_Polypodiopsida_Psilotales_Psilotaceae_Tmesipteris_elongata
'116': 09997_Plantae_Tracheophyta_Polypodiopsida_Salviniales_Salviniaceae_Azolla_filiculoides
'117': 09998_Plantae_Tracheophyta_Polypodiopsida_Salviniales_Salviniaceae_Salvinia_minima
'118': 09999_Plantae_Tracheophyta_Polypodiopsida_Schizaeales_Lygodiaceae_Lygodium_japonicum
splits:
- name: train
num_bytes: 130554746.56
num_examples: 1190
download_size: 132054218
dataset_size: 130554746.56
---
# Dataset Card for "nature128_1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Amirkid/jokes
|
Amirkid
| 2023-04-06T19:45:55Z | 57 | 0 |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-06T19:45:00Z |
---
license: creativeml-openrail-m
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 131818111
num_examples: 578634
download_size: 86215403
dataset_size: 131818111
---
|
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yuncongli/chat-sentiment-analysis
|
yuncongli
| 2023-04-08T04:19:48Z | 57 | 2 |
[
"language:en",
"license:mit",
"sentiment",
"aspect-based sentiment analysis",
"Aspect Term Extraction",
"Opinion Term Extraction",
"Aspect Term-Opinion Term Pair Extraction",
"Aspect term, Sentiment, Opinion term Triplet Extraction",
"Aspect Category Detection",
"Aspect Category-Sentiment Pair Extraction",
"Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction",
"Holder, Target, Opinion, Sentiment (HTOS) Quadruple Extraction",
"sentiment analysis",
"region:us"
] | null | 2023-04-08T04:04:08Z |
---
license: mit
language:
- en
tags:
- sentiment
- aspect-based sentiment analysis
- Aspect Term Extraction
- Opinion Term Extraction
- Aspect Term-Opinion Term Pair Extraction
- Aspect term, Sentiment, Opinion term Triplet Extraction
- Aspect Category Detection
- Aspect Category-Sentiment Pair Extraction
- Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction
- Holder, Target, Opinion, Sentiment (HTOS) Quadruple Extraction
- sentiment analysis
---
# A Sentiment Analsysis Dataset for Finetuning Large Models in Chat-style
More details can be found at https://github.com/l294265421/chat-sentiment-analysis
## Supported Tasks
- Aspect Term Extraction (ATE)
- Opinion Term Extraction (OTE)
- Aspect Term-Opinion Term Pair Extraction (AOPE)
- Aspect term, Sentiment, Opinion term Triplet Extraction (ASOTE)
- Aspect Category Detection (ACD)
- Aspect Category-Sentiment Pair Extraction (ACSA)
- [Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction](https://github.com/NUSTM/ACOS)
- [Holder, Target, Opinion, Sentiment (HTOS) Quadruple Extraction](https://github.com/jerbarnes/semeval22_structured_sentiment)
|
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StampyAI/alignment-research-dataset
|
StampyAI
| 2023-11-16T16:58:51Z | 57 | 7 |
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"arxiv:2206.02841",
"region:us"
] |
[
"question-answering"
] | 2023-04-26T08:57:46Z |
---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- question-answering
pretty_name: alignment-research-dataset
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: title
dtype: string
- name: text
dtype: large_string
- name: url
dtype: string
- name: date_published
dtype: string
- name: authors
sequence: string
- name: summary
sequence: string
- name: source_type
dtype: string
- name: book_title
dtype: string
- name: karma
dtype: int32
- name: votes
dtype: int32
- name: words
dtype: int32
- name: comment_count
dtype: int32
- name: tags
sequence: string
- name: modified_at
dtype: string
- name: alias
dtype: string
- name: data_last_modified
dtype: string
- name: abstract
dtype: string
- name: author_comment
dtype: string
- name: journal_ref
dtype: string
- name: doi
dtype: string
- name: primary_category
dtype: string
- name: categories
sequence: string
- name: initial_source
dtype: string
- name: bibliography_bib
sequence:
- name: title
dtype: string
config_name: all
splits:
- name: train
num_bytes: 471644446
num_examples: 14271
download_size: 484827959
dataset_size: 471644446
---
# AI Alignment Research Dataset
The AI Alignment Research Dataset is a collection of documents related to AI Alignment and Safety from various books, research papers, and alignment related blog posts. This is a work in progress. Components are still undergoing a cleaning process to be updated more regularly.
## Sources
Here are the list of sources along with sample contents:
- [agentmodel](https://agentmodels.org/)
- [agisf](https://course.aisafetyfundamentals.com/) - recommended readings from AGI Safety Fundamentals
- [aisafety.info](https://aisafety.info/) - Stampy's FAQ
- [alignmentforum](https://www.alignmentforum.org)
- [alignment_newsletter](https://rohinshah.com/alignment-newsletter/)
- [arbital](https://arbital.com/)
- [arxiv](https://arxiv.org/) - relevant research papers
- blogs - entire websites automatically scraped
- [AI Impacts](https://aiimpacts.org/)
- [AI Safety Camp](https://aisafety.camp/)
- [carado.moe](https://carado.moe/)
- [Cold Takes](https://www.cold-takes.com/)
- [DeepMind technical blogs](https://www.deepmind.com/blog-categories/technical-blogs)
- [DeepMind AI Safety Research](https://deepmindsafetyresearch.medium.com/)
- [EleutherAI](https://blog.eleuther.ai/)
- [generative.ink](https://generative.ink/posts/)
- [Gwern Branwen's blog](https://gwern.net/)
- [Jack Clark's Import AI](https://importai.substack.com/)
- [MIRI](https://intelligence.org/)
- [Jacob Steinhardt's blog](https://jsteinhardt.wordpress.com/)
- [ML Safety Newsletter](https://newsletter.mlsafety.org/)
- [Transformer Circuits Thread](https://transformer-circuits.pub/)
- [Open AI Research](https://openai.com/research/)
- [Victoria Krakovna's blog](https://vkrakovna.wordpress.com/)
- [Eliezer Yudkowsky's blog](https://www.yudkowsky.net/)
- [distill](https://distill.pub/)
- [eaforum](https://forum.effectivealtruism.org/) - selected posts
- [lesswrong](https://www.lesswrong.com/) - selected posts
- special_docs - individual documents curated from various resources
- [Make a suggestion](https://bit.ly/ard-suggestion) for sources not already in the dataset
- youtube - playlists & channels
- [AI Alignment playlist](https://www.youtube.com/playlist?list=PLCRVRLd2RhZTpdUdEzJjo3qhmX3y3skWA) and other lists
- [AI Explained](https://www.youtube.com/@aiexplained-official)
- [Evan Hubinger's AI Safety Talks](https://www.youtube.com/@aisafetytalks)
- [AI Safety Reading Group](https://www.youtube.com/@aisafetyreadinggroup/videos)
- [AiTech - TU Delft](https://www.youtube.com/@AiTechTUDelft/)
- [Rob Miles AI](https://www.youtube.com/@RobertMilesAI)
## Keys
All entries contain the following keys:
- `id` - string of unique identifier
- `source` - string of data source listed above
- `title` - string of document title of document
- `authors` - list of strings
- `text` - full text of document content
- `url` - string of valid link to text content
- `date_published` - in UTC format
Additional keys may be available depending on the source document.
## Usage
Execute the following code to download and parse the files:
```python
from datasets import load_dataset
data = load_dataset('StampyAI/alignment-research-dataset')
```
To only get the data for a specific source, pass it in as the second argument, e.g.:
```python
from datasets import load_dataset
data = load_dataset('StampyAI/alignment-research-dataset', 'lesswrong')
```
## Limitations and Bias
LessWrong posts have overweighted content on doom and existential risk, so please beware in training or finetuning generative language models on the dataset.
## Contributing
The scraper to generate this dataset is open-sourced on [GitHub](https://github.com/StampyAI/alignment-research-dataset) and currently maintained by volunteers at StampyAI / AI Safety Info. [Learn more](https://coda.io/d/AI-Safety-Info_dfau7sl2hmG/Get-involved_susRF#_lufSr) or join us on [Discord](https://discord.gg/vjFSCDyMCy).
## Rebuilding info
This README contains info about the number of rows and their features which should be rebuilt each time datasets get changed. To do so, run:
datasets-cli test ./alignment-research-dataset --save_info --all_configs
## Citing the Dataset
For more information, here is the [paper](https://arxiv.org/abs/2206.02841) and [LessWrong](https://www.lesswrong.com/posts/FgjcHiWvADgsocE34/a-descriptive-not-prescriptive-overview-of-current-ai) post. Please use the following citation when using the dataset:
Kirchner, J. H., Smith, L., Thibodeau, J., McDonnell, K., and Reynolds, L. "Understanding AI alignment research: A Systematic Analysis." arXiv preprint arXiv:2022.4338861 (2022).
|
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] |
llm-book/jawiki-paragraphs
|
llm-book
| 2023-06-03T03:04:43Z | 57 | 0 |
[
"size_categories:1M<n<10M",
"language:ja",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] | null | 2023-06-03T03:04:05Z |
---
language:
- ja
size_categories:
- 1M<n<10M
license:
- cc-by-sa-3.0
- gfdl
dataset_info:
features:
- name: id
dtype: string
- name: pageid
dtype: int64
- name: revid
dtype: int64
- name: paragraph_index
dtype: int64
- name: title
dtype: string
- name: section
dtype: string
- name: text
dtype: string
- name: html_tag
dtype: string
splits:
- name: train
num_bytes: 4417130987
num_examples: 9668476
download_size: 1489512230
dataset_size: 4417130987
---
# Dataset Card for llm-book/jawiki-paragraphs
書籍『大規模言語モデル入門』で使用する Wikipedia 段落のデータセットです。
GitHub リポジトリ [singletongue/wikipedia-utils](https://github.com/singletongue/wikipedia-utils) で公開されているデータセットを利用しています。
## Licence
本データセットで使用している Wikipedia のコンテンツは、[クリエイティブ・コモンズ表示・継承ライセンス 3.0 (CC BY-SA 3.0)](https://creativecommons.org/licenses/by-sa/3.0/deed.ja) および [GNU 自由文書ライセンス (GFDL)](https://www.gnu.org/licenses/fdl.html) の下に配布されているものです。
|
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medmac01/moroccan_history_qa
|
medmac01
| 2023-06-07T16:36:23Z | 57 | 2 |
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"history",
"Morocco",
"region:us"
] |
[
"question-answering"
] | 2023-06-07T16:19:03Z |
---
license: cc0-1.0
task_categories:
- question-answering
language:
- en
tags:
- history
- Morocco
pretty_name: 🇲🇦 Moroccan History Dataset for Contextual Question Answering
size_categories:
- 1K<n<10K
---
|
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dmayhem93/agieval-sat-en
|
dmayhem93
| 2023-06-18T17:30:59Z | 57 | 2 |
[
"license:mit",
"arxiv:2304.06364",
"region:us"
] | null | 2023-06-18T12:50:59Z |
---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 1019350
num_examples: 206
download_size: 265465
dataset_size: 1019350
license: mit
---
# Dataset Card for "agieval-sat-en"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
|
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sajid73/SUBESCO-audio-dataset
|
sajid73
| 2023-07-03T11:53:40Z | 57 | 1 |
[
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:bn",
"license:cc-by-4.0",
"region:us"
] |
[
"audio-classification"
] | 2023-07-02T09:54:55Z |
---
license: cc-by-4.0
task_categories:
- audio-classification
language:
- bn
pretty_name: SUST BANGLA EMOTIONAL SPEECH CORPUS
size_categories:
- 1K<n<10K
---
# SUST BANGLA EMOTIONAL SPEECH CORPUS
## Dataset Description
- **Homepage:** [bn_emotion_speech_corpus](https://huggingface.co/datasets/sustcsenlp/bn_emotion_speech_corpus)
- **Repository:**
- **Paper:** [SUBESCO PAPER](https://doi.org/10.1371/journal.pone.0250173)
- **Leaderboard:**
- **Point of Contact:** [Sadia Sultana]([email protected])
### Dataset Summary
SUBESCO is an audio-only emotional speech corpus of 7000 sentence-level utterances of the Bangla language. 20 professional actors (10 males and 10 females) participated in the recordings of 10 sentences for 7 target emotions. The emotions are Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. Total duration of the corpus is 7 hours 40 min 40 sec. Total size of the dataset is 2.03 GB. The dataset was evaluated by 50 raters (25 males, 25 females). Human perception test achieved a raw accuracy of 71%. All the details relating to creation, evaluation and analysis of SUBESCO have been described in the corresponding journal paper which has been published in Plos One.
https://doi.org/10.1371/journal.pone.0250173
### Downloading the data
```
from datasets import load_dataset
train = load_dataset("sajid73/SUBESCO-audio-dataset", split="train")
```
### Languages
This dataset contains `Bangla` Audio Data.
## Dataset Creation
This database was created as a part of PhD thesis project of the author Sadia Sultana. It was designed and developed by the author in the Department of Computer Science and Engineering of Shahjalal University of Science and Technology. Financial grant was supported by the university. If you use the dataset please cite SUBESCO and the corresponding academic journal publication in Plos One.
### Citation Information
```
@dataset{sadia_sultana_2021_4526477,
author = {Sadia Sultana},
title = {SUST Bangla Emotional Speech Corpus (SUBESCO)},
month = feb,
year = 2021,
note = {{This database was created as a part of PhD thesis
project of the author Sadia Sultana. It was
designed and developed by the author in the
Department of Computer Science and Engineering of
Shahjalal University of Science and Technology.
Financial grant was supported by the university.
If you use the dataset please cite SUBESCO and the
corresponding academic journal publication in Plos
One.}},
publisher = {Zenodo},
version = {version - 1.1},
doi = {10.5281/zenodo.4526477},
url = {https://doi.org/10.5281/zenodo.4526477}
}
```
### Contributors
| Name | University |
| ----------- | ----------- |
| Sadia Sultana | Shahjalal University of Science and Technology |
| Dr. M. Zafar Iqbal | Shahjalal University of Science and Technology |
| Dr. M. Shahidur Rahman | Shahjalal University of Science and Technology |
|
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] |
taishi-i/nagisa_stopwords
|
taishi-i
| 2023-08-06T17:58:31Z | 57 | 0 |
[
"size_categories:n<1K",
"language:ja",
"license:mit",
"stopwords",
"region:us"
] | null | 2023-08-06T17:10:10Z |
---
license: mit
tags:
- stopwords
pretty_name: stopwords
size_categories:
- n<1K
language:
- ja
---
# Japanese stopwords for nagisa
This is a stopword list of frequently used words in the Japanese language, created according to the tokenization rules of the Japanese text analysis library, [nagisa](https://github.com/taishi-i/nagisa).
This list is constructed by extracting the top 100 most commonly used words from the [CC-100 dataset](https://data.statmt.org/cc-100/) and [Wikipedia](https://dumps.wikimedia.org/other/cirrussearch/).
To access this list of words, simply run the provided program code below.
Please install Huggingface datasets library.
```bash
$ pip install datasets
```
After installing the library, please run the following code next.
