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albertvillanova/medmnist-v2 | albertvillanova | 2023-05-30T05:40:52Z | 183 | 3 | medmnist-v2 | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"task_ids:multi-label-image-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"medical",
"arxiv:2110.14795",
"region:us"
] | 2023-05-30T05:40:52Z | 2023-05-29T09:00:40.000Z | 2023-05-29T09:00:40 | ---
language: en
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: MedMNIST v2
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
- multi-label-image-classification
paperswithcode_id: medmnist-v2
tags:
- medical
---
# Dataset Card for MedMNIST v2
## 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://medmnist.com/
- **Repository:** https://github.com/MedMNIST/MedMNIST
- **Paper:** [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://arxiv.org/abs/2110.14795)
- **Leaderboard:**
- **Point of Contact:** [Bingbing Ni](mailto:nibingbing@sjtu.edu.cn)
### Dataset Summary
We introduce MedMNIST v2, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28 x 28 (2D) or 28 x 28 x 28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of 708,069 2D images and 9,998 3D images in total, could support numerous research / educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## 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
The dataset is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) (CC BY 4.0).
Each subset keeps the same license as that of the source dataset. Please also cite the corresponding paper of source data if you use any subset of MedMNIST.
### Citation Information
If you find this project useful, please cite both v1 and v2 papers:
```
@article{medmnistv2,
title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
journal={Scientific Data},
volume={10},
number={1},
pages={41},
year={2023},
publisher={Nature Publishing Group UK London}
}
@inproceedings{medmnistv1,
title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
pages={191--195},
year={2021}
}
```
Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the [project website](https://medmnist.com/).
### Contributions
Thanks to [@albertvillanova](https://huggingface.co/albertvillanova) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
microsoft/LCC_csharp | microsoft | 2023-06-21T02:59:17Z | 183 | 3 | null | [
"region:us"
] | 2023-06-21T02:59:17Z | 2023-06-21T02:46:10.000Z | 2023-06-21T02:46:10 | ---
dataset_info:
features:
- name: context
dtype: string
- name: gt
dtype: string
splits:
- name: train
num_bytes: 1851797668
num_examples: 100000
- name: validation
num_bytes: 136620599
num_examples: 10000
- name: test
num_bytes: 136701413
num_examples: 10000
download_size: 581666513
dataset_size: 2125119680
---
# Dataset Card for "LCC_csharp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
explodinggradients/WikiEval | explodinggradients | 2023-09-18T15:12:16Z | 183 | 0 | null | [
"region:us"
] | 2023-09-18T15:12:16Z | 2023-08-24T10:01:45.000Z | 2023-08-24T10:01:45 | ---
dataset_info:
features:
- name: answer
dtype: string
- name: question
dtype: string
- name: context_v1
sequence: string
- name: context_v2
sequence: string
- name: ungrounded_answer
dtype: string
- name: source
dtype: string
- name: poor_answer
dtype: string
splits:
- name: train
num_bytes: 548755
num_examples: 50
download_size: 354738
dataset_size: 548755
---
# WikiEval
Dataset for to do correlation analysis of difference metrics proposed in [Ragas](https://github.com/explodinggradients/ragas)
This dataset was generated from 50 pages from Wikipedia with edits post 2022.
## Column description
* question: a question that can be answered from the given Wikipedia page (source).
* source: The source Wikipedia page from which the question and context are generated.
* grounded_answer: answer grounded on context_v1
* ungrounded_answer: answer generated without context_v1
* poor_answer: answer with poor relevancy compared to grounded_answer and ungrounded_answer
* context_v1: Ideal context to answer the given question
* contetx_v2: context that contains redundant information compared to context_v1 | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
RealTimeData/wikitext_alltime | RealTimeData | 2023-10-12T17:26:51Z | 183 | 0 | null | [
"license:cc-by-2.0",
"region:us"
] | 2023-10-12T17:26:51Z | 2023-09-30T20:40:28.000Z | 2023-09-30T20:40:28 | ---
license: cc-by-2.0
---
# Wikipedia for All Times
You could find the history of 419 selected Wikipedia pages for every month between 2017 to 2022.
Use this to download the historical version of Wikipedia articles in a specific month:
```
ds = datasets.load_dataset('RealTimeData/wikitext_alltime', '2017-8')
```
The time stamp follows the format of "YYYY-MM". | [
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kresnik/zeroth_korean | kresnik | 2023-01-04T06:54:55Z | 182 | 5 | null | [
"region:us"
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bond005/sova_rudevices | bond005 | 2022-11-01T15:59:30Z | 182 | 1 | null | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"source_datasets:extended",
"language:ru",
"license:cc-by-4.0",
"region:us"
] | 2022-11-01T15:59:30Z | 2022-11-01T13:03:55.000Z | 2022-11-01T13:03:55 | ---
pretty_name: RuDevices
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- ru
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id:
size_categories:
- 10K<n<100k
source_datasets:
- extended
task_categories:
- automatic-speech-recognition
- audio-classification
---
# Dataset Card for sova_rudevices
## 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:** [SOVA RuDevices](https://github.com/sovaai/sova-dataset)
- **Repository:** [SOVA Dataset](https://github.com/sovaai/sova-dataset)
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
- **Point of Contact:** [SOVA.ai](mailto:support@sova.ai)
### Dataset Summary
SOVA Dataset is free public STT/ASR dataset. It consists of several parts, one of them is SOVA RuDevices. This part is an acoustic corpus of approximately 100 hours of 16kHz Russian live speech with manual annotating, prepared by [SOVA.ai team](https://github.com/sovaai).
Authors do not divide the dataset into train, validation and test subsets. Therefore, I was compelled to prepare this splitting. The training subset includes more than 82 hours, the validation subset includes approximately 6 hours, and the test subset includes approximately 6 hours too.
### Supported Tasks and Leaderboards
- `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). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER.
### Languages
The audio is in Russian.
## Dataset Structure
### Data Instances
A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided.
```
{'audio': {'path': '/home/bond005/datasets/sova_rudevices/data/train/00003ec0-1257-42d1-b475-db1cd548092e.wav',
'array': array([ 0.00787354, 0.00735474, 0.00714111, ...,
-0.00018311, -0.00015259, -0.00018311]), dtype=float32),
'sampling_rate': 16000},
'transcription': 'мне получше стало'}
```
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- transcription: the transcription of the audio file.
### Data Splits
This dataset consists of three splits: training, validation, and test. This splitting was realized with accounting of internal structure of SOVA RuDevices (the validation split is based on the subdirectory `0`, and the test split is based on the subdirectory `1` of the original dataset), but audio recordings of the same speakers can be in different splits at the same time (the opposite is not guaranteed).
| | Train | Validation | Test |
| ----- | ------ | ---------- | ----- |
| examples | 81607 | 5835 | 5799 |
| hours | 82.4h | 5.9h | 5.8h |
## 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
All recorded audio files were manually annotated.
#### Who are the annotators?
[Needs More Information]
### 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Egor Zubarev, Timofey Moskalets, and SOVA.ai team.
### Licensing Information
[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@misc{sova2021rudevices,
author = {Zubarev, Egor and Moskalets, Timofey and SOVA.ai},
title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech},
publisher = {GitHub},
journal = {GitHub repository},
year = {2021},
howpublished = {\url{https://github.com/sovaai/sova-dataset}},
}
```
### Contributions
Thanks to [@bond005](https://github.com/bond005) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rwkv-x-dev/openorca-gpt4 | rwkv-x-dev | 2023-08-28T14:44:06Z | 182 | 3 | null | [
"region:us"
] | 2023-08-28T14:44:06Z | 2023-08-28T14:38:13.000Z | 2023-08-28T14:38:13 | ---
pretty_name: OpenOrca
configs:
- config_name: default
default: true
data_files:
- split: train
path:
- "*.parquet"
---
OpenOrca but just the GPT4 bits. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cherry0324/oxfordpets_caption | cherry0324 | 2023-11-17T18:06:41Z | 182 | 0 | null | [
"region:us"
] | 2023-11-17T18:06:41Z | 2023-11-17T17:59:58.000Z | 2023-11-17T17:59:58 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 325420058.632
num_examples: 3693
download_size: 393978599
dataset_size: 325420058.632
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oxfordpets_caption"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
manishiitg/hi-llm | manishiitg | 2023-11-24T06:43:40Z | 182 | 0 | null | [
"region:us"
] | 2023-11-24T06:43:40Z | 2023-11-22T08:24:12.000Z | 2023-11-22T08:24:12 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 318565947
num_examples: 85460
download_size: 139306765
dataset_size: 318565947
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hi-llm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
maydogan/TRSAv1 | maydogan | 2022-02-24T12:36:52Z | 181 | 1 | null | [
"region:us"
] | 2022-02-24T12:36:52Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | TRSAv1 (Turkish Sentiment Analysis Version 1) Dataset
This data set has been produced to contribute to Turkish NLP studies.
The dataset consists of a total of 150 thousand samples, 50 thousand negative, 50 thousand positive and 50 thousand neutral.
It can be used in text classification and sentiment analysis studies by citing the related study.
Related Work
Aydoğan M, Kocaman V. TRSAv1: A new benchmark dataset for classifying user reviews on Turkish e-commerce websites. Journal of Information Science. February 2022. doi:10.1177/01655515221074328 | [
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stepkurniawan/sustainability-methods-wiki | stepkurniawan | 2023-10-16T18:53:27Z | 181 | 0 | null | [
"license:mit",
"region:us"
] | 2023-10-16T18:53:27Z | 2023-09-20T12:48:53.000Z | 2023-09-20T12:48:53 | ---
license: mit
configs:
- config_name: 50_QA
data_files:
- split: train
path: 50_QA/train-*
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
config_name: 50_QA
features:
- name: contexts
dtype: string
- name: summary
dtype: string
- name: question
dtype: string
- name: ground_truths
dtype: string
splits:
- name: train
num_bytes: 78182
num_examples: 50
download_size: 57005
dataset_size: 78182
---
This is a table dump from Prof. Henrik van Wehrden's famous sustainability wiki. He is a sustainability professor in Leuphana University, Germany, and passionate about digitalizing his mind. Therefore, the wiki is born.