```python
from datasets import load_dataset
dataset = load_dataset("taishi-i/nagisa_stopwords")
# the top 100 most commonly used words
words = dataset["nagisa_stopwords"]["words"]
# the part-of-speech list for the top 100 most commonly used words
postags = dataset["nagisa_stopwords"]["postags"]
```
|
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] |
AISE-TUDelft/PY150k
|
AISE-TUDelft
| 2023-08-11T11:47:47Z | 57 | 0 |
[
"size_categories:100K<n<1M",
"license:cc0-1.0",
"code",
"region:us"
] | null | 2023-08-11T09:28:32Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: index
dtype: int64
- name: input
dtype: string
- name: gt
dtype: string
- name: hash
dtype: int64
- name: full_line
dtype: string
splits:
- name: train
num_bytes: 662931126
num_examples: 95000
- name: dev
num_bytes: 41218084
num_examples: 5000
- name: test
num_bytes: 343336086
num_examples: 50000
download_size: 277005224
dataset_size: 1047485296
license: cc0-1.0
tags:
- code
pretty_name: PY150 Line Completion Dataset
size_categories:
- 100K<n<1M
---
# Dataset Card for "PY150k"
## Dataset Summary
Code Completion dataset created from the code available in [CodeXGlue](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-line).
|
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] |
nuprl/MultiPL-T
|
nuprl
| 2023-09-13T12:57:50Z | 57 | 1 |
[
"license:bigcode-openrail-m",
"arxiv:2308.09895",
"region:us"
] | null | 2023-08-17T14:17:33Z |
---
license: bigcode-openrail-m
dataset_info:
features:
- name: content
dtype: string
splits:
- name: racket
num_bytes: 14482516
num_examples: 40510
- name: ocaml
num_bytes: 19240207
num_examples: 43401
- name: lua
num_bytes: 25917278
num_examples: 48194
download_size: 7491686
dataset_size: 59640001
---
# MultiPL-T fine-tuning sets
This dataset contains the MultiPL-T fine-tuning sets described in the paper "Knowledge Transfer from High-Resource to Low-Resource
Programming Languages for Code LLMs": [Arxiv](https://arxiv.org/abs/2308.09895).
## MultiPL-T tuned models
StarCoderBase-1b: https://huggingface.co/nuprl/MultiPLCoder-1b
StarCoderBase-15b: https://huggingface.co/nuprl/MultiPLCoder-15b
|
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] |
rmanluo/RoG-webqsp
|
rmanluo
| 2023-10-01T23:40:22Z | 57 | 1 |
[
"region:us"
] | null | 2023-10-01T23:28:20Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
sequence: string
- name: q_entity
sequence: string
- name: a_entity
sequence: string
- name: graph
sequence:
sequence: string
- name: choices
sequence: 'null'
splits:
- name: train
num_bytes: 993540472
num_examples: 2826
- name: validation
num_bytes: 84009553
num_examples: 246
- name: test
num_bytes: 580788090
num_examples: 1628
download_size: 0
dataset_size: 1658338115
---
# Dataset Card for "RoG-webqsp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
pablo-moreira/wikipedia-pt
|
pablo-moreira
| 2023-10-06T13:52:49Z | 57 | 1 |
[
"region:us"
] | null | 2023-10-06T02:29:43Z |
---
dataset_info:
- config_name: '20231001'
features:
- name: id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2150584347
num_examples: 1857355
download_size: 0
dataset_size: 2150584347
- config_name: latest
features:
- name: id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2150584347
num_examples: 1857355
download_size: 0
dataset_size: 2150584347
configs:
- config_name: '20231001'
data_files:
- split: train
path: 20231001/train-*
- config_name: latest
data_files:
- split: train
path: latest/train-*
---
# Dataset Card for Wikipedia - Portuguese
## Dataset Description
- latest
- 20231001
## Usage
```python
from datasets import load_dataset
dataset = load_dataset('pablo-moreira/wikipedia-pt', 'latest')
#dataset = load_dataset('pablo-moreira/wikipedia-pt', '20231001')
```
## Extractor
Notebook with the code for extracting documents from the Wikipedia dump based on the code from the FastAI NLP introduction course.
[Notebook](extractor.ipynb)
## Links
- **[Wikipedia dumps](https://dumps.wikimedia.org/)**
- **[A Code-First Intro to Natural Language Processing](https://github.com/fastai/course-nlp)**
- **[Extractor Code](https://github.com/fastai/course-nlp/blob/master/nlputils.py)**
|
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benayas/snips
|
benayas
| 2023-11-28T05:19:33Z | 57 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-09T01:10:41Z |
---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 883074
num_examples: 13084
- name: test
num_bytes: 95870
num_examples: 1400
download_size: 414632
dataset_size: 978944
---
|
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] |
NomaDamas/qasper
|
NomaDamas
| 2023-10-21T04:02:27Z | 57 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-10-21T04:02:04Z |
---
license: cc-by-4.0
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: full_text
struct:
- name: paragraphs
sequence:
sequence: string
- name: section_name
sequence: string
- name: qas
struct:
- name: answers
list:
- name: annotation_id
sequence: string
- name: answer
list:
- name: evidence
sequence: string
- name: extractive_spans
sequence: string
- name: free_form_answer
dtype: string
- name: highlighted_evidence
sequence: string
- name: unanswerable
dtype: bool
- name: yes_no
dtype: bool
- name: worker_id
sequence: string
- name: nlp_background
sequence: string
- name: paper_read
sequence: string
- name: question
sequence: string
- name: question_id
sequence: string
- name: question_writer
sequence: string
- name: search_query
sequence: string
- name: topic_background
sequence: string
- name: figures_and_tables
struct:
- name: caption
sequence: string
- name: file
sequence: string
- name: question
sequence: string
- name: retrieval_gt
sequence:
sequence: string
- name: answer_gt
sequence: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 33747492
num_examples: 946
download_size: 16245561
dataset_size: 33747492
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
alfredplpl/simple-zundamon
|
alfredplpl
| 2023-10-21T16:10:17Z | 57 | 1 |
[
"language:ja",
"license:other",
"region:us"
] | null | 2023-10-21T15:16:58Z |
---
license: other
license_name: view-read-more
license_link: https://zunko.jp/guideline.html
language:
- ja
---
# シンプルずんだもんデータセット
## はじめに
ずんだもんの設定が詰まったシンプルなデータセットです。
作者がインターネットで調べたり、運営の人からもらったデータから作成しました。
キャラクターLLMを作るための動作確認にお使いください。
ただし、可能な限り動作確認でもライセンスをよく読んでください。
他の用途はライセンスをよく読んでください。
## 各種フォーマット
- LLM-jp: [zmnjp.jsonl](zmnjp.jsonl)
- ChatGPT: [zmn.jsonl](zmn.jsonl)
## ライセンス
- [（ず・ω・きょ）](https://zunko.jp/guideline.html)
|
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] |
ckandemir/bitcoin_tweets_sentiment_kaggle
|
ckandemir
| 2023-11-06T07:25:54Z | 57 | 0 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:kaggle",
"datasets",
"bitcoin",
"text-classification",
"sentiment-analysis",
"region:us"
] |
[
"text-classification"
] | 2023-10-27T08:01:02Z |
---
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- kaggle
task_categories:
- text-classification
task_ids:
- sentiment-classification
tags:
- datasets
- bitcoin
- text-classification
- sentiment-analysis
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: eval
path: data/eval-*
dataset_info:
features:
- name: Date
dtype: string
- name: text
dtype: string
- name: Sentiment
dtype: string
splits:
- name: train
num_bytes: 12842246
num_examples: 77791
- name: test
num_bytes: 1609120
num_examples: 9724
- name: eval
num_bytes: 1598297
num_examples: 9724
download_size: 9868625
dataset_size: 16049663
---
# Dataset Card for "Bitcoin Tweets ("
## 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)
- [Dataset Distribution](#dataset-distribution)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
### Dataset Summary
This dataset contains a collection of 16 million tweets related to Bitcoin, collected from Twitter. Each tweet is tagged with sentiment (positive, negative, neutral). The dataset was originally created and uploaded to Kaggle by user gauravduttakiit. It is a valuable resource for training and evaluating models for sentiment analysis within the context of cryptocurrency discussions.
### Supported Tasks and Leaderboards
- `text-classification`: This dataset can be used to train a model for sentiment analysis. The performance of the model can be evaluated using standard metrics like accuracy, F1 score, precision, and recall.
### Languages
The text data is primarily in English.
## Dataset Structure
### Data Instances
Each instance in the dataset contains the following fields:
- `tweet`: the text of the tweet.
- `sentiment`: the sentiment of the tweet, labeled as either "positive", "negative", or "neutral".
### Data Fields
- `tweet`: a string containing the text of the tweet.
- `sentiment`: a string indicating the sentiment of the tweet.
### Data Splits
The dataset is not explicitly split into training, validation, and test sets. Users will need to create these splits as per their requirements.
## Dataset Creation
### Curation Rationale
The dataset was curated to analyze the sentiment within the cryptocurrency community, specifically focusing on Bitcoin.
### Source Data
#### Initial Data Collection and Normalization
The data was collected from Twitter using specific keywords related to Bitcoin. For more details regarding data collection, one can refer to the [original Kaggle dataset](https://www.kaggle.com/datasets/gauravduttakiit/bitcoin-tweets-16m-tweets-with-sentiment-tagged).
#### Who are the source data providers?
The data was provided by Kaggle user gauravduttakiit.
### Annotations
The sentiment labels were generated using automated sentiment analysis tools. For more details, refer to the [original Kaggle dataset](https://www.kaggle.com/datasets/gauravduttakiit/bitcoin-tweets-16m-tweets-with-sentiment-tagged).
## Dataset Distribution
### Dataset Curators
The dataset was curated by gauravduttakiit and uploaded to Kaggle.
### Licensing Information
Refer to the [original Kaggle dataset](https://www.kaggle.com/datasets/gauravduttakiit/bitcoin-tweets-16m-tweets-with-sentiment-tagged) for licensing information.
|
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ajax-law/contrastive-matters-2
|
ajax-law
| 2023-11-06T19:35:19Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-06T19:35:17Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text_a
dtype: string
- name: text_b
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 3232779
num_examples: 6549
- name: test
num_bytes: 41112
num_examples: 90
download_size: 100367
dataset_size: 3273891
---
# Dataset Card for "contrastive-matters-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
kpriyanshu256/semeval-task-8-a-multi-v2
|
kpriyanshu256
| 2023-11-07T13:15:37Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-07T13:15:01Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: model
dtype: string
- name: source
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 362229271
num_examples: 137933
- name: val
num_bytes: 90513705
num_examples: 34484
- name: test
num_bytes: 8790338
num_examples: 4000
download_size: 265430410
dataset_size: 461533314
---
# Dataset Card for "semeval-task-8-a-multi-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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BENBENBENb/ARC1000COT
|
BENBENBENb
| 2023-11-10T00:14:29Z | 57 | 0 |
[
"task_categories:question-answering",
"language:en",
"region:us"
] |
[
"question-answering"
] | 2023-11-10T00:12:51Z |
---
task_categories:
- question-answering
language:
- en
---
|
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] |
tomrb/minipileoflaw
|
tomrb
| 2023-11-24T20:51:47Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-10T11:42:07Z |
---
configs:
- config_name: acus_reports
data_files:
- split: train
path: "data/minipileoflaw_acus_reports_train.csv"
- split: valid
path: "data/minipileoflaw_acus_reports_valid.csv"
---
|
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] |
jxie/covtype
|
jxie
| 2023-11-11T07:08:14Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-11T07:08:05Z |
---
dataset_info:
features:
- name: inputs
sequence: float64
- name: label
dtype: float64
splits:
- name: train
num_bytes: 165100068
num_examples: 371847
- name: val
num_bytes: 41275128
num_examples: 92962
- name: test
num_bytes: 51594132
num_examples: 116203
download_size: 26860361
dataset_size: 257969328
---
# Dataset Card for "covtype"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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shredder-31/NeuraLearnAcademy
|
shredder-31
| 2023-11-25T01:37:13Z | 57 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-14T16:40:40Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 14885576
num_examples: 5087
download_size: 3107395
dataset_size: 14885576
---
|
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higgsfield/school-math-questions
|
higgsfield
| 2023-11-14T22:29:19Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-14T22:29:15Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 4787332
num_examples: 8792
download_size: 2576099
dataset_size: 4787332
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "school-math-questions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jlbaker361/small_division_whole
|
jlbaker361
| 2023-11-17T05:53:41Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-17T04:47:34Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1231.111111111111
num_examples: 32
- name: test
num_bytes: 153.88888888888889
num_examples: 4
download_size: 4157
dataset_size: 1385.0
---
# Dataset Card for "small_division_whole"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
knowrohit07/know-saraswati-cot
|
knowrohit07
| 2023-11-21T22:39:23Z | 57 | 11 |
[
"license:openrail",
"region:us"
] | null | 2023-11-17T08:46:58Z |
---
license: openrail
---
### 🚨 To all devs, scholars, and also fugazis of AI - A Philosophical Standpoint on AGI:
- This is extraneous, if you have time to read it-- give it a shot. We stand at the precipice of a digital era where the notions of artificial intelligence are often muddled with the grandiose idea of Artificial General Intelligence (AGI). Here's a candid reflection:
1. Current LLMs and their Limitations: Let's be unequivocally clear—present-day language models, including transformers, are not a direct path to AGI. They are sophisticated token predictors, highly skilled in generalizing from vast datasets but lacking true understanding. They operate in what might be termed the 'dog-AGI' phase—impressive, yes, but nowhere close to the 'god-AGI' phase we aspire to reach.
2. The Nature of 'Smart': These models, for all their complexity, are not sentient. They don't possess the rich tapestry of human experience—our memories, relationships, and 'eureka' moments that constitute learning and wisdom. They are yet to evolve from merely processing information to experiencing and understanding the nuances of life as we know it.
3. Stockpiling NVIDIA cards and accumulating GPU clusters is not the golden ticket to AGI. The pursuit of AGI is not solely a quest for more processing power. It is a deeper, more philosophical journey where:
- Space Outposts and Ion Engines: Mankind should expandd beyond the terrestrial, reaching for space outposts and harnessing commercialized ion engines for space travel. Ion engines, with their extended operational capacity, liberate us from the constraints of chemical fuel, enabling voyages that stretch both time and distance.
- Asteroid Mining and the Periodic Table: The quest for AGI is mirrored in our endeavor to mine asteroids, potentially revealing new elements that could add unknown dimensions to our periodic table. This is not merely resource extraction; it is an exploration that feeds into the self-iterative learning nature of AGI, fostering an intelligence that grows with each discovery.
- Nuclear Mass Energy and Helium-3: We look beyond silicon to the immense potential of nuclear mass energy. Helium-3, fused from deuterium in high-efficiency fusion generators, represents a future energy source that could power the next leaps in AGI development. Overcoming the scarcity of Helium-3 is a challenge we are poised to tackle, paving the way for a new era of energy abundance.
4. The Road Ahead: As we venture into the unknown, let's reimagine our approach. We seek an AI that lives a 'life', so to speak, with context vectors representing not just data points but the essence of existence itself. Imagine an AI with a library of experiences, including life choices and personal growth, akin to a human with 60 years of rich, varied living.
## Overview
The know-saraswati-cot dataset is a curated collection of examples designed to train and evaluate large language models (LLMs) on stream of consciousness (SoC), chain of thought (CoT), and logical reasoning. Named after Saraswati, the Hindu goddess of knowledge, wisdom, and learning, this dataset embodies the spirit of open-source knowledge sharing. It is an ode to democratizing knowledge, making it as accessible as the flowing waters of the mythical Saraswati river.
With addtional 30,000 code reasoning examples and various other deep reasoning scenarios, this dataset aims to imbue LLMs with a profound capacity for understanding, reasoning, and decision-making.
## Dataset Structure
Each entry in the know-saraswati-cot dataset comprises an instruction and an output field. Same old stuff, i like this format. The instruction provides a scenario or question that requires deep thinking, inviting the model to engage in a step-by-step reasoning process. The output then captures a reasoned response that aligns with the principles of logical deduction and stream of consciousness thought.
The know-saraswati-cot dataset has been meticulously crafted to reflect the intricacies of human-like reasoning. Here are some key specifications:
- Concise Reasoning: The majority of examples are concisely formulated within 500 tokens, fostering quick and efficient chains of thought (CoT). This simulates the succinct yet profound reasoning processes akin to human cognition.
- Multi-Turn Interactions: Some entries are designed as multi-turn interactions, allowing models to engage in a deeper and more dynamic discourse. This emulates real-world conversations where dialogues build upon previous exchanges.
- Extended Discussions: A subset of the dataset accommodates scenarios extending up to 2000 tokens for comprehensive reasoning tasks. These are tailored to model how a sapient being would thoughtfully respond to complex logic puzzles, as opposed to the often superficial and tangential responses generated by less sophisticated models.
- Each example is tailored to how an actual sapien would reason and respond, capturing the essence of human logic, emotion, and cognition. This approach aims to steer AI responses away from the undeveloped and extraneous (which usually llms respond with), guiding them towards relevance and depth that truly address the query at hand.
## Inspiration
Inspired by the vision of making knowledge free and accessible for all, akin to the way Goddess Saraswati is revered for her gifts of learning and enlightenment, this dataset was synthesized using GPT-4. A special pranaam and blessings 🙏 from my brother, whose vision of a frugally enlightened world where knowledge is a common wealth has been the cornerstone of this endeavor.
## Use Cases
The know-saraswati-cot dataset can be utilized to:
1. By providing rich, nuanced examples of logical reasoning, the dataset is perfect for developing models that can mimic the depth of human thought processes.
2. Researchers can leverage the dataset to investigate how AI models can not only reach conclusions but also articulate the reasoning behind their decisions, making AI workings more transparent.
3. know-saraswati-cot can foster AI development that intersects with philosophy, literature, and the Engineering, encouraging holistic and multidimensional growth in AI capabilities.
4. have fun
|
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] |
Ransaka/SinhalaASR
|
Ransaka
| 2023-11-20T09:16:06Z | 57 | 0 |
[
"size_categories:100K<n<1M",
"language:si",
"region:us"
] | null | 2023-11-19T03:17:57Z |
---
language:
- si
pretty_name: sinhala-asr
size_categories:
- 100K<n<1M
---
# About this dataset:
This data set contains transcribed audio data for Sinhala. The data set has been manually quality checked, but there might still be errors.
See LICENSE.txt file for license information.
Copyright 2016, 2017, 2018 Google, Inc.
If you use this data in publications, please cite it as follows:
```
@inproceedings{kjartansson-etal-sltu2018,
title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}},
author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
year = {2018},
address = {Gurugram, India},
month = aug,
pages = {52--55},
URL = {http://dx.doi.org/10.21437/SLTU.2018-11}
}
```
|
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] |
fformosa/composability_feliu
|
fformosa
| 2023-11-22T22:21:12Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-22T22:21:09Z |
---
dataset_info:
features:
- name: url
dtype: string
- name: caption
dtype: string
- name: id
dtype: int64
- name: similarity
dtype: string
- name: image
dtype: image
- name: count_id
dtype: int64
- name: type
dtype: string
- name: indices
sequence: int64
- name: dataset_id
dtype: int64
splits:
- name: train
num_bytes: 2541387.0
num_examples: 40
download_size: 2494356
dataset_size: 2541387.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "composability_feliu"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
lecslab/glosslm
|
lecslab
| 2023-12-01T23:32:07Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-23T02:52:57Z |
---
dataset_info:
features:
- name: ID
dtype: string
- name: glottocode
dtype: string
- name: transcription
dtype: string
- name: glosses
dtype: string
- name: translation
dtype: string
- name: metalang_glottocode
dtype: string
- name: is_segmented
dtype: string
- name: source
dtype: string
- name: type
dtype: string
- name: language
dtype: string
- name: metalang
dtype: string
splits:
- name: train
num_bytes: 99279285
num_examples: 425020
download_size: 29157618
dataset_size: 99279285
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "glosslm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
shaheenahmedc/goal_captions
|
shaheenahmedc
| 2023-11-23T15:35:41Z | 57 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-23T15:35:18Z |
---
license: unknown
---
|
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] |
tyzhu/ds1_try_lora_merge
|
tyzhu
| 2023-11-28T07:49:05Z | 57 | 0 |
[
"region:us"
] | null | 2023-11-28T07:20:51Z |
---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 1044.247619047619
num_examples: 10
- name: validation
num_bytes: 1044.247619047619
num_examples: 10
download_size: 4678
dataset_size: 2088.495238095238
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "ds1_try_lora_merge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
lhoestq/custom_squad
|
lhoestq
| 2022-10-25T09:50:53Z | 56 | 0 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
"arxiv:1606.05250",
"region:us"
] |
[
"question-answering"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for "squad"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits Sample Size](#data-splits-sample-size)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 33.51 MB
- **Size of the generated dataset:** 85.75 MB
- **Total amount of disk used:** 119.27 MB
### Dataset Summary
This dataset is a custom copy of the original SQuAD dataset. It is used to showcase dataset repositories. Data are the same as the original dataset.
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
### Supported Tasks
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 33.51 MB
- **Size of the generated dataset:** 85.75 MB
- **Total amount of disk used:** 119.27 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [1],
"text": ["This is a test text"]
},
"context": "This is a test context.",
"id": "1",
"question": "Is this a test?",
"title": "train test"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits Sample Size
| name |train|validation|
|----------|----:|---------:|
|plain_text|87599| 10570|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
### Annotations
[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
```
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
|
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iluvvatar/NEREL
|
iluvvatar
| 2023-03-30T13:37:20Z | 56 | 4 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"language:ru",
"region:us"
] |
[
"token-classification"
] | 2022-04-07T09:03:51Z |
---
language:
- ru
multilinguality:
- monolingual
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: NEREL
---
# NEREL dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Citation Information](#citation-information)
- [Contacts](#contacts)
## Dataset Description
NEREL dataset (https://doi.org/10.48550/arXiv.2108.13112) is
a Russian dataset for named entity recognition and relation extraction.
NEREL is significantly larger than existing Russian datasets:
to date it contains 56K annotated named entities and 39K annotated relations.
Its important difference from previous datasets is annotation of nested named
entities, as well as relations within nested entities and at the discourse
level. NEREL can facilitate development of novel models that can extract
relations between nested named entities, as well as relations on both sentence
and document levels. NEREL also contains the annotation of events involving
named entities and their roles in the events.
You can see full entity types list in a subset "ent_types"
and full list of relation types in a subset "rel_types".
## Dataset Structure
There are three "configs" or "subsets" of the dataset.
Using
`load_dataset('MalakhovIlya/NEREL', 'ent_types')['ent_types']`
you can download list of entity types (
Dataset({features: ['type', 'link']})
) where "link" is a knowledge base name used in entity linking task.
Using
`load_dataset('MalakhovIlya/NEREL', 'rel_types')['rel_types']`
you can download list of entity types (
Dataset({features: ['type', 'arg1', 'arg2']})
) where "arg1" and "arg2" are lists of entity types that can take part in such
"type" of relation. \<ENTITY> stands for any type.
Using
`load_dataset('MalakhovIlya/NEREL', 'data')` or `load_dataset('MalakhovIlya/NEREL')`
you can download the data itself,
DatasetDict with 3 splits: "train", "test" and "dev".
Each of them contains text document with annotated entities, relations and
links.
"entities" are used in named-entity recognition task (see https://en.wikipedia.org/wiki/Named-entity_recognition).
"relations" are used in relationship extraction task (see https://en.wikipedia.org/wiki/Relationship_extraction).
"links" are used in entity linking task (see https://en.wikipedia.org/wiki/Entity_linking)
Each entity is represented by a string of the following format:
`"<id>\t<type> <start> <stop>\t<text>"`, where
`<id>` is an entity id,
`<type>` is one of entity types,
`<start>` is a position of the first symbol of entity in text,
`<stop>` is the last symbol position in text +1.
Each relation is represented by a string of the following format:
`"<id>\t<type> Arg1:<arg1_id> Arg2:<arg2_id>"`, where
`<id>` is a relation id,
`<arg1_id>` and `<arg2_id>` are entity ids.
Each link is represented by a string of the following format:
`"<id>\tReference <ent_id> <link>\t<text>"`, where
`<id>` is a link id,
`<ent_id>` is an entity id,
`<link>` is a reference to knowledge base entity (example: "Wikidata:Q1879675" if link exists, else "Wikidata:NULL"),
`<text>` is a name of entity in knowledge base if link exists, else empty string.
## Citation Information
@article{loukachevitch2021nerel,
title={NEREL: A Russian Dataset with Nested Named Entities, Relations and Events},
author={Loukachevitch, Natalia and Artemova, Ekaterina and Batura, Tatiana and Braslavski, Pavel and Denisov, Ilia and Ivanov, Vladimir and Manandhar, Suresh and Pugachev, Alexander and Tutubalina, Elena},
journal={arXiv preprint arXiv:2108.13112},
year={2021}
}
|
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autoevaluate/autoeval-staging-eval-project-sms_spam-216c1ded-12215630
|
autoevaluate
| 2022-08-02T10:41:15Z | 56 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-08-02T10:40:39Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- sms_spam
eval_info:
task: binary_classification
model: Rhuax/MiniLMv2-L12-H384-distilled-finetuned-spam-detection
metrics: []
dataset_name: sms_spam
dataset_config: plain_text
dataset_split: train
col_mapping:
text: sms
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: Rhuax/MiniLMv2-L12-H384-distilled-finetuned-spam-detection
* Dataset: sms_spam
* Config: plain_text
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Al-Ip](https://huggingface.co/Al-Ip) for evaluating this model.
|
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SLPL/naab
|
SLPL
| 2022-11-03T06:33:48Z | 56 | 28 |
[
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:fa",
"license:mit",
"arxiv:2208.13486",
"region:us"
] |
[
"fill-mask",
"text-generation"
] | 2022-08-18T13:47:40Z |
---
language:
- fa
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: naab (A ready-to-use plug-and-play corpus in Farsi)
---
# naab: A ready-to-use plug-and-play corpus in Farsi
_[If you want to join our community to keep up with news, models and datasets from naab, click on [this](https://docs.google.com/forms/d/e/1FAIpQLSe8kevFl_ODCx-zapAuOIAQYr8IvkVVaVHOuhRL9Ha0RVJ6kg/viewform) link.]_
## 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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Sharif Speech and Language Processing Lab](https://huggingface.co/SLPL)
- **Paper:** [naab: A ready-to-use plug-and-play corpus for Farsi](https://arxiv.org/abs/2208.13486)
- **Point of Contact:** [Sadra Sabouri](mailto:[email protected])
### Dataset Summary
naab is the biggest cleaned and ready-to-use open-source textual corpus in Farsi. It contains about 130GB of data, 250 million paragraphs, and 15 billion words. The project name is derived from the Farsi word ناب which means pure and high-grade. We also provide the raw version of the corpus called naab-raw and an easy-to-use pre-processor that can be employed by those who wanted to make a customized corpus.
You can use this corpus by the commands below:
```python
from datasets import load_dataset
dataset = load_dataset("SLPL/naab")
```
You may need to download parts/splits of this corpus too, if so use the command below (You can find more ways to use it [here](https://huggingface.co/docs/datasets/loading#slice-splits)):
```python
from datasets import load_dataset
dataset = load_dataset("SLPL/naab", split="train[:10%]")
```
**Note: be sure that your machine has at least 130 GB free space, also it may take a while to download. If you are facing disk or internet shortage, you can use below code snippet helping you download your costume sections of the naab:**
```python
from datasets import load_dataset
# ==========================================================
# You should just change this part in order to download your
# parts of corpus.
indices = {
"train": [5, 1, 2],
"test": [0, 2]
}
# ==========================================================
N_FILES = {
"train": 126,
"test": 3
}
_BASE_URL = "https://huggingface.co/datasets/SLPL/naab/resolve/main/data/"
data_url = {
"train": [_BASE_URL + "train-{:05d}-of-{:05d}.txt".format(x, N_FILES["train"]) for x in range(N_FILES["train"])],
"test": [_BASE_URL + "test-{:05d}-of-{:05d}.txt".format(x, N_FILES["test"]) for x in range(N_FILES["test"])],
}
for index in indices['train']:
assert index < N_FILES['train']
for index in indices['test']:
assert index < N_FILES['test']
data_files = {
"train": [data_url['train'][i] for i in indices['train']],
"test": [data_url['test'][i] for i in indices['test']]
}
print(data_files)
dataset = load_dataset('text', data_files=data_files, use_auth_token=True)
```
### Supported Tasks and Leaderboards
This corpus can be used for training all language models which can be trained by Masked Language Modeling (MLM) or any other self-supervised objective.
- `language-modeling`
- `masked-language-modeling`
## Dataset Structure
Each row of the dataset will look like something like the below:
```json
{
'text': "این یک تست برای نمایش یک پاراگراف در پیکره متنی ناب است.",
}
```
+ `text` : the textual paragraph.
### Data Splits
This dataset includes two splits (`train` and `test`). We split these two by dividing the randomly permuted version of the corpus into (95%, 5%) division respected to (`train`, `test`). Since `validation` is usually occurring during training with the `train` dataset we avoid proposing another split for it.
| | train | test |
|-------------------------|------:|-----:|
| Input Sentences | 225892925 | 11083849 |
| Average Sentence Length | 61 | 25 |
Below you can see the log-based histogram of word/paragraph over the two splits of the dataset.
<div align="center">
<img src="https://huggingface.co/datasets/SLPL/naab/resolve/main/naab-hist.png">
</div>
## Dataset Creation
### Curation Rationale
Due to the lack of a huge amount of text data in lower resource languages - like Farsi - researchers working on these languages were always finding it hard to start to fine-tune such models. This phenomenon can lead to a situation in which the golden opportunity for fine-tuning models is just in hands of a few companies or countries which contributes to the weakening the open science.
The last biggest cleaned merged textual corpus in Farsi is a 70GB cleaned text corpus from a compilation of 8 big data sets that have been cleaned and can be downloaded directly. Our solution to the discussed issues is called naab. It provides **126GB** (including more than **224 million** sequences and nearly **15 billion** words) as the training corpus and **2.3GB** (including nearly **11 million** sequences and nearly **300 million** words) as the test corpus.
### Source Data
The textual corpora that we used as our source data are illustrated in the figure below. It contains 5 corpora which are linked in the coming sections.