This Wiki pages are focused on sustainability and highly subjective on his view of the world.
Link: https://sustainabilitymethods.org/index.php/Main_Page | [
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dwadden/healthver_entailment | dwadden | 2023-10-31T00:37:09Z | 181 | 0 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-2.0",
"region:us"
] | 2023-10-31T00:37:09Z | 2023-10-30T22:27:12.000Z | 2023-10-30T22:27:12 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: HealthVer
dataset_info:
features:
- name: claim_id
dtype: int32
- name: claim
dtype: string
- name: abstract_id
dtype: int32
- name: title
dtype: string
- name: abstract
sequence: string
- name: verdict
dtype: string
- name: evidence
sequence: int32
splits:
- name: train
num_bytes: 9490482
num_examples: 5292
- name: validation
num_bytes: 1707997
num_examples: 940
- name: test
num_bytes: 1620257
num_examples: 903
download_size: 3610222
dataset_size: 12818736
---
# Dataset Card for "healthver_entailment"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
## Dataset Description
- **Repository:** <https://github.com/sarrouti/HealthVe>
- **Point of Contact:** [David Wadden](mailto:davidw@allenai.org)
### Dataset Summary
HealthVer is a dataset of public health claims, verified against scientific research articles. For this version of the dataset, we follow the preprocessing from the MultiVerS modeling paper https://github.com/dwadden/multivers, verifying claims against full article abstracts rather than individual sentences. Entailment labels and rationales are included.
## Dataset Structure
### Data fields
- `claim_id`: An `int32` claim identifier.
- `claim`: A `string`.
- `abstract_id`: An `int32` abstract identifier.
- `title`: A `string`.
- `abstract`: A list of `strings`, one for each sentence in the abstract.
- `verdict`: The fact-checking verdict, a `string`.
- `evidence`: A list of sentences from the abstract which provide evidence for the verdict.
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0.07156331092119217,
0.15509778261184692,
-0.289797842502594,
-0.5944621562957764
] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
beanham/medsum | beanham | 2023-11-15T20:58:04Z | 181 | 0 | null | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"medical",
"region:us"
] | 2023-11-15T20:58:04Z | 2023-11-15T20:27:01.000Z | 2023-11-15T20:27:01 | ---
task_categories:
- summarization
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
---
This dataset comes from the EACL 2023 paper: An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters
https://github.com/abachaa/MTS-Dialog/tree/main/Main-Dataset | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sehyun66/STOCKPRICE | sehyun66 | 2023-11-24T13:29:07Z | 181 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-24T13:29:07Z | 2023-11-24T13:10:29.000Z | 2023-11-24T13:10:29 | ---
license: mit
dataset_info:
config_name: NASDAQ_3y
features:
- name: Date
dtype: string
- name: Open
dtype: float64
- name: High
dtype: float64
- name: Low
dtype: float64
- name: Close
dtype: float64
- name: Adj Close
dtype: float64
- name: Volume
dtype: int64
- name: Ticker
dtype: string
splits:
- name: train
num_bytes: 112710930
num_examples: 1612680
download_size: 64458393
dataset_size: 112710930
configs:
- config_name: NASDAQ_3y
data_files:
- split: train
path: NASDAQ_3y/train-*
---
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TnT/Multi_CodeNet4Repair | TnT | 2023-08-08T12:07:38Z | 180 | 7 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | 2023-08-08T12:07:38Z | 2023-08-03T07:57:44.000Z | 2023-08-03T07:57:44 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- code
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: problem_id
dtype: string
- name: user_id
dtype: string
- name: time_limit
dtype: float64
- name: memory_limit
dtype: float64
- name: problem_description
dtype: string
- name: codes
dtype: string
- name: status
dtype: string
- name: submission_ids
dtype: string
- name: memories
dtype: string
- name: cpu_times
dtype: string
- name: code_sizes
dtype: string
splits:
- name: train
num_bytes: 5338549
num_examples: 1088
- name: test
num_bytes: 3239026
num_examples: 623
download_size: 2211909
dataset_size: 8577575
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Sakil/DPO_dataset | Sakil | 2023-08-25T12:36:57Z | 180 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-08-25T12:36:57Z | 2023-08-25T12:19:09.000Z | 2023-08-25T12:19:09 | ---
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
EMBO/BLURB | EMBO | 2022-12-09T07:57:37Z | 179 | 3 | null | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:closed-domain-qa",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2007.15779",
"arxiv:1909.06146",
"region:us"
] | 2022-12-09T07:57:37Z | 2022-03-14T10:29:16.000Z | 2022-03-14T10:29:16 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
- sentence-similarity
- text-classification
task_ids:
- closed-domain-qa
- named-entity-recognition
- parsing
- semantic-similarity-scoring
- text-scoring
- topic-classification
pretty_name: BLURB (Biomedical Language Understanding and Reasoning Benchmark.)
---
# Dataset Card for BLURB
## 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://microsoft.github.io/BLURB/index.html
- **Paper:** [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://arxiv.org/pdf/2007.15779.pdf)
- **Leaderboard:** https://microsoft.github.io/BLURB/leaderboard.html
- **Point of Contact:**
### Dataset Summary
BLURB is a collection of resources for biomedical natural language processing. In general domains, such as newswire and the Web, comprehensive benchmarks and leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. In biomedicine, however, such resources are ostensibly scarce. In the past, there have been a plethora of shared tasks in biomedical NLP, such as BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These efforts have played a significant role in fueling interest and progress by the research community, but they typically focus on individual tasks. The advent of neural language models, such as BERT provides a unifying foundation to leverage transfer learning from unlabeled text to support a wide range of NLP applications. To accelerate progress in biomedical pretraining strategies and task-specific methods, it is thus imperative to create a broad-coverage benchmark encompassing diverse biomedical tasks.
Inspired by prior efforts toward this direction (e.g., BLUE), we have created BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP applications, as well as a leaderboard for tracking progress by the community. BLURB includes thirteen publicly available datasets in six diverse tasks. To avoid placing undue emphasis on tasks with many available datasets, such as named entity recognition (NER), BLURB reports the macro average across all tasks as the main score. The BLURB leaderboard is model-agnostic. Any system capable of producing the test predictions using the same training and development data can participate. The main goal of BLURB is to lower the entry barrier in biomedical NLP and help accelerate progress in this vitally important field for positive societal and human impact.
#### BC5-chem
The corpus consists of three separate sets of
articles with diseases, chemicals and their relations annotated.
The training (500 articles) and development (500 articles) sets
were released to task participants in advance to support text-mining
method development. The test set (500 articles) was used for final
system performance evaluation.
- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [BioCreative V CDR task corpus: a resource for chemical disease relation extraction](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/)
#### BC5-disease
The corpus consists of three separate sets of
articles with diseases, chemicals and their relations annotated.
The training (500 articles) and development (500 articles) sets
were released to task participants in advance to support text-mining
method development. The test set (500 articles) was used for final
system performance evaluation.
- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [BioCreative V CDR task corpus: a resource for chemical disease relation extraction](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/)
#### BC2GM
The BioCreative II Gene Mention task.
The training corpus for the current task consists mainly of
the training and testing corpora (text collections) from the
BCI task, and the testing corpus for the current task
consists of an additional 5,000 sentences that were held
'in reserve' from the previous task.
In the current corpus, tokenization is not provided;
instead participants are asked to identify a gene mention
in a sentence by giving its start and end characters.
As before, the training set consists of a set of sentences,
and for each sentence a set of gene mentions
(GENE annotations).
- **Homepage:** https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [verview of BioCreative II gene mention recognition](https://link.springer.com/article/10.1186/gb-2008-9-s2-s2)
#### NCBI Disease
The NCBI disease corpus is fully annotated at the mention
and concept level to serve as a research resource for the biomedical natural
language processing community.
Corpus Characteristics
----------------------
* 793 PubMed abstracts
* 6,892 disease mentions
* 790 unique disease concepts
* Medical Subject Headings (MeSH®)
* Online Mendelian Inheritance in Man (OMIM®)
* 91% of the mentions map to a single disease concept
**divided into training, developing and testing sets.
Corpus Annotation
* Fourteen annotators
* Two-annotators per document (randomly paired)
* Three annotation phases
* Checked for corpus-wide consistency of annotations
- **Homepage:** https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [NCBI disease corpus: a resource for disease name recognition and concept normalization](https://pubmed.ncbi.nlm.nih.gov/24393765/)
#### JNLPBA
The BioNLP / JNLPBA Shared Task 2004 involves the identification
and classification of technical terms referring to concepts of interest to
biologists in the domain of molecular biology. The task was organized by GENIA
Project based on the annotations of the GENIA Term corpus (version 3.02).
Corpus format: The JNLPBA corpus is distributed in IOB format, with each line
containing a single token and its tag, separated by a tab character.
Sentences are separated by blank lines.
- **Homepage: ** http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper: ** [Introduction to the Bio-entity Recognition Task at JNLPBA](https://aclanthology.org/W04-1213)
#### EBM PICO
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
#### ChemProt
- **Homepage:**
- **Repository:**
- **Paper:**
#### DDI
- **Homepage:**
- **Repository:**
- **Paper:**
#### GAD
- **Homepage:**
- **Repository:**
- **Paper:**
#### BIOSSES
BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article.
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows:
- very strong: 0.80–1.00
- strong: 0.60–0.79
- moderate: 0.40–0.59
- weak: 0.20–0.39
- very weak: 0.00–0.19
- **Homepage:** https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
- **Repository:** https://github.com/gizemsogancioglu/biosses
- **Paper:** [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954)
- **Point of Contact:** [Gizem Soğancıoğlu](gizemsogancioglu@gmail.com) and [Arzucan Özgür](gizemsogancioglu@gmail.com)
#### HoC
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
#### PubMedQA
We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement. PubMedQA is publicly available at this https URL.