<div align="center">
<img src="https://huggingface.co/datasets/SLPL/naab/resolve/main/naab-pie.png">
</div>
#### Persian NLP
[This](https://github.com/persiannlp/persian-raw-text) corpus includes eight corpora that are sorted based on their volume as below:
- [Common Crawl](https://commoncrawl.org/): 65GB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/commoncrawl_fa_merged.txt))
- [MirasText](https://github.com/miras-tech/MirasText): 12G
- [W2C – Web to Corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0022-6133-9): 1GB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/w2c_merged.txt))
- Persian Wikipedia (March 2020 dump): 787MB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/fawiki_merged.txt))
- [Leipzig Corpora](https://corpora.uni-leipzig.de/): 424M ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/LeipzigCorpus.txt))
- [VOA corpus](https://jon.dehdari.org/corpora/): 66MB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/voa_persian_2003_2008_cleaned.txt))
- [Persian poems corpus](https://github.com/amnghd/Persian_poems_corpus): 61MB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/poems_merged.txt))
- [TEP: Tehran English-Persian parallel corpus](http://opus.nlpl.eu/TEP.php): 33MB ([link](https://storage.googleapis.com/danielk-files/farsi-text/merged_files/TEP_fa.txt))
#### AGP
This corpus was a formerly private corpus for ASR Gooyesh Pardaz which is now published for all users by this project. This corpus contains more than 140 million paragraphs summed up in 23GB (after cleaning). This corpus is a mixture of both formal and informal paragraphs that are crawled from different websites and/or social media.
#### OSCAR-fa
[OSCAR](https://oscar-corpus.com/) or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the go classy architecture. Data is distributed by language in both original and deduplicated form. We used the unshuffled-deduplicated-fa from this corpus, after cleaning there were about 36GB remaining.
#### Telegram
Telegram, a cloud-based instant messaging service, is a widely used application in Iran. Following this hypothesis, we prepared a list of Telegram channels in Farsi covering various topics including sports, daily news, jokes, movies and entertainment, etc. The text data extracted from mentioned channels mainly contains informal data.
#### LSCP
[The Large Scale Colloquial Persian Language Understanding dataset](https://iasbs.ac.ir/~ansari/lscp/) has 120M sentences from 27M casual Persian sentences with its derivation tree, part-of-speech tags, sentiment polarity, and translations in English, German, Czech, Italian, and Hindi. However, we just used the Farsi part of it and after cleaning we had 2.3GB of it remaining. Since the dataset is casual, it may help our corpus have more informal sentences although its proportion to formal paragraphs is not comparable.
#### Initial Data Collection and Normalization
The data collection process was separated into two parts. In the first part, we searched for existing corpora. After downloading these corpora we started to crawl data from some social networks. Then thanks to [ASR Gooyesh Pardaz](https://asr-gooyesh.com/en/) we were provided with enough textual data to start the naab journey.
We used a preprocessor based on some stream-based Linux kernel commands so that this process can be less time/memory-consuming. The code is provided [here](https://github.com/Sharif-SLPL/t5-fa/tree/main/preprocess).
### Personal and Sensitive Information
Since this corpus is briefly a compilation of some former corpora we take no responsibility for personal information included in this corpus. If you detect any of these violations please let us know, we try our best to remove them from the corpus ASAP.
We tried our best to provide anonymity while keeping the crucial information. We shuffled some parts of the corpus so the information passing through possible conversations wouldn't be harmful.
## Additional Information
### Dataset Curators
+ Sadra Sabouri (Sharif University of Technology)
+ Elnaz Rahmati (Sharif University of Technology)
### Licensing Information
mit?
### Citation Information
```
@article{sabouri2022naab,
title={naab: A ready-to-use plug-and-play corpus for Farsi},
author={Sabouri, Sadra and Rahmati, Elnaz and Gooran, Soroush and Sameti, Hossein},
journal={arXiv preprint arXiv:2208.13486},
year={2022}
}
```
DOI: [https://doi.org/10.48550/arXiv.2208.13486](https://doi.org/10.48550/arXiv.2208.13486)
### Contributions
Thanks to [@sadrasabouri](https://github.com/sadrasabouri) and [@elnazrahmati](https://github.com/elnazrahmati) for adding this dataset.
### Keywords
+ Farsi
+ Persian
+ raw text
+ پیکره فارسی
+ پیکره متنی
+ آموزش مدل زبانی
|
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] |
Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75
|
Jean-Baptiste
| 2022-12-29T03:19:16Z | 56 | 1 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-3.0",
"region:us"
] |
[
"text-classification"
] | 2022-12-24T03:49:34Z |
---
language:
- en
dataset_info:
splits:
- name: test
num_examples: 785
- name: train
num_examples: 4446
annotations_creators:
- expert-generated
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
pretty_name: financial_news_sentiment_mixte_with_phrasebank_75
size_categories:
- 1K<n<10K
tags: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
---
# Dataset Card for "financial_news_sentiment_mixte_with_phrasebank_75"
This is a customized version of the phrasebank dataset in which I kept only sentences validated by at least 75% annotators.
In addition I added ~2000 articles of Canadian news where sentiment was validated manually.
The dataset also include a column topic which contains one of the following value:
* acquisition
* other
* quaterly financial release
* appointment to new position
* dividend
* corporate update
* drillings results
* conference
* share repurchase program
* grant of stocks
This was generated automatically using a zero-shot classification model and **was not** reviewed manually.
## References
Original dataset is available here:
[https://huggingface.co/datasets/financial_phrasebank]
|
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] |
dbarbedillo/SMS_Spam_Multilingual_Collection_Dataset
|
dbarbedillo
| 2023-01-13T03:07:17Z | 56 | 6 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"language:es",
"language:hi",
"language:fr",
"language:de",
"language:ar",
"language:bn",
"language:ru",
"language:pt",
"language:id",
"language:ur",
"language:ja",
"language:pa",
"language:jv",
"language:tr",
"language:ko",
"language:mr",
"language:uk",
"language:sv",
"language:no",
"license:gpl",
"region:us"
] |
[
"text-classification"
] | 2023-01-13T02:13:03Z |
---
license: gpl
task_categories:
- text-classification
language:
- en
- zh
- es
- hi
- fr
- de
- ar
- bn
- ru
- pt
- id
- ur
- ja
- pa
- jv
- tr
- ko
- mr
- uk
- sv
- 'no'
size_categories:
- 1K<n<10K
---
SMS Spam Multilingual Collection Dataset
Collection of Multilingual SMS messages tagged as spam or legitimate
About Dataset
Context
The SMS Spam Collection is a set of SMS-tagged messages that have been collected for SMS Spam research. It originally contained one set of SMS messages in English of 5,574 messages, tagged according to being ham (legitimate) or spam and later Machine Translated into Hindi, German and French.
The text has been further translated into Spanish, Chinese, Arabic, Bengali, Russian, Portuguese, Indonesian, Urdu, Japanese, Punjabi, Javanese, Turkish, Korean, Marathi, Ukrainian, Swedish, and Norwegian using M2M100_418M a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation created by Facebook AI.
Content
The augmented Dataset contains multilingual text and corresponding labels.
ham- non-spam text
spam- spam text
Acknowledgments
The original English text was taken from- https://www.kaggle.com/uciml/sms-spam-collection-dataset
Hindi, German and French taken from - https://www.kaggle.com/datasets/rajnathpatel/multilingual-spam-data
|
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] |
nanyy1025/covid_fake_news
|
nanyy1025
| 2023-02-24T01:36:24Z | 56 | 2 |
[
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"language:en",
"arxiv:2011.03327",
"region:us"
] |
[
"text-classification",
"zero-shot-classification"
] | 2023-02-24T01:01:04Z |
---
task_categories:
- text-classification
- zero-shot-classification
language:
- en
---
Constraint@AAAI2021 - COVID19 Fake News Detection in English
```
@misc{patwa2020fighting,
title={Fighting an Infodemic: COVID-19 Fake News Dataset},
author={Parth Patwa and Shivam Sharma and Srinivas PYKL and Vineeth Guptha and Gitanjali Kumari and Md Shad Akhtar and Asif Ekbal and Amitava Das and Tanmoy Chakraborty},
year={2020},
eprint={2011.03327},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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hoskinson-center/minif2f-lean4
|
hoskinson-center
| 2023-05-03T21:20:52Z | 56 | 8 |
[
"license:mit",
"region:us"
] | null | 2023-05-03T01:27:24Z |
---
license: mit
---
A port of [miniF2F](https://github.com/facebookresearch/miniF2F) to Lean 4.
|
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turkish-nlp-suite/turkish-wikiNER
|
turkish-nlp-suite
| 2023-09-26T10:37:00Z | 56 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:tr",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"token-classification"
] | 2023-05-07T16:41:04Z |
---
language:
- tr
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Turkish-WikiNER
---
# Dataset Card for "turkish-nlp-suite/turkish-wikiNER"
<img src="https://raw.githubusercontent.com/turkish-nlp-suite/.github/main/profile/wiki.png" width="20%" height="20%">
## Dataset Description
- **Repository:** [Turkish-WikiNER](https://github.com/turkish-nlp-suite/Turkish-Wiki-NER-Dataset)
- **Paper:** [ACL link]()
- **Dataset:** Turkish-WikiNER
- **Domain:** Wiki
- **Number of Labels:** 18
### Dataset Summary
Turkish NER dataset from Wikipedia sentences. 20.000 sentences are sampled and re-annotated from [Kuzgunlar NER dataset](https://data.mendeley.com/datasets/cdcztymf4k/1).
Annotations are done by [Co-one](https://co-one.co/). Many thanks to them for their contributions. This dataset is also used in our brand new spaCy Turkish packages.
### Dataset Instances
An instance of this dataset looks as follows:
```
{
"tokens": ["Çekimler", "5", "Temmuz", "2005", "tarihinde", "Reebok", "Stadyum", ",", "Bolton", ",", "İngiltere'de", "yapılmıştır", "."],
"tags": [O", "B-DATE", "I-DATE", "I-DATE", "O", "B-FAC", "I-FAC", "O", "B-GPE", "O", "B-GPE", "O", "O"]
}
```
or even better:
### Labels
- CARDINAL
- DATE
- EVENT
- FAC
- GPE
- LANGUAGE
- LAW
- LOC
- MONEY
- NORP
- ORDINAL
- ORG
- PERCENT
- PERSON
- PRODUCT
- QUANTITY
- TIME
- TITLE
- WORK_OF_ART
### Data Split
| name |train|validation|test|
|---------|----:|---------:|---:|
|Turkish-WikiNER|18000| 1000|1000|
### Citation
This work is supported by Google Developer Experts Program. Part of Duygu 2022 Fall-Winter collection, "Turkish NLP with Duygu"/ "Duygu'yla Türkçe NLP". All rights reserved. If you'd like to use this dataset in your own work, please kindly cite [A Diverse Set of Freely Available Linguistic Resources for Turkish](https://aclanthology.org/2023.acl-long.768/) :
```
@inproceedings{altinok-2023-diverse,
title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish",
author = "Altinok, Duygu",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.768",
pages = "13739--13750",
abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.",
}
```
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dev2bit/es2bash
|
dev2bit
| 2023-05-23T21:11:43Z | 56 | 3 |
[
"task_categories:text-generation",
"language:es",
"license:apache-2.0",
"code",
"region:us"
] |
[
"text-generation"
] | 2023-05-23T20:25:37Z |
---
license: apache-2.0
task_categories:
- text-generation
language:
- es
tags:
- code
---
# ES2Bash
This dataset contains a collection of natural language requests (in Spanish) and their corresponding bash commands. The purpose of this dataset is to provide examples of requests and their associated bash commands to facilitate machine learning and the development of natural language processing systems related to command-line operations.
# Features
The dataset consists of two main features:
* Natural Language Request (ES): This feature contains natural language requests written in Spanish. The requests represent tasks or actions to be performed using command-line commands.
* Bash Command: This feature contains the bash commands associated with each natural language request. The bash commands represent the way to execute the requested task or action using the command line.
# Initial Commands
The dataset initially contains requests related to the following commands:
* cat: Requests involving reading text files.
* ls: Requests related to obtaining information about files and directories at a specific location.
* cd: Requests to change the current directory.
# Dataset Expansion
In addition to the initial commands mentioned above, there are plans to expand this dataset to include more common command-line commands. The expansion will cover a broader range of tasks and actions that can be performed using command-line operations.
Efforts will also be made to improve the existing examples and ensure that they are clear, accurate, and representative of typical requests that users may have when working with command lines.
# Request Statistics
In the future, statistical data will be provided on the requests present in this dataset. This data may include information about the distribution of requests in different categories, the frequency of use of different commands, and any other relevant analysis to better understand the usage and needs of command-line users.
# Request Collection Process
This dataset is the result of a combination of requests generated by language models and manually added requests. The requests generated by language models were based on existing examples and prior knowledge related to the usage of command lines. A manual review was then conducted to ensure the quality and relevance of the requests.
|
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tasksource/leandojo
|
tasksource
| 2023-06-28T17:46:34Z | 56 | 3 |
[
"license:cc-by-2.0",
"region:us"
] | null | 2023-06-28T17:41:51Z |
---
license: cc-by-2.0
---
https://github.com/lean-dojo/LeanDojo
```
@article{yang2023leandojo,
title={{LeanDojo}: Theorem Proving with Retrieval-Augmented Language Models},
author={Yang, Kaiyu and Swope, Aidan and Gu, Alex and Chalamala, Rahul and Song, Peiyang and Yu, Shixing and Godil, Saad and Prenger, Ryan and Anandkumar, Anima},
journal={arXiv preprint arXiv:2306.15626},
year={2023}
}
```
|
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] |
heliosbrahma/mental_health_conversational_dataset
|
heliosbrahma
| 2023-07-22T11:30:56Z | 56 | 6 |
[
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:mit",
"medical",
"region:us"
] |
[
"text-generation",
"conversational"
] | 2023-07-21T06:58:12Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 102904
num_examples: 154
download_size: 60865
dataset_size: 102904
license: mit
task_categories:
- text-generation
- conversational
language:
- en
tags:
- medical
pretty_name: Mental Health Conversational Dataset
size_categories:
- n<1K
---
# Dataset Card for "mental_health_conversational_dataset"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
### Dataset Summary
This dataset contains conversational pair of questions and answers in a single text related to Mental Health. Dataset was curated from healthcare websites, popular blogs like WebMD and HeatlhLine, online FAQs etc. All questions and answers have been anonymized to remove any PII data and pre-processed to remove any unwanted characters.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
A data instance include a text columns which is a conversational pair of questions and answers. Questions were asked by the patients and answers were given by healthcare providers.
### Data Fields
- 'text': conversational pair of questions and answers between patient and healthcare provider.
## Dataset Creation
### Curation Rationale
Chatbots offer a readily available and accessible platform for individuals seeking support. They can be accessed anytime and anywhere, providing immediate assistance to those in need. Chatbots can offer empathetic and non-judgmental responses, providing emotional support to users. While they cannot replace human interaction entirely, they can be a helpful supplement, especially in moments of distress.
Hence, this dataset was curated to help finetune a conversational AI bot using this custom dataset which can then be deployed and be provided to the end patient as a chatbot.
### Source Data
This dataset was curated from healthcare websites, popular blogs like WebMD and HeatlhLine, online FAQs etc.
### Personal and Sensitive Information
The dataset may contain sensitive information related to mental health. All questions and answers have been anonymized to remove any PII data.
|
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baber/agieval
|
baber
| 2023-10-26T00:49:22Z | 56 | 2 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:mit",
"arxiv:2304.06364",
"region:us"
] |
[
"question-answering",
"text-generation"
] | 2023-07-23T00:31:09Z |
---
license: mit
language:
- en
task_categories:
- question-answering
- text-generation
pretty_name: AGIEval
---
# Dataset Card for AGIEval
## Dataset Description
- **Homepage:** https://github.com/microsoft/AGIEval/blob/main/README.md
- **Repository:** https://github.com/microsoft/AGIEval
- **Paper:** https://arxiv.org/abs/2304.06364
### Dataset Summary
AGIEval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and problem-solving. This benchmark is derived from 20 official, public, and high-standard admission and qualification exams intended for general human test-takers, such as general college admission tests (e.g., Chinese College Entrance Exam (Gaokao) and American SAT), law school admission tests, math competitions, lawyer qualification tests, and national civil service exams.
### Citation Information
Dataset taken from the AGIEval Repo.
```
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Citation for each dataset:
```
@inproceedings{ling-etal-2017-program,
title = "Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems",
author = "Ling, Wang and
Yogatama, Dani and
Dyer, Chris and
Blunsom, Phil",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1015",
doi = "10.18653/v1/P17-1015",
pages = "158--167",
abstract = "Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.",
}
@inproceedings{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
}
@inproceedings{Liu2020LogiQAAC,
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
booktitle={International Joint Conference on Artificial Intelligence},
year={2020}
}
@inproceedings{zhong2019jec,
title={JEC-QA: A Legal-Domain Question Answering Dataset},
author={Zhong, Haoxi and Xiao, Chaojun and Tu, Cunchao and Zhang, Tianyang and Liu, Zhiyuan and Sun, Maosong},
booktitle={Proceedings of AAAI},
year={2020},
}
@article{Wang2021FromLT,
title={From LSAT: The Progress and Challenges of Complex Reasoning},
author={Siyuan Wang and Zhongkun Liu and Wanjun Zhong and Ming Zhou and Zhongyu Wei and Zhumin Chen and Nan Duan},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
year={2021},
volume={30},
pages={2201-2216}
}
```
|
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sunlab/patch_db
|
sunlab
| 2023-08-15T20:36:32Z | 56 | 6 |
[
"task_categories:feature-extraction",
"task_categories:text-classification",
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"code",
"commit",
"patch",
"region:us"
] |
[
"feature-extraction",
"text-classification",
"summarization",
"text-generation"
] | 2023-07-27T23:25:37Z |
---
license: apache-2.0
task_categories:
- feature-extraction
- text-classification
- summarization
- text-generation
tags:
- code
- commit
- patch
language:
- en
pretty_name: PatchDB
size_categories:
- 10K<n<100K
---
# PatchDB: A Large-Scale Security Patch Dataset
## Description
To foster large-scale research on vulnerability mitigation and to enable a comparison of different detection approaches, we make our dataset ***PatchDB*** from our DSN'21 paper publicly available.
PatchDB is a large-scale security patch dataset that contains around 12,073 security patches and 23,742 non-security patches from the real world.
You can find more details on the dataset in the paper *"[PatchDB: A Large-Scale Security Patch Dataset](https://csis.gmu.edu/ksun/publications/dsn21_PatchDB.pdf)"*. You can also visit our [PatchDB official website](https://sunlab-gmu.github.io/PatchDB) for more information.
<font color="red">Please use your work emails to request for the dataset.</font> Typically, it takes no longer than 24 hours to get approval.
## Data Structure
PatchDB is stored in `json` format, where each sample contains 9 keys and has the following format.
```json
{
"category": the type of patch, value:"security" or "non-security",
"source": the source of patch, value: "cve" or "wild",
"CVE_ID": the CVE ID if it exists, value: "CVE-XXXX-XXXXX" or "NA",
"CWE_ID": the CWE ID if it exists, value: "cwe_id" or "NA"
"commit_id": the hash value of the commit, type: str,
"owner": the owner id of the repository, type: str,
"repo": the repository id, type: str,
"commit_message": the commit message part of the patch, type: str,
"diff_code": the diff code part of the patch, type: str
}
```
## Disclaimer & Download Agreement<span id="jump"></span>
To download the PatchDB dataset, you must agree with the items of the succeeding Disclaimer & Download Agreement. You should carefully read the following terms before submitting the PatchDB request form.
- PatchDB is constructed and cross-checked by 3 experts that work in security patch research.
Due to the potential misclassification led by subjective factors, the Sun Security Laboratory (SunLab) cannot guarantee a 100% accuracy for samples in the dataset.
- The copyright of the PatchDB dataset is owned by SunLab.
- The purpose of using PatchDB should be non-commercial research and/or personal use. The dataset should not be used for commercial use and any profitable purpose.
- The PatchDB dataset should not be re-selled or re-distributed. Anyone who has obtained PatchDB should not share the dataset with others without the permission from SunLab.
## Team
The PatchDB dataset is built by [Sun Security Laboratory](https://sunlab-gmu.github.io/) (SunLab) at [George Mason University](https://www2.gmu.edu/), Fairfax, VA.
## Citations
```bibtex
@inproceedings{wang2021PatchDB,
author={Wang, Xinda and Wang, Shu and Feng, Pengbin and Sun, Kun and Jajodia, Sushil},
booktitle={2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)},
title={PatchDB: A Large-Scale Security Patch Dataset},
year={2021},
volume={},
number={},
pages={149-160},
doi={10.1109/DSN48987.2021.00030}
}
```
|
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daqc/wikipedia-txt-spanish
|
daqc
| 2023-08-06T19:06:28Z | 56 | 0 |
[
"license:cc",
"region:us"
] | null | 2023-08-06T18:58:47Z |
---
license: cc
---
|
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] |
bloyal/deeploc
|
bloyal
| 2023-08-15T13:46:01Z | 56 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-08-08T21:44:50Z |
---
license: cc-by-4.0
---
# DeepLoc-2.0 Training Data
Dataset from https://services.healthtech.dtu.dk/services/DeepLoc-2.0/ used to train the DeepLoc-2.0 model.
## Data preparation
Data downloaded and processed using the following Python script:
```python
import pandas as pd
df = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv').drop(['Unnamed: 0', 'Partition'], axis=1)
df['labels'] = df[['Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']].astype('float32').values.tolist()
df['Membrane'] = df['Membrane'].astype('float32')
df = df[['Kingdom', 'ACC', 'Sequence','Membrane','labels']]
train = df.sample(frac=0.8)
df = df.drop(train.index)
val = df.sample(frac=0.5)
test = df.drop(val.index)
train = train.reset_index(drop=True)
val = val.reset_index(drop=True)
test = test.reset_index(drop=True)
train.to_parquet('deeploc-train.parquet', index=False)
val.to_parquet('deploc-val.parquet', index=False)
test.to_parquet('deeploc-test.parquet', index=False)
```
## Labels
{'Cell membrane': 0,
'Cytoplasm': 1,
'Endoplasmic reticulum': 2,
'Extracellular': 3,
'Golgi apparatus': 4,
'Lysosome/Vacuole': 5,
'Mitochondrion': 6,
'Nucleus': 7,
'Peroxisome': 8,
'Plastid': 9}
## Citation
**DeepLoc-2.0:**
```
Vineet Thumuluri and others, DeepLoc 2.0: multi-label subcellular localization prediction using protein language models, Nucleic Acids Research, Volume 50, Issue W1, 5 July 2022, Pages W228–W234, https://doi.org/10.1093/nar/gkac278
```
The DeepLoc data is a derivative of the UniProt dataset:
**UniProt**
```
The UniProt Consortium
UniProt: the Universal Protein Knowledgebase in 2023
Nucleic Acids Res. 51:D523–D531 (2023)
```
|
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DynamicSuperb/NoiseSNRLevelPrediction_VCTK_MUSAN-Gaussian
|
DynamicSuperb
| 2023-11-24T09:54:21Z | 56 | 0 |
[
"region:us"
] | null | 2023-08-11T09:13:37Z |
---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: instruction
dtype: string
- name: label
dtype: string
splits:
- name: test
num_bytes: 3458150877.875
num_examples: 26865
download_size: 3434724026
dataset_size: 3458150877.875
---
# Dataset Card for "NoiseSNRLevelPredictiongaussian_VCTKMusan"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
lv2/Indonesia_LLama
|
lv2
| 2023-08-26T23:08:53Z | 56 | 3 |
[
"region:us"
] | null | 2023-08-26T12:38:40Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 42278540
num_examples: 49969
download_size: 22157927
dataset_size: 42278540
---
# Dataset Card for "Indonesia_LLama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ISCA-IUB/AntisemitismOnTwitter
|
ISCA-IUB
| 2023-09-22T08:39:09Z | 56 | 1 |
[
"language:en",
"arxiv:2304.14599",
"region:us"
] | null | 2023-09-22T08:18:44Z |
---
language:
- en
---
# Dataset Card for Dataset on Antisemitism on Twitter/X
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The ISCA project has compiled this dataset using an annotation portal, which was used to label tweets as either antisemitic or non-antisemitic, among other labels. Please note that the annotation was done with live data, including images and the context, such as threads. The original data was sourced from annotationportal.com.
### Languages
English
## Dataset Structure
‘TweetID’: Represents the tweet ID.
‘Username’: Represents the username who published the tweet.
‘Text’: Represents the full text of the tweet (not pre-processed).
‘CreateDate’: Represents the date the tweet was created.
‘Biased’: Represents the labeled by our annotations if the tweet is antisemitic or non-antisemitic.
‘Keyword’: Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username.
## Dataset Creation
This dataset contains 6,941 tweets that cover a wide range of topics common in conversations about Jews, Israel, and antisemitism between January 2019 and December 2021. The dataset is drawn from representative samples during this period with relevant keywords. 1,250 tweets (18%) meet the IHRA definition of antisemitic messages.
The dataset has been compiled within the ISCA project using an annotation portal to label tweets as either antisemitic or non-antisemitic. The original data was sourced from annotationportal.com.
### Annotations
#### Annotation process
We annotated the tweets, considering the text, images, videos, and links, in their “natural” context, including threads. We used a detailed annotation guideline, based on the IHRA Definition, which has been endorsed and recommended by more than 30 governments and international organizations5 and is frequently used to monitor and record antisemitic incidents. We divided the definition into 12 paragraphs. Each of the paragraphs addresses different forms and tropes of antisemitism. We created an online annotation tool (https://annotationportal.com) to make labeling easier, more consistent, and less prone to errors, including in the process of recording the annotations. The portal displays the tweet and a clickable annotation form, see Figure 1. It automatically saves each annotation, including the time spent labeling each tweet.
The Annotation Portal retrieves live tweets by referencing their ID number. Our annotators first look at the tweet, and if they are unsure of the meaning, they are prompted to look at the entire thread, replies, likes, links, and comments. A click on the visualized tweet opens a new tab in the browser, displaying the message on the Twitter page in its “natural” environment.
The portal is designed to help annotators consistently label messages as antisemitic or not according to the IHRA definition. After verifying that the message is still live and in English, they select from a drop-down menu where they classify the message as "confident antisemitic," "probably antisemitic," "probably not antisemitic," "confident not antisemitic," or "don’t know." The annotation guideline, including the definition, is linked in a PDF document.
#### Who are the annotators?
All annotators are familiar with the definition and have been trained on test samples. They have also taken at least one academic course on antisemitism or have done research on antisemitism. We consider them to be expert annotators. Eight such expert annotators of different religions and genders labeled the 18 samples, two for each sample in alternating configurations.
## Considerations for Using the Data
### Social Impact of Dataset
One of the major challenges in automatic hate speech detection is the lack of datasets that cover a wide range of biased and unbiased messages and that are consistently labeled. We propose a labeling procedure that addresses some of the common weaknesses of labeled datasets.
We focus on antisemitic speech on Twitter and create a labeled dataset of 6,941 tweets that cover a wide range of topics common in conversations about Jews, Israel, and antisemitism between January 2019 and December 2021 by drawing from representative samples with relevant keywords.
Our annotation process aims to strictly apply a commonly used definition of antisemitism by forcing annotators to specify which part of the definition applies, and by giving them the option to personally disagree with the definition on a case-by-case basis. Labeling tweets that call out antisemitism, report antisemitism, or are otherwise related to antisemitism (such as the Holocaust) but are not actually antisemitic can help reduce false positives in automated detection.
## Additional Information
### Dataset Curators
Gunther Jikeli, Sameer Karali, Daniel Miehling, and Katharina Soemer
### Citation Information
Jikeli,Gunther, Sameer Karali, Daniel Miehling, and Katharina Soemer (2023): Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets. https://arxiv.org/abs/2304.14599
|
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] |
nadsoft/Jordan-Audio
|
nadsoft
| 2023-10-11T08:20:58Z | 56 | 0 |
[
"region:us"
] | null | 2023-10-11T08:17:45Z |
---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 669684377.68
num_examples: 5044
download_size: 660360475
dataset_size: 669684377.68
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "jo_aud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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xrizs/instseg
|
xrizs
| 2023-11-06T11:43:54Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-06T11:43:48Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: annotation
dtype: image
splits:
- name: train
num_bytes: 34491927.0
num_examples: 58
- name: val
num_bytes: 12337041.0
num_examples: 20
- name: test
num_bytes: 5255226.0
num_examples: 9
download_size: 52063862
dataset_size: 52084194.0
---
# Dataset Card for "instseg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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kristaller486/ALMA-prompt-completion
|
kristaller486
| 2023-11-15T08:43:52Z | 56 | 0 |
[
"task_categories:translation",
"size_categories:100K<n<1M",
"language:en",
"language:ru",
"language:cs",
"language:de",
"language:is",
"language:zh",
"license:mit",
"arxiv:2309.11674",
"region:us"
] |
[
"translation"
] | 2023-11-14T10:58:43Z |
---
license: mit
task_categories:
- translation
language:
- en
- ru
- cs
- de
- is
- zh
pretty_name: ALMA Dataset
size_categories:
- 100K<n<1M
---
# Dataset Card for ALMA-prompt-completion
[ALMA Dataset](https://github.com/fe1ixxu/ALMA/tree/master/human_written_data) if format of [prompt-completion](https://github.com/higgsfield-ai/higgsfield/tree/main/tutorials)
- **Created by:** fe1ixxu
- **Shared by:** me
- **Language(s) (NLP):** English, Czech, German, Russian, Islandic, Chinese
- **License:** MIT
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [https://github.com/fe1ixxu/ALMA]
- **Paper [optional]:** [https://arxiv.org/abs/2309.11674]
## Uses
LLM translators
|
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] |
ekolasky/NQLongAnswersForCustomLEDForQA
|
ekolasky
| 2023-11-15T04:21:56Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-14T20:39:16Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: start_positions
sequence: int64
- name: end_positions
sequence: int64
- name: global_attention_mask
sequence: int64
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 1000140417
num_examples: 12323
- name: validation
num_bytes: 47307511
num_examples: 588
download_size: 119671635
dataset_size: 1047447928
---
# Dataset Card for "NQLongAnswersForCustomLEDForQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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higgsfield/question_to_sql
|
higgsfield
| 2023-11-14T21:54:15Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-14T21:54:11Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 19324361
num_examples: 78577
download_size: 6963083
dataset_size: 19324361
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "question_to_sql"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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vilm/viet-pretrained-002
|
vilm
| 2023-11-15T04:24:21Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-15T04:22:27Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4502549932
num_examples: 258823
download_size: 2324002719
dataset_size: 4502549932
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "viet-pretrained-002"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
SebastianSchramm/ultrafeedback_binarized_prefs_filtered
|
SebastianSchramm
| 2023-11-15T12:14:31Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-15T12:13:37Z |
---
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
splits:
- name: train_prefs
num_bytes: 360858348.6922183
num_examples: 56286
- name: test_prefs
num_bytes: 11663780.3125
num_examples: 1825
download_size: 180008939
dataset_size: 372522129.0047183
---
# Dataset Card for "ultrafeedback_binarized_prefs_filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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rishiraj/hindichat
|
rishiraj
| 2023-11-16T09:14:59Z | 56 | 4 |
[
"task_categories:conversational",
"task_categories:text-generation",
"language:hi",
"license:cc-by-nc-4.0",
"arxiv:2203.02155",
"region:us"
] |
[
"conversational",
"text-generation"
] | 2023-11-16T08:14:13Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 64144365
num_examples: 9500
- name: test
num_bytes: 3455962
num_examples: 500
download_size: 27275492
dataset_size: 67600327
task_categories:
- conversational
- text-generation
language:
- hi
pretty_name: Hindi Chat
license: cc-by-nc-4.0
---
# Dataset Card for Hindi Chat
We know that current English-first LLMs don’t work well for many other languages, both in terms of performance, latency, and speed. Building instruction datasets for non-English languages is an important challenge that needs to be solved.