- **Homepage:** https://pubmedqa.github.io/
- **Repository:** https://github.com/pubmedqa/pubmedqa
- **Paper:** [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/pdf/1909.06146.pdf)
- **Leaderboard:** [Question answering](https://pubmedqa.github.io/)
- **Point of Contact:**
#### BioASQ
Task 7b will use benchmark datasets containing training and test biomedical questions, in English, along with gold standard (reference) answers. The participants will have to respond to each test question with relevant concepts (from designated terminologies and ontologies), relevant articles (in English, from designated article repositories), relevant snippets (from the relevant articles), relevant RDF triples (from designated ontologies), exact answers (e.g., named entities in the case of factoid questions) and 'ideal' answers (English paragraph-sized summaries). 2747 training questions (that were used as dry-run or test questions in previous year) are already available, along with their gold standard answers (relevant concepts, articles, snippets, exact answers, summaries).
- **Homepage:** http://bioasq.org/
- **Repository:** http://participants-area.bioasq.org/datasets/
- **Paper:** [Automatic semantic classification of scientific literature according to the hallmarks of cancer](https://academic.oup.com/bioinformatics/article/32/3/432/1743783?login=false)
### Supported Tasks and Leaderboards
| **Dataset** | **Task** | **Train** | **Dev** | **Test** | **Evaluation Metrics** | **Added** |
|:------------:|:-----------------------:|:---------:|:-------:|:--------:|:----------------------:|-----------|
| BC5-chem | NER | 5203 | 5347 | 5385 | F1 entity-level | **Yes** |
| BC5-disease | NER | 4182 | 4244 | 4424 | F1 entity-level | **Yes** |
| NCBI-disease | NER | 5134 | 787 | 960 | F1 entity-level | **Yes** |
| BC2GM | NER | 15197 | 3061 | 6325 | F1 entity-level | **Yes** |
| JNLPBA | NER | 46750 | 4551 | 8662 | F1 entity-level | **Yes** |
| EBM PICO | PICO | 339167 | 85321 | 16364 | Macro F1 word-level | No |
| ChemProt | Relation Extraction | 18035 | 11268 | 15745 | Micro F1 | No |
| DDI | Relation Extraction | 25296 | 2496 | 5716 | Micro F1 | No |
| GAD | Relation Extraction | 4261 | 535 | 534 | Micro F1 | No |
| BIOSSES | Sentence Similarity | 64 | 16 | 20 | Pearson | **Yes** |
| HoC | Document Classification | 1295 | 186 | 371 | Average Micro F1 | No |
| PubMedQA | Question Answering | 450 | 50 | 500 | Accuracy | **Yes** |
| BioASQ | Question Answering | 670 | 75 | 140 | Accuracy | No |
Datasets used in the BLURB biomedical NLP benchmark. The Train, Dev, and test splits might not be exactly identical to those proposed in BLURB.
This is something to be checked.
### Languages
English from biomedical texts
## Dataset Structure
### Data Instances
* **NER**
```json
{
'id': 0,
'tokens': [ "DPP6", "as", "a", "candidate", "gene", "for", "neuroleptic", "-", "induced", "tardive", "dyskinesia", "." ]
'ner_tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
}
```
* **PICO**
```json
{
'TBD'
}
```
* **Relation Extraction**
```json
{
'TBD'
}
```
* **Sentence Similarity**
```json
{'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation'
'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase'
'score': 2.2}
```
* **Document Classification**
```json
{
'TBD'
}
```
* **Question Answering**
* PubMedQA
```json
{'context': {'contexts': ['Programmed cell death (PCD) is the regulated death of cells within an organism. The lace plant (Aponogeton madagascariensis) produces perforations in its leaves through PCD. The leaves of the plant consist of a latticework of longitudinal and transverse veins enclosing areoles. PCD occurs in the cells at the center of these areoles and progresses outwards, stopping approximately five cells from the vasculature. The role of mitochondria during PCD has been recognized in animals; however, it has been less studied during PCD in plants.',
'The following paper elucidates the role of mitochondrial dynamics during developmentally regulated PCD in vivo in A. madagascariensis. A single areole within a window stage leaf (PCD is occurring) was divided into three areas based on the progression of PCD; cells that will not undergo PCD (NPCD), cells in early stages of PCD (EPCD), and cells in late stages of PCD (LPCD). Window stage leaves were stained with the mitochondrial dye MitoTracker Red CMXRos and examined. Mitochondrial dynamics were delineated into four categories (M1-M4) based on characteristics including distribution, motility, and membrane potential (ΔΨm). A TUNEL assay showed fragmented nDNA in a gradient over these mitochondrial stages. Chloroplasts and transvacuolar strands were also examined using live cell imaging. The possible importance of mitochondrial permeability transition pore (PTP) formation during PCD was indirectly examined via in vivo cyclosporine A (CsA) treatment. This treatment resulted in lace plant leaves with a significantly lower number of perforations compared to controls, and that displayed mitochondrial dynamics similar to that of non-PCD cells.'],
'labels': ['BACKGROUND', 'RESULTS'],
'meshes': ['Alismataceae',
'Apoptosis',
'Cell Differentiation',
'Mitochondria',
'Plant Leaves'],
'reasoning_free_pred': ['y', 'e', 's'],
'reasoning_required_pred': ['y', 'e', 's']},
'final_decision': 'yes',
'long_answer': 'Results depicted mitochondrial dynamics in vivo as PCD progresses within the lace plant, and highlight the correlation of this organelle with other organelles during developmental PCD. To the best of our knowledge, this is the first report of mitochondria and chloroplasts moving on transvacuolar strands to form a ring structure surrounding the nucleus during developmental PCD. Also, for the first time, we have shown the feasibility for the use of CsA in a whole plant system. Overall, our findings implicate the mitochondria as playing a critical and early role in developmentally regulated PCD in the lace plant.',
'pubid': 21645374,
'question': 'Do mitochondria play a role in remodelling lace plant leaves during programmed cell death?'}
```
### Data Fields
* **NER**
* `id`: string
* `ner_tags`: Sequence[ClassLabel]
* `tokens`: Sequence[String]
* **PICO**
* To be added
* **Relation Extraction**
* To be added
* **Sentence Similarity**
* `sentence 1`: string
* `sentence 2`: string
* `score`: float ranging from 0 (no relation) to 4 (equivalent)
* **Document Classification**
* To be added
* **Question Answering**
* PubMedQA
* `pubid`: integer
* `question`: string
* `context`: sequence of strings [`contexts`, `labels`, `meshes`, `reasoning_required_pred`, `reasoning_free_pred`]
* `long_answer`: string
* `final_decision`: string
### Data Splits
Shown in the table of supported tasks.
## Dataset Creation
### Curation Rationale
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES
* HoC
* PubMedQA
* BioASQ
### Source Data
[More Information Needed]
### Annotations
All the datasets have been obtained and annotated by experts in the biomedical domain. Check the different citations for further details.
#### Annotation process
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES - The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees.
* HoC
* PubMedQA
* BioASQ
### Dataset Curators
All the datasets have been obtained and annotated by experts in thebiomedical domain. Check the different citations for further details.
### Licensing Information
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES - BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).
* HoC
* PubMedQA - MIT License Copyright (c) 2019 pubmedqa
* BioASQ
### Citation Information
* BC5-chem & BC5-disease
```latex
@article{article,
author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
year = {2016},
month = {05},
pages = {baw068},
title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
volume = {2016},
journal = {Database},
doi = {10.1093/database/baw068}
}
```
* BC2GM
```latex
@article{article,
author = {Smith, Larry and Tanabe, Lorraine and Ando, Rie and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig and Povinelli, Richard and Vlachos, Andreas and Baumgartner Jr, William and Hunter, Lawrence and Carpenter, Bob and Wilbur, W.},
year = {2008},
month = {09},
pages = {S2},
title = {Overview of BioCreative II gene mention recognition},
volume = {9 Suppl 2},
journal = {Genome biology},
doi = {10.1186/gb-2008-9-s2-s2}
}
```
* JNLPBA
```latex
@inproceedings{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and
Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
month = aug # " 28th and 29th",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://aclanthology.org/W04-1213",
pages = "73--78",
}
```
* NCBI Disiease
```latex
@article{10.5555/2772763.2772800,
author = {Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
title = {NCBI Disease Corpus},
year = {2014},
issue_date = {February 2014},
publisher = {Elsevier Science},
address = {San Diego, CA, USA},
volume = {47},
number = {C},
issn = {1532-0464},
abstract = {Graphical abstractDisplay Omitted NCBI disease corpus is built as a gold-standard resource for disease recognition.793 PubMed abstracts are annotated with disease mentions and concepts (MeSH/OMIM).14 Annotators produced high consistency level and inter-annotator agreement.Normalization benchmark results demonstrate the utility of the corpus.The corpus is publicly available to the community. Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora.This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH ) or Online Mendelian Inheritance in Man (OMIM ). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.},
journal = {J. of Biomedical Informatics},
month = {feb},
pages = {1–10},
numpages = {10}}
```
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES
```latex
@article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
}
```
* HoC
* PubMedQA
```latex
@inproceedings{jin2019pubmedqa,
title={PubMedQA: A Dataset for Biomedical Research Question Answering},
author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={2567--2577},
year={2019}
}
```
* BioASQ
```latex
@article{10.1093/bioinformatics/btv585,
author = {Baker, Simon and Silins, Ilona and Guo, Yufan and Ali, Imran and Högberg, Johan and Stenius, Ulla and Korhonen, Anna},
title = "{Automatic semantic classification of scientific literature according to the hallmarks of cancer}",
journal = {Bioinformatics},
volume = {32},
number = {3},
pages = {432-440},
year = {2015},
month = {10},
abstract = "{Motivation: The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.Results: We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.Availability and implementation: The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html .Contact:simon.baker@cl.cam.ac.uk}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/btv585},
url = {https://doi.org/10.1093/bioinformatics/btv585},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/32/3/432/19568147/btv585.pdf},
}
```
### Contributions
* This dataset has been uploaded and generated by Dr. Jorge Abreu Vicente.