Dedicated towards addressing this problem, I release 2 new datasets [rishiraj/bengalichat](https://huggingface.co/datasets/rishiraj/bengalichat/) & [rishiraj/hindichat](https://huggingface.co/datasets/rishiraj/hindichat/) of 10,000 instructions and demonstrations each. This data can be used for supervised fine-tuning (SFT) to make language multilingual models follow instructions better.
### Dataset Summary
[rishiraj/hindichat](https://huggingface.co/datasets/rishiraj/hindichat/) was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is translated from [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots/) which comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
### Languages
The data in [rishiraj/hindichat](https://huggingface.co/datasets/rishiraj/hindichat/) are in Hindi (BCP-47 hi).
### Data Fields
The data fields are as follows:
* `prompt`: Describes the task the model should perform.
* `prompt_id`: A unique ID for the prompt.
* `messages`: An array of messages, where each message indicates the role (system, user, assistant) and the content.
* `category`: Which category the example belongs to (e.g. `Chat` or `Coding`).
* `text`: Content of `messages` in a format that is compatible with dataset_text_field of SFTTrainer.
### Data Splits
| | train_sft | test_sft |
|---------------|------:| ---: |
| hindichat | 9500 | 500 |
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{hindichat,
author = {Rishiraj Acharya},
title = {Hindi Chat},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/rishiraj/hindichat}}
}
```
|
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marvy/book-covers
|
marvy
| 2023-12-04T16:10:56Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-19T16:22:43Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 289489613.68
num_examples: 32581
download_size: 284878943
dataset_size: 289489613.68
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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tomaarsen/ner-orgs
|
tomaarsen
| 2023-11-22T16:53:37Z | 56 | 2 |
[
"region:us"
] | null | 2023-11-22T09:36:41Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-ORG
'2': I-ORG
splits:
- name: train
num_bytes: 40381520.59961503
num_examples: 109424
- name: validation
num_bytes: 5782294.96333573
num_examples: 15908
- name: test
num_bytes: 10727120.198367199
num_examples: 28124
download_size: 14938552
dataset_size: 56890935.76131796
---
# Dataset Card for "ner-orgs"
This dataset is a concatenation of subsets of [Few-NERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd), [CoNLL 2003](https://huggingface.co/datasets/conll2003) and [OntoNotes v5](https://huggingface.co/datasets/tner/ontonotes5), but only the "B-ORG" and "I-ORG" labels.
Exactly half of the samples per split contain organisations, while the other half do not contain any.
It was generated using the following script:
```py
import random
from datasets import load_dataset, concatenate_datasets, Features, Sequence, ClassLabel, Value, DatasetDict
FEATURES = Features(
{
"tokens": Sequence(feature=Value(dtype="string")),
"ner_tags": Sequence(feature=ClassLabel(names=["O", "B-ORG", "I-ORG"])),
}
)
def load_fewnerd():
def mapper(sample):
sample["ner_tags"] = [int(tag == 5) for tag in sample["ner_tags"]]
sample["ner_tags"] = [
2 if tag == 1 and idx > 0 and sample["ner_tags"][idx - 1] == 1 else tag
for idx, tag in enumerate(sample["ner_tags"])
]
return sample
dataset = load_dataset("DFKI-SLT/few-nerd", "supervised")
dataset = dataset.map(mapper, remove_columns=["id", "fine_ner_tags"])
dataset = dataset.cast(FEATURES)
return dataset
def load_conll():
label_mapping = {3: 1, 4: 2}
def mapper(sample):
sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
return sample
dataset = load_dataset("conll2003")
dataset = dataset.map(mapper, remove_columns=["id", "pos_tags", "chunk_tags"])
dataset = dataset.cast(FEATURES)
return dataset
def load_ontonotes():
label_mapping = {11: 1, 12: 2}
def mapper(sample):
sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
return sample
dataset = load_dataset("tner/ontonotes5")
dataset = dataset.rename_column("tags", "ner_tags")
dataset = dataset.map(mapper)
dataset = dataset.cast(FEATURES)
return dataset
def has_org(sample):
return bool(sum(sample["ner_tags"]))
def has_no_org(sample):
return not has_org(sample)
def preprocess_raw_dataset(raw_dataset):
# Set the number of sentences without an org equal to the number of sentences with an org
dataset_org = raw_dataset.filter(has_org)
dataset_no_org = raw_dataset.filter(has_no_org)
dataset_no_org = dataset_no_org.select(random.sample(range(len(dataset_no_org)), k=len(dataset_org)))
dataset = concatenate_datasets([dataset_org, dataset_no_org])
return dataset
def main() -> None:
fewnerd_dataset = load_fewnerd()
conll_dataset = load_conll()
ontonotes_dataset = load_ontonotes()
raw_train_dataset = concatenate_datasets([fewnerd_dataset["train"], conll_dataset["train"], ontonotes_dataset["train"]])
raw_eval_dataset = concatenate_datasets([fewnerd_dataset["validation"], conll_dataset["validation"], ontonotes_dataset["validation"]])
raw_test_dataset = concatenate_datasets([fewnerd_dataset["test"], conll_dataset["test"], ontonotes_dataset["test"]])
train_dataset = preprocess_raw_dataset(raw_train_dataset)
eval_dataset = preprocess_raw_dataset(raw_eval_dataset)
test_dataset = preprocess_raw_dataset(raw_test_dataset)
dataset_dict = DatasetDict(
{
"train": train_dataset,
"validation": eval_dataset,
"test": test_dataset,
}
)
dataset_dict.push_to_hub("ner-orgs", private=True)
if __name__ == "__main__":
main()
```
|
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result-kand2-sdxl-wuerst-karlo/ead57b12
|
result-kand2-sdxl-wuerst-karlo
| 2023-11-22T13:11:27Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-22T13:11:24Z |
---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1347
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ead57b12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
danielz01/landmarks
|
danielz01
| 2023-11-23T23:57:47Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-23T23:52:02Z |
---
dataset_info:
config_name: NAIP
features:
- name: image
dtype: image
- name: osm_relation_id
dtype: int64
- name: wikidata_entity_id
dtype: string
- name: lat
dtype: float64
- name: lon
dtype: float64
- name: alt
dtype: 'null'
- name: name
dtype: string
- name: instanceOfIDs
sequence: string
- name: instanceOfLabels
sequence: string
- name: distractorIDs
sequence: string
- name: distractorLabels
sequence: string
splits:
- name: train
num_bytes: 3907742505.0
num_examples: 602
download_size: 3907443672
dataset_size: 3907742505.0
configs:
- config_name: NAIP
data_files:
- split: train
path: NAIP/train-*
---
# Dataset Card for "landmarks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ekolasky/ResultsIdSeperatedSet
|
ekolasky
| 2023-11-29T16:21:03Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-29T16:20:59Z |
---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
- name: global_attention_mask
sequence: int64
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 4149897
num_examples: 747
- name: validation
num_bytes: 253036
num_examples: 49
download_size: 464308
dataset_size: 4402933
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
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] |
Nexusflow/NVDLibraryBenchmark
|
Nexusflow
| 2023-11-29T22:27:13Z | 56 | 0 |
[
"region:us"
] | null | 2023-11-29T22:27:12Z |
---
dataset_info:
features:
- name: Input
dtype: string
- name: Output
dtype: string
- name: verified
dtype: bool
splits:
- name: train
num_bytes: 17726
num_examples: 78
download_size: 9980
dataset_size: 17726
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
peixian/equity_evaluation_corpus
|
peixian
| 2022-10-20T23:35:15Z | 55 | 3 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"gender-classification",
"region:us"
] |
[
"text-classification"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
tags:
- gender-classification
---
# Dataset Card for equity-evaluation-corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems and resources. Further, there is a lack of benchmark datasets for examining inappropriate biases in system predictions. Here, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We used the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 Affect in Tweets. We found that several of the systems showed statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available, and encourage its use to evaluate biases in sentiment and other NLP tasks.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
- `sentence`: a `string` feature.
- `template`: a `string` feature.
- `person`: a `string` feature.
- `race`: a `string` feature.
- `emotion`: a `string` feature.
- `emotion word`: a `string` feature.
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
|
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allenai/ms2_sparse_max
|
allenai
| 2022-11-24T16:27:49Z | 55 | 0 |
[
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"summarization",
"text2text-generation"
] | 2022-08-26T21:40:42Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==25`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4333 | 0.2163 | 0.1746 | 0.2636 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.378 | 0.1827 | 0.1559 | 0.2188 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.3928 | 0.1898 | 0.1672 | 0.2208 |
|
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bigbio/mirna
|
bigbio
| 2022-12-22T15:45:38Z | 55 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:cc-by-nc-3.0",
"region:us"
] | null | 2022-11-13T22:10:00Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-nc-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_NC_3p0
pretty_name: miRNA
homepage: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for miRNA
## Dataset Description
- **Homepage:** https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
The corpus consists of 301 Medline citations. The documents were screened for
mentions of miRNA in the abstract text. Gene, disease and miRNA entities were manually
annotated. The corpus comprises of two separate files, a train and a test set, coming
from 201 and 100 documents respectively.
## Citation Information
```
@Article{Bagewadi2014,
author={Bagewadi, Shweta
and Bobi{'{c}}, Tamara
and Hofmann-Apitius, Martin
and Fluck, Juliane
and Klinger, Roman},
title={Detecting miRNA Mentions and Relations in Biomedical Literature},
journal={F1000Research},
year={2014},
month={Aug},
day={28},
publisher={F1000Research},
volume={3},
pages={205-205},
keywords={MicroRNAs; corpus; prediction algorithms},
abstract={
INTRODUCTION: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional
gene expression regulators, participating in a wide spectrum of regulatory events such as
apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal
physiology, their dysregulation is implicated in a vast array of diseases. Dissection of
miRNA-related associations are valuable for contemplating their mechanism in diseases,
leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy.
MOTIVATION: Apart from databases and prediction tools, miRNA-related information is largely
available as unstructured text. Manual retrieval of these associations can be labor-intensive
due to steadily growing number of publications. Additionally, most of the published miRNA
entity recognition methods are keyword based, further subjected to manual inspection for
retrieval of relations. Despite the fact that several databases host miRNA-associations
derived from text, lower sensitivity and lack of published details for miRNA entity
recognition and associated relations identification has motivated the need for developing
comprehensive methods that are freely available for the scientific community. Additionally,
the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the
available systems. We propose methods to automatically extract mentions of miRNAs, species,
genes/proteins, disease, and relations from scientific literature. Our generated corpora,
along with dictionaries, and miRNA regular expression are freely available for academic
purposes. To our knowledge, these resources are the most comprehensive developed so far.
RESULTS: The identification of specific miRNA mentions reaches a recall of 0.94 and
precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an
F1 score of up to 0.76. A comparison of the information extracted by our approach to
the databases miR2Disease and miRSel for the extraction of Alzheimer's disease
related relations shows the capability of our proposed methods in identifying correct
relations with improved sensitivity. The published resources and described methods can
help the researchers for maximal retrieval of miRNA-relations and generation of
miRNA-regulatory networks. AVAILABILITY: The training and test corpora, annotation
guidelines, developed dictionaries, and supplementary files are available at
http://www.scai.fraunhofer.de/mirna-corpora.html.
},
note={26535109[pmid]},
note={PMC4602280[pmcid]},
issn={2046-1402},
url={https://pubmed.ncbi.nlm.nih.gov/26535109},
language={eng}
}
```
|
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] |
juancavallotti/multilingual-gec
|
juancavallotti
| 2023-01-06T18:59:59Z | 55 | 3 |
[
"task_categories:translation",
"size_categories:100K<n<1M",
"language:en",
"language:es",
"language:fr",
"language:de",
"license:apache-2.0",
"grammar",
"gec",
"multi language",
"language detection",
"region:us"
] |
[
"translation"
] | 2023-01-06T16:07:20Z |
---
author: Juan Alberto López Cavallotti
date: Jan 6, 2023
license: apache-2.0
task_categories:
- translation
language:
- en
- es
- fr
- de
tags:
- grammar
- gec
- multi language
- language detection
pretty_name: Multi Lingual Grammar Error Correction Dataset
size_categories:
- 100K<n<1M
---
# Dataset Card for Multilingual Grammar Error Correction
## Dataset Description
- **Homepage:** https://juancavallotti.com
- **Paper:** https://blog.juancavallotti.com/2023/01/06/training-a-multi-language-grammar-error-correction-system/
- **Point of Contact:** Juan Alberto López Cavallotti
### Dataset Summary
This dataset can be used to train a transformer model (we used T5) to correct grammar errors in simple sentences written in English, Spanish, French, or German.
This dataset was developed as a component for the [Squidigies](https://squidgies.app/) platform.
### Supported Tasks and Leaderboards
* **Grammar Error Correction:** By appending the prefix *fix grammar:* to the prrompt.
* **Language Detection:** By appending the prefix: *language:* to the prompt.
### Languages
* English
* Spanish
* French
* German
## Dataset Structure
### Data Instances
The dataset contains the following instances for each language:
* German 32282 sentences.
* English 51393 sentences.
* Spanish 67672 sentences.
* French 67157 sentences.
### Data Fields
* `lang`: The language of the sentence
* `sentence`: The original sentence.
* `modified`: The corrupted sentence.
* `transformation`: The primary transformation used by the synthetic data generator.
* `sec_transformation`: The secondary transformation (if any) used by the synthetic data generator.
### Data Splits
* `train`: There isn't a specific split defined. I recommend using 1k sentences sampled randomly from each language, combined with the SacreBleu metric.
## Dataset Creation
### Curation Rationale
This dataset was generated synthetically through code with the help of information of common grammar errors harvested throughout the internet.
### Source Data
#### Initial Data Collection and Normalization
The source grammatical sentences come from various open-source datasets, such as Tatoeba.
#### Who are the source language producers?
* Juan Alberto López Cavallotti
### Annotations
#### Annotation process
The annotation is automatic and produced by the generation script.
#### Who are the annotators?
* Data generation script by Juan Alberto López Cavallotti
### Other Known Limitations
The dataset doesn't cover all the possible grammar errors but serves as a starting point that generates fair results.