* Thanks to [@GamalC](https://github.com/GamalC) for uploading the NER datasets to GitHub, from where I got them.
* I am not part of the team that generated BLURB. This dataset is intended to help researchers to usethe BLURB benchmarking for NLP in Biomedical NLP.
* Thanks to [@bwang482](https://github.com/bwang482) for uploading the [BIOSSES dataset](https://github.com/bwang482/datasets/tree/master/datasets/biosses). We forked the [BIOSSES 🤗 dataset](https://huggingface.co/datasets/biosses) to add it to this BLURB benchmark.
* Thank you to [@tuner007](https://github.com/tuner007) for adding this dataset to the 🤗 hub | [
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GEM/xwikis | GEM | 2023-02-22T13:05:19Z | 179 | 2 | null | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:de",
"language:en",
"language:fr",
"language:cs",
"license:cc-by-sa-4.0",
"arxiv:2202.09583",
"region:us"
] | 2023-02-22T13:05:19Z | 2022-03-14T15:31:48.000Z | 2022-03-14T15:31:48 | ---
annotations_creators:
- found
language_creators:
- unknown
language:
- de
- en
- fr
- cs
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xwikis
---
# Dataset Card for GEM/xwikis
## Dataset Description
- **Homepage:** https://github.com/lauhaide/clads
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/2202.09583
- **Leaderboard:** N/A
- **Point of Contact:** Laura Perez-Beltrachini
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xwikis).
### Dataset Summary
The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/xwikis')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/xwikis).
#### website
[Github](https://github.com/lauhaide/clads)
#### paper
https://arxiv.org/abs/2202.09583
#### authors
Laura Perez-Beltrachini (University of Edinburgh)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/lauhaide/clads)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
https://arxiv.org/abs/2202.09583
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@InProceedings{clads-emnlp,
author = "Laura Perez-Beltrachini and Mirella Lapata",
title = "Models and Datasets for Cross-Lingual Summarisation",
booktitle = "Proceedings of The 2021 Conference on Empirical Methods in Natural Language Processing ",
year = "2021",
address = "Punta Cana, Dominican Republic",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Laura Perez-Beltrachini
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
lperez@ed.ac.uk
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`German`, `English`, `French`, `Czech`, `Chinese`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Cross-lingual and Multi-lingual single long input document abstractive summarisation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Entity descriptive summarisation, that is, generate a summary that conveys the most salient facts of a document related to a given entity.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Laura Perez-Beltrachini (University of Edinburgh)
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Laura Perez-Beltrachini (University of Edinburgh) and Ronald Cardenas (University of Edinburgh)
### Dataset Structure
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
For each language pair and direction there exists a train/valid/test split.
The test split is a sample of size 7k from the intersection of titles existing in the four languages (cs,fr,en,de).
Train/valid are randomly split.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
- identification of entity salient information
- translation
- multi-linguality
- cross-lingual transfer, zero-shot, few-shot
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
ROUGE-1/2/L
## Dataset Curation
### Original Curation
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
other
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
found
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
The input documents have section structure information.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by another rater
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Bilingual annotators assessed the content overlap of source document and target summaries.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`public domain`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
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CEBaB/CEBaB | CEBaB | 2022-08-16T21:54:47Z | 179 | 5 | null | [
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lambdalabs/naruto-blip-captions | lambdalabs | 2022-10-27T21:17:06Z | 179 | 12 | null | [
"region:us"
] | 2022-10-27T21:17:06Z | 2022-10-27T18:02:46.000Z | 2022-10-27T18:02:46 | # Dataset Card for Naruto BLIP captions
_Dataset used to train [TBD](TBD)._
The original images were obtained from [narutopedia.com](https://naruto.fandom.com/wiki/Narutopedia) and captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
## Example stable diffusion outputs
> "Bill Gates with a hoodie", "John Oliver with Naruto style", "Hello Kitty with Naruto style", "Lebron James with a hat", "Mickael Jackson as a ninja", "Banksy Street art of ninja"
## Citation
If you use this dataset, please cite it as:
```
@misc{cervenka2022naruto2,
author = {Cervenka, Eole},
title = {Naruto BLIP captions},
year={2022},
howpublished= {\url{https://huggingface.co/datasets/lambdalabs/naruto-blip-captions/}}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
izumi-lab/llm-japanese-dataset-vanilla | izumi-lab | 2023-09-29T14:40:26Z | 179 | 10 | null | [
"size_categories:1M<n<10M",
"language:ja",
"license:cc-by-sa-4.0",
"arxiv:2305.12720",
"arxiv:2309.03412",
"region:us"
] | 2023-09-29T14:40:26Z | 2023-05-23T14:45:27.000Z | 2023-05-23T14:45:27 | ---
license: cc-by-sa-4.0
language:
- ja
size_categories:
- 1M<n<10M
---
# llm-japanese-dataset-vanilla
LLM構築用の日本語チャットデータセット
[izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) から，日英翻訳のデータセット等を抜いたものです．
主に，日本語LLMモデルなどに対して，チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます．
※様々な公開言語資源を利用させていただきました．関係各位にはこの場を借りて御礼申し上げます．
## データの詳細
データの詳細は，[izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) に関する，以下の論文を参照してください．
- 日本語: [https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383](https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383)
- 英語: [https://arxiv.org/abs/2305.12720](https://arxiv.org/abs/2305.12720)
- GitHub: [https://github.com/masanorihirano/llm-japanese-dataset](https://github.com/masanorihirano/llm-japanese-dataset)
- 最新情報: [llm.msuzuki.me](https://llm.msuzuki.me).
なお，Citationには，よろしければ，以下をご利用ください．
```
@preprint{Suzuki2023-llmvanilla,
title={{From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models}},
autor={Masahiro Suzuki and Masanori Hirano and Hiroki Sakaji},
doi={10.48550/arXiv.2309.03412},
archivePrefix={arXiv},
arxivId={2309.03412},
year={2023}
}
```
共同研究，データ提供，各種支援，その他問い合わせは，izumi-llm@socsim.org へ．
## How to use
```python
from datasets import load_dataset
dataset = load_dataset("izumi-lab/llm-japanese-dataset-vanilla", revision="0.1.0")
print(dataset.num_rows)
# {'train': 1811964}
dataset = load_dataset("izumi-lab/llm-japanese-dataset-vanilla", revision="1.0.0")
print(dataset.num_rows)
# {'train': 2515626}
```
v0.1.0 contains 1,811,964 data
v1.0.0 contains 2,515,626 data
For more details, see: https://github.com/masanorihirano/llm-japanese-dataset/tree/vanilla
## LICENSE
CC-BY-SA 4.0
(For more details, see: LICENSE, NOTICE.md, NOTICE2.md)
## Note
To see more latest information, please go to [llm.msuzuki.me](https://llm.msuzuki.me).
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vblagoje/lfqa | vblagoje | 2021-10-17T13:44:46Z | 178 | 13 | null | [
"region:us"
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nampdn-ai/tinystories-vietnamese | nampdn-ai | 2023-07-14T01:34:17Z | 178 | 6 | null | [
"language:en",
"language:vi",
"license:mit",
"doi:10.57967/hf/0894",
"region:us"
] | 2023-07-14T01:34:17Z | 2023-07-12T10:34:01.000Z | 2023-07-12T10:34:01 | ---
license: mit
language:
- en
- vi
pretty_name: Vietnamese Tiny Stories
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
simonycl/p3_0.5_dataset | simonycl | 2023-11-03T17:45:50Z | 178 | 0 | null | [
"language:en",
"region:us"
] | 2023-11-03T17:45:50Z | 2023-10-31T23:52:01.000Z | 2023-10-31T23:52:01 | ---
language:
- en
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: choices
sequence: string
- name: input
dtype: string
- name: output
dtype: string
- name: dataset
dtype: string
- name: category
dtype: string
- name: prompt_template
dtype: string
splits:
- name: train
num_bytes: 193730861
num_examples: 304955
- name: test
num_bytes: 20032003
num_examples: 17255
download_size: 86885152
dataset_size: 213762864
---
# Dataset Card for P3_0.5
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```bash
{
'choices': [ "Yes", "No" ],
'input': "Given that No Weapons of Mass Destruction Found in Iraq Yet. Does it follow that Weapons of Mass Destruction Found in Iraq. Yes or no?",
'label': "No",
'dataset': "rte",
'category': "nli",
'prompt_template': "super_glue_rte_does_it_follow_that"
}
```
To check all the prompted examples, you can use the [Promptsource hosted tool](http://bigscience.huggingface.co/promptsource) and choose the `Prompted dataset viewer` mode in the left panel.
### Data Fields
The data fields are the same among all splits:
- `choices`: the choices (in natural language) available to the model
- `input`: the natural language input fed to the model
- `label`: the natural language target that the model has to generate
- `dataset`: the dataset that the data are from
- `category`: the NLP task it belongs to
- `prompt_template`: the prompt template used to form the input
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
galatolo/TeTIm-Eval | galatolo | 2022-12-15T14:58:24Z | 177 | 1 | null | [
"task_categories:text-to-image",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"curated",
"high-quality",
"text-to-image",
"evaluation",
"validation",
"region:us"
] | 2022-12-15T14:58:24Z | 2022-08-13T09:53:36.000Z | 2022-08-13T09:53:36 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc
multilinguality:
- monolingual
pretty_name: TeTIm-Eval
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- curated
- high-quality
- text-to-image
- evaluation
- validation
task_categories:
- text-to-image
task_ids: []
---
# TeTIm-Eval
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hugginglearners/netflix-shows | hugginglearners | 2022-08-18T03:04:55Z | 177 | 4 | null | [
"license:cc0-1.0",
"region:us"
] | 2022-08-18T03:04:55Z | 2022-08-18T03:04:50.000Z | 2022-08-18T03:04:50 | ---
license:
- cc0-1.0
kaggle_id: infamouscoder/dataset-netflix-shows
---
# Dataset Card for Dataset: NetFlix Shows
## 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://kaggle.com/datasets/infamouscoder/dataset-netflix-shows
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The raw data is Web Scrapped through Selenium. It contains Unlabelled text data of around 9000 Netflix Shows and Movies along with Full details like Cast, Release Year, Rating, Description, etc.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was shared by [@infamouscoder](https://kaggle.com/infamouscoder)
### Licensing Information
The license for this dataset is cc0-1.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] | [
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composite/pauq | composite | 2023-11-07T08:34:15Z | 177 | 2 | null | [
"region:us"
] | 2023-11-07T08:34:15Z | 2023-07-17T09:45:17.000Z | 2023-07-17T09:45:17 | ---
dataset_info:
- config_name: ru_os
features:
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dtype: string
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dtype: string
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dtype: string
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sequence: string
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sequence: string
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dtype: string
splits:
- name: train
num_examples: 8800
- name: test
num_examples: 1074
- config_name: en_os
features:
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splits:
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num_examples: 8800
- name: test
num_examples: 1076
- config_name: ru_trl
features:
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features:
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splits:
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features:
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splits:
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features:
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---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
Link to databases: https://drive.google.com/file/d/1Xjbp207zfCaBxhPgt-STB_RxwNo2TIW2/view
### Dataset Summary
The Russian version of the [Spider](https://yale-lily.github.io/spider) - Yale Semantic Parsing and Text-to-SQL Dataset.
Major changings:
- Adding (not replacing) new Russian language values in DB tables. Table and DB names remain the original.
- Localization of natural language questions into Russian. All DB values replaced by new.
- Changing in SQL-queries filters.
- Filling empty table with values.
- Complementing the dataset with the new samples of underrepresented types.
### Languages
Russian
## Dataset Creation
### Curation Rationale
The translation from English to Russian is undertaken by a professional human translator with SQL-competence. A verification of the translated questions and their conformity with the queries, and an updating of the databases are undertaken by 4 computer science students.
Details are in the [section 3](https://aclanthology.org/2022.findings-emnlp.175.pdf).
## Additional Information
### Licensing Information
The presented dataset have been collected in a manner which is consistent with the terms of use of the original Spider, which is distributed under the CC BY-SA 4.0 license.
### Citation Information
[Paper link](https://aclanthology.org/2022.findings-emnlp.175.pdf)
```
@inproceedings{bakshandaeva-etal-2022-pauq,
title = "{PAUQ}: Text-to-{SQL} in {R}ussian",
author = "Bakshandaeva, Daria and
Somov, Oleg and
Dmitrieva, Ekaterina and
Davydova, Vera and
Tutubalina, Elena",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.175",
pages = "2355--2376",
abstract = "Semantic parsing is an important task that allows to democratize human-computer interaction. One of the most popular text-to-SQL datasets with complex and diverse natural language (NL) questions and SQL queries is Spider. We construct and complement a Spider dataset for Russian, thus creating the first publicly available text-to-SQL dataset for this language. While examining its components - NL questions, SQL queries and databases content - we identify limitations of the existing database structure, fill out missing values for tables and add new requests for underrepresented categories. We select thirty functional test sets with different features that can be used for the evaluation of neural models{'} abilities. To conduct the experiments, we adapt baseline architectures RAT-SQL and BRIDGE and provide in-depth query component analysis. On the target language, both models demonstrate strong results with monolingual training and improved accuracy in multilingual scenario. In this paper, we also study trade-offs between machine-translated and manually-created NL queries. At present, Russian text-to-SQL is lacking in datasets as well as trained models, and we view this work as an important step towards filling this gap.",
}
```
### Contributions
Thanks to [@gugutse](https://github.com/Gugutse), [@runnerup96](https://github.com/runnerup96), [@dmi3eva](https://github.com/dmi3eva), [@veradavydova](https://github.com/VeraDavydova), [@tutubalinaev](https://github.com/tutubalinaev) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
FinGPT/fingpt-fiqa_qa | FinGPT | 2023-10-10T06:51:12Z | 177 | 2 | null | [
"region:us"
] | 2023-10-10T06:51:12Z | 2023-10-10T06:37:38.000Z | 2023-10-10T06:37:38 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 20914549
num_examples: 17110
download_size: 10813846
dataset_size: 20914549
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "fingpt-fiqa_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tungkho178/data_v2 | tungkho178 | 2023-11-25T09:12:41Z | 177 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-25T09:12:41Z | 2023-11-23T17:01:12.000Z | 2023-11-23T17:01:12 | ---
license: apache-2.0
---
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GEM/SIMPITIKI | GEM | 2022-10-24T15:30:05Z | 176 | 2 | null | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:it",
"license:cc-by-4.0",
"region:us"
] | 2022-10-24T15:30:05Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- it
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: SIMPITIKI
---
# Dataset Card for GEM/SIMPITIKI
## Dataset Description
- **Homepage:** https://github.com/dhfbk/simpitiki
- **Repository:** https://github.com/dhfbk/simpitiki/tree/master/corpus
- **Paper:** http://ceur-ws.org/Vol-1749/paper52.pdf
- **Leaderboard:** N/A
- **Point of Contact:** Sara Tonelli
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/SIMPITIKI).
### Dataset Summary
SIMPITIKI is an Italian Simplification dataset. Its examples were selected from Italian Wikipedia such that their editing tracking descriptions contain any of the words "Simplified"/"Simplify"/"Simplification".
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/SIMPITIKI')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/SIMPITIKI).
#### website
[Github](https://github.com/dhfbk/simpitiki)
#### paper
[Website](http://ceur-ws.org/Vol-1749/paper52.pdf)
#### authors
Sara Tonelli (Fondazione Bruno Kessler), Alessio Palmero Aprosio (Fondazione Bruno Kessler), Francesca Saltori (Fondazione Bruno Kessler)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/dhfbk/simpitiki)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/dhfbk/simpitiki/tree/master/corpus)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Website](http://ceur-ws.org/Vol-1749/paper52.pdf)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@article{tonelli2016simpitiki,
title={SIMPITIKI: a Simplification corpus for Italian},
author={Tonelli, Sara and Aprosio, Alessio Palmero and Saltori, Francesca},
journal={Proceedings of CLiC-it},
year={2016}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Sara Tonelli
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
satonelli@fbk.eu
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
None
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Italian`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The purpose of the dataset is to train NLG models to simplify complex text by learning different types of transformations (verb to noun, noun to verbs, deletion, insertion, etc)
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Simplification
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
This dataset aims to enhance research in text simplification in Italian language with different text transformations.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`, `independent`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Fondazione Bruno Kessler (FBK)
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Sara Tonelli (Fondazione Bruno Kessler), Alessio Palmero Aprosio (Fondazione Bruno Kessler), Francesca Saltori (Fondazione Bruno Kessler)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
EU Horizon 2020 Programme via the SIMPATICO Project (H2020-EURO-6-2015, n. 692819)
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Sebastien Montella (Orange Labs), Vipul Raheja (Grammarly Inc.)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
Each sample comes with the following fields:
- `gem_id` (string): Unique sample ID
-`text` (string): The raw text to be simplified
-`simplified_text` (string): The simplified version of "text" field
-`transformation_type` (string): Nature of transformation applied to raw text in order to simplify it.
-`source_dataset` (string): Initial dataset source of sample. Values: 'itwiki' (for Italian Wikipedia) or 'tn' (manually annotated administrative documents from the Municipality of Trento, Italy)
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The dataset is organized as a pairs where the raw text (input) is associated with its simplified text (output). The editing transformation and the source dataset of each sample is also provided for advanced analysis.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
SIMPITIKI dataset selects documents from Italian Wikipedia such that their editing tracking descriptions contain any of the words "Simplified"/"Simplify"/"Simplification". For the Public Administration domain of the documents of the Municipality of Trento (Italy)
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{"transformation_id": 31, "transformation_type": "Transformation - Lexical Substitution (word level)", "source_dataset": "tn", "text": "- assenza per <del>e</del>si<del>genze</del> particolari attestate da relazione dei servizi sociali;", "simplified_text": "- assenza per <ins>bi</ins>s<ins>ogn</ins>i particolari attestati da relazione dei servizi sociali;"}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
Several splits are proposed to train models on different configurations:
-"train": Training samples randomly selected from initial corpus. 816 training samples.
-"validation": Validating samples randomly selected from initial corpus. 174 validating samples.
-"test": Testing samples randomly selected from initial corpus. 176 validating samples.
-"challenge_seen_transformations_train": This training challenge split includes specific transformations to simplify the raw text. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 562 training samples.
-"challenge_seen_transformations_val": This validating challenge split includes same transformations than the ones observed in training. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 121 validating samples.
-"challenge_seen_transformations_test": This testing challenge split includes same transformations than the ones observed in training. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 127 testing samples.
-"challenge_unseen_transformations_test" : "Insert - Subject", "Delete - Subject", "Transformation - Lexical Substitution (phrase level)", "Transformation - Verb to Noun (nominalization)", "Transformation - Verbal Voice". 356 testing samples.
-"challenge_itwiki_train": This challenge split includes random samples from the Italian Wikipedia as source dataset. 402 training samples.
-"challenge_itwiki_val": This validating challenge split includes random samples from the Italian Wikipedia as source dataset. 86 validating samples.
-"challenge_itwiki_test": This testing challenge split includes random samples from the Italian Wikipedia as source dataset. 87 testing samples.
-"challenge_tn_test": This testing challenge split includes all samples from the Municipality of Trento administrative documents ('tn') as source dataset. 591 testing samples.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The training ratio is set to 0.7. The validation and test somehow equally divide the remaining 30% of the dataset.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset promotes Simplification task for Italian language.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Models can be evaluated if they can simplify text regarding different simplification transformations.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
The SIMPITIKI dataset provides a single file. Several splits are proposed to train models on different configurations:
-"train": Training samples randomly selected from initial corpus. 816 training samples.