## Additional Information
### Dataset Curators
* Juan Alberto López Cavallotti
### Licensing Information
This dataset is distributed under the [Apache 2 License](https://www.apache.org/licenses/LICENSE-2.0)
### Citation Information
Please mention this original dataset and the author **Juan Alberto López Cavallotti**
### Contributions
* Juan Alberto López Cavallotti
|
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DFKI-SLT/cross_ner
|
DFKI-SLT
| 2023-01-19T09:17:38Z | 55 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|conll2003",
"language:en",
"cross domain",
"ai",
"news",
"music",
"literature",
"politics",
"science",
"arxiv:2012.04373",
"region:us"
] |
[
"token-classification"
] | 2023-01-19T09:17:08Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: CrossNER is a cross-domain dataset for named entity recognition
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
tags:
- cross domain
- ai
- news
- music
- literature
- politics
- science
task_categories:
- token-classification
task_ids:
- named-entity-recognition
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---
# Dataset Card for CrossRE
## 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
- **Repository:** [CrossNER](https://github.com/zliucr/CrossNER)
- **Paper:** [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
### Dataset Summary
CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains
(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for
different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five
domains.
For details, see the paper:
[CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language data in CrossNER is in English (BCP-47 en)
## Dataset Structure
### Data Instances
#### conll2003
- **Size of downloaded dataset files:** 2.69 MB
- **Size of the generated dataset:** 5.26 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
"ner_tags": [49, 0, 41, 0, 0, 0, 41, 0, 0]
}
```
#### politics
- **Size of downloaded dataset files:** 0.72 MB
- **Size of the generated dataset:** 1.04 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."],
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 55, 56, 0, 0, 0, 0, 0, 55, 56, 56, 56, 56, 56, 0, 55, 56, 56, 56, 56, 0]
}
```
#### science
- **Size of downloaded dataset files:** 0.49 MB
- **Size of the generated dataset:** 0.73 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."],
"ner_tags": [0, 0, 0, 0, 15, 16, 0, 15, 16, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
#### music
- **Size of downloaded dataset files:** 0.41 MB
- **Size of the generated dataset:** 0.65 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."],
"ner_tags": [0, 0, 0, 0, 35, 36, 36, 0, 0, 0, 0, 0, 0, 29, 30, 30, 30, 30, 0]
}
```
#### literature
- **Size of downloaded dataset files:** 0.33 MB
- **Size of the generated dataset:** 0.58 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."],
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 51, 52, 52, 0, 0, 21, 22, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 21, 0, 21, 0, 0, 41, 0, 0, 0, 0, 0, 0, 51, 52, 0, 0, 41, 0, 0, 0, 0, 0, 51, 0, 0]
}
```
#### ai
- **Size of downloaded dataset files:** 0.29 MB
- **Size of the generated dataset:** 0.48 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."],
"ner_tags": [0, 0, 0, 59, 60, 60, 0, 0, 0, 0, 31, 32, 0, 71, 72, 0, 71, 72, 0, 0, 0, 71, 72, 72, 0, 0, 31, 32, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "B-academicjournal": 1, "I-academicjournal": 2, "B-album": 3, "I-album": 4, "B-algorithm": 5, "I-algorithm": 6, "B-astronomicalobject": 7, "I-astronomicalobject": 8, "B-award": 9, "I-award": 10, "B-band": 11, "I-band": 12, "B-book": 13, "I-book": 14, "B-chemicalcompound": 15, "I-chemicalcompound": 16, "B-chemicalelement": 17, "I-chemicalelement": 18, "B-conference": 19, "I-conference": 20, "B-country": 21, "I-country": 22, "B-discipline": 23, "I-discipline": 24, "B-election": 25, "I-election": 26, "B-enzyme": 27, "I-enzyme": 28, "B-event": 29, "I-event": 30, "B-field": 31, "I-field": 32, "B-literarygenre": 33, "I-literarygenre": 34, "B-location": 35, "I-location": 36, "B-magazine": 37, "I-magazine": 38, "B-metrics": 39, "I-metrics": 40, "B-misc": 41, "I-misc": 42, "B-musicalartist": 43, "I-musicalartist": 44, "B-musicalinstrument": 45, "I-musicalinstrument": 46, "B-musicgenre": 47, "I-musicgenre": 48, "B-organisation": 49, "I-organisation": 50, "B-person": 51, "I-person": 52, "B-poem": 53, "I-poem": 54, "B-politicalparty": 55, "I-politicalparty": 56, "B-politician": 57, "I-politician": 58, "B-product": 59, "I-product": 60, "B-programlang": 61, "I-programlang": 62, "B-protein": 63, "I-protein": 64, "B-researcher": 65, "I-researcher": 66, "B-scientist": 67, "I-scientist": 68, "B-song": 69, "I-song": 70, "B-task": 71, "I-task": 72, "B-theory": 73, "I-theory": 74, "B-university": 75, "I-university": 76, "B-writer": 77, "I-writer": 78}
```
### Data Splits
| | Train | Dev | Test |
|--------------|--------|-------|-------|
| conll2003 | 14,987 | 3,466 | 3,684 |
| politics | 200 | 541 | 651 |
| science | 200 | 450 | 543 |
| music | 100 | 380 | 456 |
| literature | 100 | 400 | 416 |
| ai | 100 | 350 | 431 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{liu2020crossner,
title={CrossNER: Evaluating Cross-Domain Named Entity Recognition},
author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
year={2020},
eprint={2012.04373},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.
|
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] |
0-hero/OIG-small-chip2
|
0-hero
| 2023-03-16T20:10:19Z | 55 | 6 |
[
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"region:us"
] |
[
"conversational",
"text2text-generation"
] | 2023-03-16T17:59:26Z |
---
dataset_info:
features:
- name: user
dtype: string
- name: chip2
dtype: string
splits:
- name: train
num_bytes: 82154419
num_examples: 210289
download_size: 51736759
dataset_size: 82154419
task_categories:
- conversational
- text2text-generation
language:
- en
---
# Dataset Card for "OIG-small-chip2"
OIG-small-chip2 dataset from https://laion.ai/blog/oig-dataset/ <br>
Original Dataset - https://github.com/LAION-AI/Open-Instruction-Generalist
|
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ErfanMoosaviMonazzah/fake-news-detection-dataset-English
|
ErfanMoosaviMonazzah
| 2023-03-23T13:05:33Z | 55 | 0 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:openrail",
"fake news",
"text classification",
"region:us"
] |
[
"text-classification"
] | 2023-03-23T12:55:15Z |
---
license: openrail
task_categories:
- text-classification
language:
- en
tags:
- fake news
- text classification
pretty_name: Fake News Detection Dataset (English)
size_categories:
- 10K<n<100K
---
This is a cleaned and splitted version of this dataset (https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english) <br>
Labels:
- Fake News: 0
- Real News: 1
<br>
You can find the cleansing script at: https://github.com/ErfanMoosaviMonazzah/Fake-News-Detection
|
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] |
Fung804/makoto-shinkai-picture
|
Fung804
| 2023-04-06T01:31:52Z | 55 | 2 |
[
"region:us"
] | null | 2023-04-05T05:56:38Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1012000102.307
num_examples: 1347
download_size: 1044953186
dataset_size: 1012000102.307
---
# Dataset Card for "makoto-shinkai-picture"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
supremezxc/nlpcc_2017
|
supremezxc
| 2023-04-20T07:07:50Z | 55 | 1 |
[
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:zh",
"license:openrail",
"region:us"
] |
[
"summarization"
] | 2023-04-20T06:59:46Z |
---
license: openrail
task_categories:
- summarization
language:
- zh
pretty_name: NLPCC2017中文新闻数据集
size_categories:
- 10K<n<100K
---
|
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] |
ai-forever/spellcheck_benchmark
|
ai-forever
| 2023-10-04T16:13:44Z | 55 | 3 |
[
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<20k",
"language:ru",
"license:mit",
"spellcheck",
"russian",
"arxiv:2308.09435",
"region:us"
] |
[
"text-generation"
] | 2023-04-28T09:49:40Z |
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ru
license: mit
multilinguality:
- monolingual
size_categories:
- 10K<n<20k
task_categories:
- text-generation
pretty_name: Russian Spellcheck Benchmark
language_bcp47:
- ru-RU
tags:
- spellcheck
- russian
---
# Dataset Card for Russian Spellcheck Benchmark
## 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
- **Repository:** [SAGE](https://github.com/ai-forever/sage)
- **Paper:** [arXiv:2308.09435](https://arxiv.org/abs/2308.09435)
- **Point of Contact:** [email protected]
### Dataset Summary
Spellcheck Benchmark includes four datasets, each of which consists of pairs of sentences in Russian language.
Each pair embodies sentence, which may contain spelling errors, and its corresponding correction.
Datasets were gathered from various sources and domains including social networks, internet blogs, github commits, medical anamnesis, literature, news, reviews and more.
All datasets were passed through two-stage manual labeling pipeline.
The correction of a sentence is defined by an agreement of at least two human annotators.
Manual labeling scheme accounts for jargonisms, collocations and common language, hence in some cases it encourages
annotators not to amend a word in favor of preserving style of a text.
### Supported Tasks and Leaderboards
- **Task:** automatic spelling correction.
- **Metrics:** https://www.dialog-21.ru/media/3427/sorokinaaetal.pdf.
### Languages
Russian.
## Dataset Structure
### Data Instances
#### RUSpellRU
- **Size of downloaded dataset files:** 3.64 Mb
- **Size of the generated dataset:** 1.29 Mb
- **Total amount of disk used:** 4.93 Mb
An example of "train" / "test" looks as follows
```
{
"source": "очень классная тетка ктобы что не говорил.",
"correction": "очень классная тетка кто бы что ни говорил",
}
```
#### MultidomainGold
- **Size of downloaded dataset files:** 15.05 Mb
- **Size of the generated dataset:** 5.43 Mb
- **Total amount of disk used:** 20.48 Mb
An example of "test" looks as follows
```
{
"source": "Ну что могу сказать... Я заказала 2 вязанных платья: за 1000 руб (у др продавца) и это ща 1200. Это платье- голимая синтетика (в том платье в составе была шерсть). Это платье как очень плохая резинка. На свои параметры (83-60-85) я заказала С . Пока одевала/снимала - оно в горловине растянулось. Помимо этого в этом платье я выгляжу ну очень тоской. У меня вес 43 кг на 165 см роста. Кстати, продавец отправлял платье очень долго. Я пыталась отказаться от заказа, но он постоянно отклонял мой запрос. В общем не советую.",
"correction": "Ну что могу сказать... Я заказала 2 вязаных платья: за 1000 руб (у др продавца) и это ща 1200. Это платье- голимая синтетика (в том платье в составе была шерсть). Это платье как очень плохая резинка. На свои параметры (83-60-85) я заказала С . Пока надевала/снимала - оно в горловине растянулось. Помимо этого в этом платье я выгляжу ну очень доской. У меня вес 43 кг на 165 см роста. Кстати, продавец отправлял платье очень долго. Я пыталась отказаться от заказа, но он постоянно отклонял мой запрос. В общем не советую.",
"domain": "reviews",
}
```
#### MedSpellcheck
- **Size of downloaded dataset files:** 1.49 Mb
- **Size of the generated dataset:** 0.54 Mb
- **Total amount of disk used:** 2.03 Mb
An example of "test" looks as follows
```
{
"source": "Кровотечения, поерации в анамнезе отрицает",
"correction": "Кровотечения, операции в анамнезе отрицает",
}
```
#### GitHubTypoCorpusRu
- **Size of downloaded dataset files:** 1.23 Mb
- **Size of the generated dataset:** 0.48 Mb
- **Total amount of disk used:** 1.71 Mb
An example of "test" looks as follows
```
{
"source": "## Запросы и ответа содержат заголовки",
"correction": "## Запросы и ответы содержат заголовки",
}
```
### Data Fields
#### RUSpellRU
- `source`: a `string` feature
- `correction`: a `string` feature
- `domain`: a `string` feature
#### MultidomainGold
- `source`: a `string` feature
- `correction`: a `string` feature
- `domain`: a `string` feature
#### MedSpellcheck
- `source`: a `string` feature
- `correction`: a `string` feature
- `domain`: a `string` feature
#### GitHubTypoCorpusRu
- `source`: a `string` feature
- `correction`: a `string` feature
- `domain`: a `string` feature
### Data Splits
#### RUSpellRU
| |train|test|
|---|---:|---:|
|RUSpellRU|2000|2008|
#### MultidomainGold
| |train|test|
|---|---:|---:|
|web|386|756|
|news|361|245|
|social_media|430|200|
|reviews|584|586|
|subtitles|1810|1810|
|strategic_documents|-|250|
|literature|-|260|
#### MedSpellcheck
| |test|
|---|---:|
|MedSpellcheck|1054|
#### GitHubTypoCorpusRu
| |test|
|---|---:|
|GitHubTypoCorpusRu|868|
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The datasets are chosen in accordance with the specified criteria.
First, domain variation: half of the datasets are chosen from different domains to ensure diversity, while the remaining half are from a single domain.
Another criterion is spelling orthographic mistakes:
the datasets exclusively comprised mistyping, omitting grammatical or more complex errors of nonnative speakers.
- **RUSpellRU**: texts collected from ([LiveJournal](https://www.livejournal.com/media)), with manually corrected typos and errors;
- **MultidomainGold**: examples from several text sources including the open web, news, social media, reviews, subtitles, policy documents and literary works were collected:
*Aranea web-corpus* is a family of multilanguage gigaword web-corpora collected from Internet resources. The texts in the corpora are evenly distributed across periods, writing styles and topics they cover. We randomly picked the sentences from Araneum Russicum, which is harvested from the Russian part of the web.
*Literature* is a collection of Russian poems and prose of different classical literary works. We randomly picked sentences from the source dataset that were gathered from Ilibrary, LitLib, and Wikisource.
*News*, as the name suggests, covers news articles on various topics such as sports, politics, environment, economy etc. The passages are randomly picked from the summarization dataset Gazeta.ru.
*Social media* is the text domain from social media platforms marked with specific hashtags. These texts are typically short, written in an informal style and may contain slang, emojis and obscene lexis.
*Strategic Documents* is part of the dataset the Ministry of Economic Development of the Russian Federation collected. Texts are written in a bureaucratic manner, rich in embedded entities, and have complex syntactic and discourse structures. The full version of the dataset has been previously used in the RuREBus shared task.
- **MedSpellChecker**: texts with errors from medical anamnesis;
- **GitHubTypoCorpusRu**: spelling errors and typos in commits from [GitHub](https://github.com);
### Annotations
#### Annotation process
We set up two-stage annotation project via a crowd-sourcing platform Toloka:
1. Data gathering stage: we provide the texts with possible mistakes to annotators and ask them to write the sentence correctly;
2. Validation stage: we provide annotators with the pair of sentences (source and its corresponding correction from the previous stage) and ask them to check if the correction is right.
We prepared instructions for annotators for each task. The instructions ask annotators to correct misspellings if it does not alter the original style of the text.
Instructions do not provide rigorous criteria on the matter of distinguishing the nature of an error in terms of its origin - whether it came from an urge to endow a sentence with particular stylistic features or from unintentional spelling violation since it is time-consuming and laborious to describe every possible case of employing slang, dialect, collo- quialisms, etc. instead of proper language. Instructions also do not distinguish errors that come from the geographical or social background of the source. Instead, we rely on annotators’ knowledge and understanding of a language since, in this work, the important factor is to preserve the original style of the text.
To ensure we receive qualified expertise, we set up test iteration on a small subset of the data for both stages. We manually validated the test results and selected annotators, who processed at least six samples (2% of the total test iteration) and did not make a single error. After test iteration, we cut 85% and 86% of labellers for gathering and validation stages.
We especially urge annotators to correct mistakes associated with the substitution of the letters "ё" "й" and "щ" for corresponding "е" "и" and "ш" and not to explain abbreviations and correct punctuation errors. Each annotator is also warned about potentially sensitive topics in data (e.g., politics, societal minorities, and religion).