-"validation": Validating samples randomly selected from initial corpus. 174 validating samples.
-"test": Testing samples randomly selected from initial corpus. 176 validating samples.
-"challenge_seen_transformations_train": This training challenge split includes specific transformations to simplify the raw text. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 562 training samples.
-"challenge_seen_transformations_val": This validating challenge split includes same transformations than the ones observed in training. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 121 validating samples.
-"challenge_seen_transformations_test": This testing challenge split includes same transformations than the ones observed in training. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 127 testing samples.
-"challenge_unseen_transformations_test" : "Insert - Subject", "Delete - Subject", "Transformation - Lexical Substitution (phrase level)", "Transformation - Verb to Noun (nominalization)", "Transformation - Verbal Voice". 356 testing samples.
-"challenge_itwiki_train": This challenge split includes random samples from the Italian Wikipedia as source dataset. 402 training samples.
-"challenge_itwiki_val": This validating challenge split includes random samples from the Italian Wikipedia as source dataset. 86 validating samples.
-"challenge_itwiki_test": This testing challenge split includes random samples from the Italian Wikipedia as source dataset. 87 testing samples.
-"challenge_tn_test": This testing challenge split includes all samples from the Municipality of Trento administrative documents ('tn') as source dataset. 591 testing samples.
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
The splits allows to investigate the generalization of models regarding editing/transformations ("challenge_seen_transformations_test" / "challenge_unseen_transformations_test") and for transfer learning to different domain ("challenge_tn_test")
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- Coster and Kauchak, Simple English Wikipedia: A New Text Simplification Task, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 665–669, Portland, Oregon, June 19-24, 2011
- Xu et al, Optimizing Statistical Machine Translation for Text Simplification, Transactions of the Association for Computational Linguistics, vol. 4, pp. 401–415, 2016
- Aprosio et al, Neural Text Simplification in Low-Resource Conditions Using Weak Supervision, Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation (NeuralGen), pages 37–44, Minneapolis, Minnesota, USA, June 6, 2019
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
Simplification: Process that consists in transforming an input text to its simplified version.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
The splits allows to investigate the generalization of models regarding editing/transformations ("challenge_seen_transformations_test" / "challenge_unseen_transformations_test") and for transfer learning to different domain ("challenge_tn_test")
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
FKBLEU (https://aclanthology.org/Q16-1029.pdf): Combines Flesch-Kincaid Index and iBLEU metrics.
SARI (https://aclanthology.org/Q16-1029.pdf): Compares system output against references and against the input sentence. It explicitly measures the goodness of words that are added, deleted and kept by the systems
Word-level F1
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Most of the resources for Text Simplification are in English. To stimulate research to different languages, SIMPITIKI proposes an Italian corpus with Complex-Simple sentence pairs.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Text simplification allows a smooth reading of text to enhance understanding.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
Italian Wikipedia
(Manually) Annotated administrative documents from the Municipality of Trento, Italy
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`, `Offline media collection`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
SIMPITIKI is a combination of documents from Italian Wikipedia and from the Municipality of Trento, Italy.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Samples from documents from the Municipality of Trento corpus are in the administrative domain.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
crowd-sourced
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
unknown
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
Native speaker
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
0
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
0
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
unknown
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Annotators specified any of the tags as designed by Brunato et al. (https://aclanthology.org/W15-1604/):
-Split: Splitting a clause into two clauses.
-Merge: Merge two or more clauses together.
-Reordering: Word order changes.
-Insert: Insertion of words or phrases that provide supportive information to the original sentence
-Delete: dropping redundant information.
-Transformation: Modification which can affect the sentence at the lexical, morpho-syntactic and syntactic level: Lexical substitution (word level) / Lexical substitution (phrase level) / Anaphoric replacement / Noun to Verb / Verb to Noun / Verbal voice / Verbal features/ morpho–syntactic and syntactic level, also giving rise to overlapping phenomena
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
unknown
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The dataset is available online under the CC-BY 4.0 license.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
The creator of SIMPITIKI wants to promote text simplification for Italian because few resources are available in other languages than English.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`research use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`research use only`
### Known Technical Limitations
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
The risk of surface-based metrics (BLEU, chrf++, etc) for this task is that semantic adequacy is not respected when simplifying the input document.
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bertin-project/mc4-sampling | bertin-project | 2022-11-07T12:40:51Z | 176 | 8 | mc4 | [
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] | 2022-11-07T12:40:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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license:
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size_categories:
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task_categories:
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task_ids:
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paperswithcode_id: mc4
pretty_name: mC4-sampling
language_bcp47:
- bg-Latn
- el-Latn
- hi-Latn
- ja-Latn
- ru-Latn
- zh-Latn
---
# Dataset Card for mC4-sampling
## Table of Contents
- [Dataset Card for mC4-sampling](#dataset-card-for-mc4-sampling)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Sampling](#dataset-sampling)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://huggingface.co/bertin-project/bertin-roberta-base-spanish
### Dataset Summary
This dataset builds upon the AllenAI version of the original [mC4](https://huggingface.co/datasets/allenai/c4) and adds sampling methods to perform perplexity-based filtering on the fly. Please, refer to [BERTIN Project](https://huggingface.co/bertin-project/bertin-roberta-base-spanish).
The original dataset is mC4, the multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org".
108 languages are available and are reported in the [`mc4` dataset](https://huggingface.co/datasets/mc4#dataset-summary).
You can load the mC4 subset of any language like this:
```python
from datasets import load_dataset
en_mc4 = load_dataset("mc4", "en")
```
And if you can even specify a list of languages:
```python
from datasets import load_dataset
mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"])
```
### Dataset Sampling
There are 3 main different ways of getting sampled versions of mc4 using this dataset.
#### Random
Arguably, the simplest of methods. It keeps a document based on a probability threshold we called `factor`. It defaults to `0.5` for random sampling:
```python
def _should_keep_doc_random(self, doc, factor=None, **kwargs):
factor = 0.5 if factor is None else factor
return self.rng.uniform() <= factor
```
The way to use this sampling method is by adding an extra parameter to the instantiation of the dataset:
```python
from datasets import load_dataset
mc4random = load_dataset(
"bertin-project/mc4-sampling", "es",
split="train",
streaming=True,
sampling_method="random",
factor=0.5,
)
for sample in mc4random:
print(sample)
break
```
#### Gaussian
This sampling method tries to adjust to the underlying distribution while oversampling the central quartiles of the perplexity distribution of the documents in mC4 for a given language. Two parameters control the shape of the approximation, `factor` (peakness of the exponential function) and `width` (spread). Default values are selected for Spanish.
```python
def _should_keep_doc_gaussian(self, doc, factor=None, width=None, boundaries=None, **kwargs):
perplexity = self.get_perplexity(doc)
width = (9 / 2) if width is None else width
factor = 0.78 if factor is None else factor
median = 662247.50212365 if boundaries is None else boundaries[1]
exponential = np.exp((-1 / width) * ((perplexity - median) / median) ** 2)
weighted_perplexity = factor * exponential
return self.rng.uniform() < weighted_perplexity
```
In order to use this sampling methods, information about the quartile boundaries of the underlying distribution need to be calculated beforehand and passed in to the instantiation of the dataset. Moreover, the path to a [KenLM model](https://github.com/kpu/kenlm/) (5-gram language model) or an object with a method `.score(text:str) -> float` need to also be passed in for the calculation of the perplexity value of a document. KenLM can be installed with pip:
```bash
pip install https://github.com/kpu/kenlm/archive/master.zip
```
```python
from datasets import load_dataset
mc4gaussian = load_dataset(
"bertin-project/mc4-sampling",
"es",
split="train",
streaming=True,
sampling_method="gaussian",
perplexity_model="./es.arpa.bin",
boundaries=[536394.99320948, 662247.50212365, 919250.87225178],
factor=0.78,
width=9/2,
)
for sample in mc4gaussian:
print(sample)
break
```
Facebook has created and released 5-gram Kneser-Ney models for 100 languages available to download and use within the KenLM library. To download your own Kneser-Ney language model, chose a language code from the next list:
```bash
af,ar,az,be,bg,bn,ca,cs,da,de,el,en,es,et,fa,fi,fr,gu,he,hi,hr,hu,hy,id,is,it,ja,ka,kk,km,kn,ko,lt,lv,mk,ml,mn,mr,my,ne,nl,no,pl,pt,ro,ru,uk,zh
```
And run the next download command replacing `lang` with your own language code:
```bash
wget http://dl.fbaipublicfiles.com/cc_net/lm/lang.arpa.bin
```
### Stepwise
The stepwise sampling method uses a simple criteria by oversampling from the central quartiles inversely proportionally their range. Only `boundaries`, `factor` (strength of the oversampling), and `perplexity_model` are needed:
```python
def _should_keep_doc_step(self, doc, factor=None, boundaries=None, **kwargs):
perplexity = self.get_perplexity(doc)
factor = 1.5e5 if factor is None else factor
if boundaries is None:
boundaries = [536394.99320948, 662247.50212365, 919250.87225178]
if perplexity <= boundaries[0]:
quartile_range = boundaries[0]
elif boundaries[0] < perplexity < boundaries[1]:
quartile_range = boundaries[1] - boundaries[0]
elif boundaries[1] < perplexity < boundaries[2]:
quartile_range = boundaries[2] - boundaries[1]
elif perplexity >= boundaries[2]:
quartile_range = 10 * boundaries[2]
probability = factor / quartile_range
return self.rng.uniform() < probability
```
In order to use this sampling method, a similar invocation is needed:
```python
mc4stepwsie = load_dataset(
"bertin-project/mc4-sampling",
"es",
split="train",
streaming=True,
sampling_method="stepwise",
perplexity_model="./es.arpa.bin",
boundaries=[536394.99320948, 662247.50212365, 919250.87225178],
factor=1.5e5,
)
for sample in mc4stepwsie:
print(sample)
break
```
### Supported Tasks and Leaderboards
mC4-sampling is mainly intended to pretrain language models and word representations on a budget.
### Languages
The dataset supports 108 languages.
## Dataset Structure
### Data Instances
An example form the `en` config is:
```
{'timestamp': '2018-06-24T01:32:39Z',
'text': 'Farm Resources in Plumas County\
Show Beginning Farmer Organizations & Professionals (304)\
There are 304 resources serving Plumas County in the following categories:\
Map of Beginning Farmer Organizations & Professionals serving Plumas County\
Victoria Fisher - Office Manager - Loyalton, CA\
Amy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\
Show Farm Income Opportunities Organizations & Professionals (353)\
There are 353 resources serving Plumas County in the following categories:\
Farm Ranch And Forest Retailers (18)\
Map of Farm Income Opportunities Organizations & Professionals serving Plumas County\
Warner Valley Wildlife Area - Plumas County\
Show Farm Resources Organizations & Professionals (297)\
There are 297 resources serving Plumas County in the following categories:\
Map of Farm Resources Organizations & Professionals serving Plumas County\
There are 57 resources serving Plumas County in the following categories:\
Map of Organic Certification Organizations & Professionals serving Plumas County',
'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'}
```
### Data Fields
The data have several fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp as a string
### Data Splits
The same splits as in [mC4 are available](https://huggingface.co/datasets/mc4#data-splits).
## Additional Information
### Licensing Information
BERTIN Project is releasing this dataset under the same terms AllenAI released mC4, that is, those of the ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
### Citation Information
To cite this dataset:
```bibtex
@article{BERTIN,
author = {Javier De la Rosa y Eduardo G. Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury},
title = {{BERTIN}: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling},
journal = {Procesamiento del Lenguaje Natural},
volume = {68},
number = {0},
year = {2022},
keywords = {},
abstract = {The pre-training of large language models usually requires massive amounts of resources, both in terms of computation and data. Frequently used web sources such as Common Crawl might contain enough noise to make this pretraining sub-optimal. In this work, we experiment with different sampling methods from the Spanish version of mC4, and present a novel data-centric technique which we name perplexity sampling that enables the pre-training of language models in roughly half the amount of steps and using one fifth of the data. The resulting models are comparable to the current state-of-the-art, and even achieve better results for certain tasks. Our work is proof of the versatility of Transformers, and paves the way for small teams to train their models on a limited budget.},
issn = {1989-7553},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6403},
pages = {13--23}
}
```
If you use this dataset, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.
To cite the original `mc4` dataset:
```
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
### Contributions
Dataset contributed by [@versae](https://github.com/versae).
Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding the original mC4 dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mozilla-foundation/common_voice_10_0 | mozilla-foundation | 2023-07-29T16:00:14Z | 176 | 19 | common-voice | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | 2023-07-29T16:00:14Z | 2022-07-22T15:10:26.000Z | 2022-07-22T15:10:26 | ---
pretty_name: Common Voice Corpus 10.0
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language_bcp47:
- ab
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- gl
- gn
- ha
- hi
- hsb
- hu
- hy-AM
- ia
- id
- ig
- it
- ja
- ka
- kab
- kk
- kmr
- ky
- lg
- lt
- lv
- mdf
- mhr
- mk
- ml
- mn
- mr
- mt
- myv
- nan-tw
- ne-NP
- nl
- nn-NO
- or
- pa-IN
- pl
- pt
- rm-sursilv
- rm-vallader
- ro
- ru
- rw
- sah
- sat
- sc
- sk
- sl
- sr
- sv-SE
- sw
- ta
- th
- tig
- tok
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- vot
- yue
- zh-CN
- zh-HK
- zh-TW
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- 10K<n<100K
ar:
- 100K<n<1M
as:
- 1K<n<10K
ast:
- n<1K
az:
- n<1K
ba:
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bas:
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be:
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bg:
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bn:
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br:
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ca:
- 1M<n<10M
ckb:
- 100K<n<1M
cnh:
- 1K<n<10K
cs:
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cv:
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cy:
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da:
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de:
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dv:
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el:
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en:
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eo:
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es:
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et:
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eu:
- 100K<n<1M
fa:
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fi:
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fr:
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fy-NL:
- 10K<n<100K
ga-IE:
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gl:
- 10K<n<100K
gn:
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ha:
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hi:
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hsb:
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hu:
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hy-AM:
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ia:
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id:
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ig:
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it:
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ja:
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ka:
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kk:
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ky:
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lt:
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lv:
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mn:
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mr:
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mt:
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myv:
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nan-tw:
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ne-NP:
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nl:
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nn-NO:
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or:
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pa-IN:
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pl:
- 100K<n<1M
pt:
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rm-sursilv:
- 1K<n<10K
rm-vallader:
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ro:
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ru:
- 100K<n<1M
rw:
- 1M<n<10M
sah:
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sat:
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sc:
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sk:
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sl:
- 10K<n<100K
sr:
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sv-SE:
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sw:
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ta:
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th:
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tig:
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tok:
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tr:
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tt:
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ug:
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uk:
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ur:
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uz:
- 100K<n<1M
vi:
- 10K<n<100K
vot:
- n<1K
yue:
- 10K<n<100K
zh-CN:
- 100K<n<1M
zh-HK:
- 100K<n<1M
zh-TW:
- 100K<n<1M
source_datasets:
- extended|common_voice
task_categories:
- automatic-speech-recognition
paperswithcode_id: common-voice
extra_gated_prompt: "By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."
---
# Dataset Card for Common Voice Corpus 10.0
## 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://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 20817 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 15234 validated hours in 96 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Toki Pona, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_10_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## 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
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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Chris1/cityscapes_segmentation | Chris1 | 2022-11-03T19:43:00Z | 176 | 1 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
CM/codexglue_code2text_python | CM | 2023-04-22T01:52:50Z | 176 | 2 | null | [
"region:us"
] | 2023-04-22T01:52:50Z | 2023-04-22T01:52:12.000Z | 2023-04-22T01:52:12 | ---
dataset_info:
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 813663148
num_examples: 251820
- name: validation
num_bytes: 46888564
num_examples: 13914
- name: test
num_bytes: 50659688
num_examples: 14918
download_size: 325303743
dataset_size: 911211400
---
# Dataset Card for "codexglue_code2text_python"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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lighteval/legal_summarization | lighteval | 2023-07-07T09:03:13Z | 176 | 13 | null | [
"region:us"
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thesistranslation/wmt14 | thesistranslation | 2023-08-09T13:08:40Z | 176 | 0 | null | [
"region:us"
] | 2023-08-09T13:08:40Z | 2023-07-31T09:21:04.000Z | 2023-07-31T09:21:04 | # Aim of this dataset
The code used to retrieve and create this dataset is almost identical to the one that you can find here [wmt14](https://huggingface.co/datasets/wmt14).
We only added the possibility to retrieve the "es-en" translation pairs from the wmt13. Keep in mind that for this language pair the validation and test sets are the newstest2012 and newstest2013 respectively.
**Pay attention**: some es-en pair sentences on the validation set contain the backslash followed by a double quote character (\\").
Thanks to the Huggingface team for all the work they have done! | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ura-hcmut/synthetic_reasoning | ura-hcmut | 2023-09-19T02:37:10Z | 176 | 0 | null | [
"task_categories:text2text-generation",
"language:vi",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-09-19T02:37:10Z | 2023-09-19T02:01:51.000Z | 2023-09-19T02:01:51 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
- vi
configs:
- config_name: induction_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_induction_training.csv
- split: test
path: synthetic_reasoning_gcp_induction.csv
- config_name: induction_azr
data_files:
- split: train
path: synthetic_reasoning_azr_induction_training.csv
- split: test
path: synthetic_reasoning_azr_induction.csv
- config_name: pattern_match_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_pattern_match_training.csv
- split: test
path: synthetic_reasoning_gcp_pattern_match.csv
- config_name: pattern_match_azr
data_files:
- split: train
path: synthetic_reasoning_azr_pattern_match_training.csv
- split: test
path: synthetic_reasoning_azr_pattern_match.csv
- config_name: variable_substitution_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_variable_substitution_training.csv
- split: test
path: synthetic_reasoning_gcp_variable_substitution.csv
- config_name: variable_substitution_azr
data_files:
- split: train
path: synthetic_reasoning_azr_variable_substitution_training.csv
- split: test
path: synthetic_reasoning_azr_variable_substitution.csv
---
# Synthetic reasoning dataset
Original version:
- https://huggingface.co/datasets/lighteval/synthetic_reasoning
Translation source code: https://github.com/martinakaduc/ura-llama/tree/main/dataset_scripts/custom_datasets | [
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LeStoe11/geeks4geeks_fixed | LeStoe11 | 2023-10-13T08:15:31Z | 176 | 0 | null | [
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SEACrowd/indosum | SEACrowd | 2023-09-26T12:28:30Z | 176 | 0 | null | [
"language:ind",
"summarization",
"region:us"
] | 2023-09-26T12:28:30Z | 2023-09-26T11:12:11.000Z | 2023-09-26T11:12:11 | ---
tags:
- summarization
language:
- ind
---
# indosum
INDOSUM is a new benchmark dataset for Indonesian text summarization.