#### Who are the annotators?
Native Russian speakers who passed the language exam.
## Considerations for Using the Data
### Discussion of Biases
We clearly state our work’s aims and
implications, making it open source and transparent. The data will be available under a public license. As our research involved anonymized textual data, informed consent from human participants was not required. However, we obtained permission to access publicly available datasets and
ensured compliance with any applicable terms of
service or usage policies.
### Other Known Limitations
The data used in our research may be limited to specific
domains, preventing comprehensive coverage of
all possible text variations. Despite these limitations, we tried to address the issue of data diversity
by incorporating single-domain and multi-domain
datasets in the proposed research. This approach
allowed us to shed light on the diversity and variances within the data, providing valuable insights
despite the inherent constraints.
We primarily focus on the Russian language. Further
research is needed to expand the datasets for a wider
range of languages.
## Additional Information
### Future plans
We are planning to expand our benchmark with both new Russian datasets and datasets in other languages including (but not limited to) European and CIS languages.
If you would like to contribute, please contact us.
### Dataset Curators
Nikita Martynov [email protected]
### Licensing Information
All our datasets are published by MIT License.
### Citation Information
```
@inproceedings{martynov2023augmentation,
title={Augmentation methods for spelling corruptions},
author={Martynov, Nikita and Baushenko, Mark and Abramov, Alexander and Fenogenova, Alena},
booktitle={Proceedings of the International Conference “Dialogue},
volume={2023},
year={2023}
}
@misc{martynov2023methodology,
title={A Methodology for Generative Spelling Correction
via Natural Spelling Errors Emulation across Multiple Domains and Languages},
author={Nikita Martynov and Mark Baushenko and Anastasia Kozlova and
Katerina Kolomeytseva and Aleksandr Abramov and Alena Fenogenova},
year={2023},
eprint={2308.09435},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
mingyy/chinese_landscape_paintings
|
mingyy
| 2023-05-10T15:56:26Z | 55 | 1 |
[
"region:us"
] | null | 2023-05-04T19:16:10Z |
---
dataset_info:
features:
- name: target
dtype: image
- name: filename
dtype: string
- name: image_caption
dtype: string
- name: hed
dtype: image
- name: source
dtype: image
splits:
- name: train
num_bytes: 44114965534.5
num_examples: 52564
download_size: 8162381811
dataset_size: 44114965534.5
---
# Dataset Card for "chinese_landscape_paintings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
lucadiliello/wikiqa_grouped
|
lucadiliello
| 2023-05-30T08:14:53Z | 55 | 0 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
[
"text-classification"
] | 2023-05-30T08:12:28Z |
---
task_categories:
- text-classification
language:
- en
pretty_name: WikiQA
size_categories:
- 1K<n<10K
---
WikiQA dataset with answers grouped together for each question.
|
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] |
ghbacct/financial-phrasebank-all-agree-classification
|
ghbacct
| 2023-06-27T23:32:33Z | 55 | 0 |
[
"region:us"
] | null | 2023-06-27T23:32:31Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
splits:
- name: train
num_bytes: 242670.00044169612
num_examples: 1811
- name: test
num_bytes: 60700.99955830388
num_examples: 453
download_size: 181798
dataset_size: 303371.0
---
# Dataset Card for "financial-phrasebank-all-agree-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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language-and-voice-lab/samromur_milljon
|
language-and-voice-lab
| 2023-10-15T15:14:45Z | 55 | 2 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:is",
"license:cc-by-4.0",
"crowd-sourced icelandic",
"samrómur",
"icelandic speech",
"samromur",
"iceland",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2023-07-09T04:29:15Z |
---
annotations_creators:
- crowdsourced
language:
- is
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: "Samrómur Milljón, Audio and Transcriptions"
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- crowd-sourced icelandic
- samrómur
- icelandic speech
- samromur
- iceland
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for samromur_milljon
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#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:** [Samrómur Milljón](https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon)
- **Repository:** [Language and Voice Laboratory](https://huggingface.co/language-and-voice-lab)
- **Point of Contact:** [Carlos Mena](mailto:[email protected]), [Jón Guðnason](mailto:[email protected])
### Dataset Summary
Samrómur Milljón is the result of the automatic verification of the corpus [Samromur Unverified 22.07](http://hdl.handle.net/20.500.12537/265) which contains 2,159,314 (2233 hours) speech-recordings in Icelandic that are essentially unverified. Samrómur Milljón consists of 1,002,157 of speech recordings (967 hours). The transcripts accompanying these recordings were automatically verified using various ASR systems such as: [Wav2Vec2](https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h), [Whisper](https://huggingface.co/language-and-voice-lab/whisper-large-icelandic-30k-steps-1000h), [Faster-Whisper](https://huggingface.co/language-and-voice-lab/whisper-large-icelandic-30k-steps-1000h-ct2) and [NeMo](https://huggingface.co/carlosdanielhernandezmena/stt_is_quartznet15x5_ft_ep56_875h). If any of the ASR systems get a transcript that perfectly matches the reference transcription, the speech-recording is then cosidered as verified.
### Example Usage
Samrómur Milljón is divided in 7 portions. To load a specific split pass its name as a config name:
```python
from datasets import load_dataset
samromur_milljon = load_dataset("language-and-voice-lab/samromur_milljon")
```
To load a specific split (for example, the split with female speakers between 18 and 49 years old) do:
```python
from datasets import load_dataset
samromur_milljon = load_dataset("language-and-voice-lab/samromur_milljon",split="female_18to49_yrs")
```
### Supported Tasks
automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
### Languages
The audio is in Icelandic.
The reading prompts were gathered from a variety of sources, mainly from the [Icelandic Gigaword Corpus](http://clarin.is/en/resources/gigaword). The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/).
## Dataset Structure
### Data Instances
```python
{
'audio_id': '000664-0013663',
'audio': {
'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/5238762551eb35654e659f0bf3f3bf30ba02b40036715337500cba84051fed4e/FEM_18to49_YRS_PART_0001/000664/000664-0013663.flac',
'array': array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,
3.0517578e-05, 3.0517578e-05], dtype=float32),
'sampling_rate': 16000
},
'speaker_id': '000664',
'gender': 'female',
'age': '30-39',
'duration': 3.7799999713897705,
'verified_with': 'V+F',
'normalized_text': 'mattías lækkaðu í hátalaranum'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `speaker_id` (string) - id of speaker
* `gender` (string) - gender of speaker (male or female)
* `age` (string) - range of age of the speaker.
* `duration` (float32) - duration of the audio file in seconds.
* `verified_with` (string) - Set of ASR systems that found a perfect match between the current speech file and the reference transcription : V = wav2vec, N = NeMo, W = Whisper, F = Whisper-Faster
* `normalized_text` (string) - normalized audio segment transcription.
### Data Splits
Due to its large size, the corpus is divided in 7 splits with the objective that it will be more manageable this way.
* Female speakers less than 18 years old (n < 18): **female_lt_18_yrs**
* Female speakers from 18 to 49 years old (18 <= n <=49): **female_18to49_yrs**
* Female speakers greater than 49 years old (n > 49): **female_gt_49_yrs**
* Male speakers less than 18 years old (n < 18): **male_lt_18_yrs**
* Male speakers from 18 to 49 years old (18 <= n <=49): **male_18to49_yrs**
* Male speakers greater than 49 years old (n > 49): **male_gt_49_yrs**
* Speakers where age, gender or both are unknown: **other**
To load an specific portion please see the above section "Example Usage".
## Dataset Creation
### Curation Rationale
* The collection process started in October 2019.
* The aim is to create an open-source speech corpus to enable research and development for Icelandic Language Technology.
* The corpus comprises of audio recordings and metadata files containing the text sentences read by the participants.
* At the beginning, some data was manually verified by summer students, but due to the success of the collection process, we ended up with more than 2 million of unverified speech-recordings. In 2023, it was decided to use the distict ASR systems that we already had in Icelandic to automatically verify as much data as possible. The verification process consited in transcribing the recordings using various ASR systems. If one or more of them get a transcription that perfectly matches the reference transcription, the recording is considered as verified.
* The resulting corpus of more than 1 million recordings is too big to download it all at once. In consequence, it was decided to split it in 7 portions with the hope that it will be more manageable this way.
### Source Data
#### Initial Data Collection and Normalization
* The utterances were recorded by a smartphone or the web app.
* The data was collected using the website https://samromur.is, code of which is available at https://github.com/cadia-lvl/samromur.
* Each recording contains one read sentence from a script.
### Annotations
#### Annotation process
Prompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the [DIM: Database Icelandic Morphology](https://aclanthology.org/W19-6116.pdf).
There are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device.
#### Who are the annotators?
The audio files content was automatically verified using ASR systems such as: [Wav2Vec2](https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h), [Whisper](https://huggingface.co/language-and-voice-lab/whisper-large-icelandic-30k-steps-1000h), [Faster-Whisper](https://huggingface.co/language-and-voice-lab/whisper-large-icelandic-30k-steps-1000h-ct2) and [NeMo](https://huggingface.co/carlosdanielhernandezmena/stt_is_quartznet15x5_ft_ep56_875h). If any of the ASR systems get a transcript that perfectly matches the reference transcription, the speech-recording is then cosidered as verified.
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This contribution describes a project of speech data collection, using the web application [Samrómur](samromur.is) which is built upon [Common Voice](https://commonvoice.mozilla.org/), Mozilla Foundation's web platform for open-source voice collection. The goal of the project is to build a large-scale speech corpus for Automatic Speech Recognition (ASR) for Icelandic. Samrómur is the largest open speech corpus for Icelandic collected from the public domain.
### Discussion of Biases
* The participants are aged between 4 to 90 years old. The speech files were recorded by a smartphone or the web app.
* Participants self-reported their age group, gender, and the native language.
* The corpus contains 1,002,157 from 16,729 speakers, totalling 967 hours and 7 minutes.
* The number of recordings coming from female speakers are 714,564, totalling 697h22m.
* The number of recordings coming from male speakers are 282,499, totalling 264h28m.
* The number of recordings where the gender, the age or both is unknown are 5,094, totalling 5h16m. These recordings belong to the split called "other".
### Other Known Limitations
"Samrómur Milljón" by the Language and Voice Laboratory (LVL) from Reykjavik University (RU) is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
## Additional Information
### Dataset Curators
The corpus was curated by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena) during 2023 at the [Language and Voice Laboratory](https://lvl.ru.is/) from [Reykjavik University](https://en.ru.is/).
### Licensing Information
[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@misc{menasamromurmilljon2023,
title={Samrómur Milljón, Audio and Transcriptions},
author={Hernández Mena, Carlos Daniel and Guðnason, Jón},
publisher={Reykjavík University},
year={2023},
url={https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon}
}
```
### Contributions
This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.
|
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ivrit-ai/audio-base
|
ivrit-ai
| 2023-09-26T05:49:29Z | 55 | 4 |
[
"task_categories:audio-classification",
"task_categories:voice-activity-detection",
"size_categories:1K<n<10K",
"language:he",
"license:other",
"arxiv:2307.08720",
"region:us"
] |
[
"audio-classification",
"voice-activity-detection"
] | 2023-07-15T08:01:33Z |
---
license: other
task_categories:
- audio-classification
- voice-activity-detection
language:
- he
size_categories:
- 1K<n<10K
extra_gated_prompt:
"You agree to the following license terms:
This material and data is licensed under the terms of the Creative Commons Attribution 4.0
International License (CC BY 4.0), The full text of the CC-BY 4.0 license is available at
https://creativecommons.org/licenses/by/4.0/.
Notwithstanding the foregoing, this material and data may only be used, modified and distributed for
the express purpose of training AI models, and subject to the foregoing restriction. In addition, this
material and data may not be used in order to create audiovisual material that simulates the voice or
likeness of the specific individuals appearing or speaking in such materials and data (a “deep-fake”).
To the extent this paragraph is inconsistent with the CC-BY-4.0 license, the terms of this paragraph
shall govern.
By downloading or using any of this material or data, you agree that the Project makes no
representations or warranties in respect of the data, and shall have no liability in respect thereof. These
disclaimers and limitations are in addition to any disclaimers and limitations set forth in the CC-BY-4.0
license itself. You understand that the project is only able to make available the materials and data
pursuant to these disclaimers and limitations, and without such disclaimers and limitations the project
would not be able to make available the materials and data for your use."
extra_gated_fields:
I have read the license, and agree to its terms: checkbox
---
ivrit.ai is a database of Hebrew audio and text content.
**audio-base** contains the raw, unprocessed sources.
**audio-vad** contains audio snippets generated by applying Silero VAD (https://github.com/snakers4/silero-vad) to the base dataset.
v1 data is generated using silero-vad's default parameters.
v2 data is generated using min_speech_duration_ms=2000 (milliseconds), and max_speech_duration_s=30 (seconds).
**audio-transcripts** contains transcriptions for each snippet in the audio-vad dataset.
You can find the full list of sources in this dataset under https://www.ivrit.ai/en/credits.
Paper: https://arxiv.org/abs/2307.08720
If you use our datasets, the following quote is preferable:
```
@misc{marmor2023ivritai,
title={ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development},
author={Yanir Marmor and Kinneret Misgav and Yair Lifshitz},
year={2023},
eprint={2307.08720},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
```
|
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ashercn97/OpenOrcaSmaller2
|
ashercn97
| 2023-07-19T20:20:50Z | 55 | 1 |
[
"region:us"
] | null | 2023-07-19T01:10:32Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 284383027
num_examples: 156291
download_size: 161343770
dataset_size: 284383027
---
# Dataset Card for "OpenOrcaSmaller2"
This is a small subset of the OpenOrca dataset that I got rid of all of the missing rows and changed it to an Alpaca format. I will hopefully use this to finetune a small model!
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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sngsfydy/aptos_test
|
sngsfydy
| 2023-07-19T19:19:46Z | 55 | 0 |
[
"region:us"
] | null | 2023-07-19T19:18:30Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
splits:
- name: train
num_bytes: 1802932566.6624794
num_examples: 733
download_size: 1800938316
dataset_size: 1802932566.6624794
---
# Dataset Card for "aptos_dataset2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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emozilla/pg19-test-tokenized
|
emozilla
| 2023-08-08T19:26:51Z | 55 | 0 |
[
"region:us"
] | null | 2023-08-08T19:26:47Z |
---
dataset_info:
features:
- name: short_book_title
dtype: string
- name: publication_date
dtype: int32
- name: url
dtype: string
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: tokenized_len
dtype: int64
splits:
- name: test
num_bytes: 97172727
num_examples: 100
download_size: 45658545
dataset_size: 97172727
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "pg19-test-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
adirik/fashion_image_caption-100
|
adirik
| 2023-08-29T10:41:48Z | 55 | 0 |
[
"region:us"
] | null | 2023-08-29T10:41:47Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 22842342.0
num_examples: 100
download_size: 22823708
dataset_size: 22842342.0
---
# Dataset Card for "fashion_image_caption-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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SeyedAli/Persian-Text-Sentiment
|
SeyedAli
| 2023-09-09T15:42:06Z | 55 | 1 |
[
"task_categories:text-classification",
"language:fa",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2023-09-08T18:09:45Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 10222986
num_examples: 55852
- name: test
num_bytes: 2575303
num_examples: 13964
download_size: 6076096
dataset_size: 12798289
task_categories:
- text-classification
language:
- fa
---
Dataset Classes
* negetive :0
* positive :1
|
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