The dataset consists of news articles and manually constructed summaries.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@INPROCEEDINGS{8629109,
author={Kurniawan, Kemal and Louvan, Samuel},
booktitle={2018 International Conference on Asian Language Processing (IALP)},
title={Indosum: A New Benchmark Dataset for Indonesian Text Summarization},
year={2018},
volume={},
number={},
pages={215-220},
doi={10.1109/IALP.2018.8629109}}
```
## License
Apache License, Version 2.0
## Homepage
[https://github.com/kata-ai/indosum](https://github.com/kata-ai/indosum)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) | [
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tmnam20/VieGLUE | tmnam20 | 2023-11-27T16:30:20Z | 176 | 0 | null | [
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jbkwon/KakaoChatData | jbkwon | 2023-11-15T01:45:29Z | 176 | 0 | null | [
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norwegian_ner | null | 2023-01-25T14:41:45Z | 175 | 0 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:no",
"license:unknown",
"region:us"
] | 2023-01-25T14:41:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- 'no'
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Norwegian NER
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---
# Dataset Card for Norwegian NER
## 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:** [Github](https://github.com/ljos/navnkjenner)
- **Repository:** [Github](https://github.com/ljos/navnkjenner)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@jplu](https://github.com/jplu) for adding this dataset. | [
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kroshan/BioASQ | kroshan | 2021-12-06T14:32:54Z | 175 | 3 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
khalidalt/HuffPost | khalidalt | 2023-05-19T18:35:08Z | 175 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2023-05-19T18:35:08Z | 2022-04-26T09:32:57.000Z | 2022-04-26T09:32:57 | ---
license: cc0-1.0
---
# Dataset Card for HuffPost
## 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://www.kaggle.com/datasets/rmisra/news-category-dataset/metadata**
### Dataset Summary
A dataset of approximately 200K news headlines from the year 2012 to 2018 collected from HuffPost.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
license: cc0-1.0
### Citation Information
```
@book{book,
author = {Misra, Rishabh and Grover, Jigyasa},
year = {2021},
month = {01},
pages = {},
title = {Sculpting Data for ML: The first act of Machine Learning},
isbn = {978-0-578-83125-1}
}
@dataset{dataset,
author = {Misra, Rishabh},
year = {2018},
month = {06},
pages = {},
title = {News Category Dataset},
doi = {10.13140/RG.2.2.20331.18729}
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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Tevatron/beir-corpus | Tevatron | 2022-07-07T23:53:45Z | 175 | 0 | null | [
"region:us"
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sbx/superlim-2 | sbx | 2023-10-12T08:10:39Z | 175 | 9 | null | [
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:token-classification",
"task_ids:sentiment-analysis",
"task_ids:acceptability-classification",
"task_ids:closed-domain-qa",
"task_ids:word-sense-disambiguation",
"task_ids:coreference-resolution",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"language:sv",
"region:us"
] | 2023-10-12T08:10:39Z | 2022-09-30T12:21:49.000Z | 2022-09-30T12:21:49 | ---
annotations_creators:
- other
language:
- sv
language_creators:
- other
license: []
multilinguality:
- monolingual
pretty_name: 'A standardized suite for evaluation and analysis of Swedish natural
language understanding systems.'
size_categories:
- unknown
source_datasets: []
tags: []
task_categories:
- multiple-choice
- text-classification
- question-answering
- sentence-similarity
- token-classification
task_ids:
- sentiment-analysis
- acceptability-classification
- closed-domain-qa
- word-sense-disambiguation
- coreference-resolution
---
# Dataset Card for [Dataset Name]
## 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:** [The official homepage of Språkbanken](https://spraakbanken.gu.se/resurser/superlim/)
- **Repository:**
- **Paper:**[SwedishGLUE – Towards a Swedish Test Set for Evaluating Natural Language Understanding Models](https://gup.ub.gu.se/publication/299130?lang=sv)
- **Leaderboard:** [To be implemented]
- **Point of Contact:**[sb-info@svenska.gu.se](sb-info@svenska.gu.se)
### Dataset Summary
SuperLim 2.0 is a continuation of SuperLim 1.0, which aims for a standardized suite for evaluation and analysis of Swedish natural language understanding systems. The projects is inspired by the GLUE/SuperGLUE projects from which the name is derived: "lim" is the Swedish translation of "glue".
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Swedish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
Most datasets have a train, dev and test split. However, there are a few (`supersim`, `sweanalogy` and `swesat-synonyms`) who only have a train and test split. The diagnostic tasks `swediagnostics` and `swewinogender` only have a test split, but they could be evaluated on models trained on `swenli` since they are also NLI-based.
## 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
To cite as a whole, use the standard reference. If you use or reference individual resources, cite the references specific for these resources:
Standard reference:
To appear in EMNLP 2023, citation will come soon.
Dataset references:
[More information needed]
Thanks to [Felix Morger](https://github.com/felixhultin) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
theodor1289/imagenet-1k_tiny | theodor1289 | 2023-03-23T08:14:11Z | 175 | 1 | null | [
"region:us"
] | 2023-03-23T08:14:11Z | 2023-03-23T08:14:03.000Z | 2023-03-23T08:14:03 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': tench, Tinca tinca
'1': goldfish, Carassius auratus
'2': great white shark, white shark, man-eater, man-eating shark, Carcharodon
carcharias
'3': tiger shark, Galeocerdo cuvieri
'4': hammerhead, hammerhead shark
'5': electric ray, crampfish, numbfish, torpedo
'6': stingray
'7': cock
'8': hen
'9': ostrich, Struthio camelus
'10': brambling, Fringilla montifringilla
'11': goldfinch, Carduelis carduelis
'12': house finch, linnet, Carpodacus mexicanus
'13': junco, snowbird
'14': indigo bunting, indigo finch, indigo bird, Passerina cyanea
'15': robin, American robin, Turdus migratorius
'16': bulbul
'17': jay
'18': magpie
'19': chickadee
'20': water ouzel, dipper
'21': kite
'22': bald eagle, American eagle, Haliaeetus leucocephalus
'23': vulture
'24': great grey owl, great gray owl, Strix nebulosa
'25': European fire salamander, Salamandra salamandra
'26': common newt, Triturus vulgaris
'27': eft
'28': spotted salamander, Ambystoma maculatum
'29': axolotl, mud puppy, Ambystoma mexicanum
'30': bullfrog, Rana catesbeiana
'31': tree frog, tree-frog
'32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
'33': loggerhead, loggerhead turtle, Caretta caretta
'34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
'35': mud turtle
'36': terrapin
'37': box turtle, box tortoise
'38': banded gecko
'39': common iguana, iguana, Iguana iguana
'40': American chameleon, anole, Anolis carolinensis
'41': whiptail, whiptail lizard
'42': agama
'43': frilled lizard, Chlamydosaurus kingi
'44': alligator lizard
'45': Gila monster, Heloderma suspectum
'46': green lizard, Lacerta viridis
'47': African chameleon, Chamaeleo chamaeleon
'48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus
komodoensis
'49': African crocodile, Nile crocodile, Crocodylus niloticus
'50': American alligator, Alligator mississipiensis
'51': triceratops
'52': thunder snake, worm snake, Carphophis amoenus
'53': ringneck snake, ring-necked snake, ring snake
'54': hognose snake, puff adder, sand viper
'55': green snake, grass snake
'56': king snake, kingsnake
'57': garter snake, grass snake
'58': water snake
'59': vine snake
'60': night snake, Hypsiglena torquata
'61': boa constrictor, Constrictor constrictor
'62': rock python, rock snake, Python sebae
'63': Indian cobra, Naja naja
'64': green mamba
'65': sea snake
'66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
'67': diamondback, diamondback rattlesnake, Crotalus adamanteus
'68': sidewinder, horned rattlesnake, Crotalus cerastes
'69': trilobite
'70': harvestman, daddy longlegs, Phalangium opilio
'71': scorpion
'72': black and gold garden spider, Argiope aurantia
'73': barn spider, Araneus cavaticus
'74': garden spider, Aranea diademata
'75': black widow, Latrodectus mactans
'76': tarantula
'77': wolf spider, hunting spider
'78': tick
'79': centipede
'80': black grouse
'81': ptarmigan
'82': ruffed grouse, partridge, Bonasa umbellus
'83': prairie chicken, prairie grouse, prairie fowl
'84': peacock
'85': quail
'86': partridge
'87': African grey, African gray, Psittacus erithacus
'88': macaw
'89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
'90': lorikeet
'91': coucal
'92': bee eater
'93': hornbill
'94': hummingbird
'95': jacamar
'96': toucan
'97': drake
'98': red-breasted merganser, Mergus serrator
'99': goose
'100': black swan, Cygnus atratus
'101': tusker
'102': echidna, spiny anteater, anteater
'103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
'104': wallaby, brush kangaroo
'105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
'106': wombat
'107': jellyfish
'108': sea anemone, anemone
'109': brain coral
'110': flatworm, platyhelminth
'111': nematode, nematode worm, roundworm
'112': conch
'113': snail
'114': slug
'115': sea slug, nudibranch
'116': chiton, coat-of-mail shell, sea cradle, polyplacophore
'117': chambered nautilus, pearly nautilus, nautilus
'118': Dungeness crab, Cancer magister
'119': rock crab, Cancer irroratus
'120': fiddler crab
'121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
'122': American lobster, Northern lobster, Maine lobster, Homarus americanus
'123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
'124': crayfish, crawfish, crawdad, crawdaddy
'125': hermit crab
'126': isopod
'127': white stork, Ciconia ciconia
'128': black stork, Ciconia nigra
'129': spoonbill
'130': flamingo
'131': little blue heron, Egretta caerulea
'132': American egret, great white heron, Egretta albus
'133': bittern
'134': crane
'135': limpkin, Aramus pictus
'136': European gallinule, Porphyrio porphyrio
'137': American coot, marsh hen, mud hen, water hen, Fulica americana
'138': bustard
'139': ruddy turnstone, Arenaria interpres
'140': red-backed sandpiper, dunlin, Erolia alpina
'141': redshank, Tringa totanus
'142': dowitcher
'143': oystercatcher, oyster catcher
'144': pelican
'145': king penguin, Aptenodytes patagonica
'146': albatross, mollymawk
'147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
'148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca
'149': dugong, Dugong dugon
'150': sea lion
'151': Chihuahua
'152': Japanese spaniel
'153': Maltese dog, Maltese terrier, Maltese
'154': Pekinese, Pekingese, Peke
'155': Shih-Tzu
'156': Blenheim spaniel
'157': papillon
'158': toy terrier
'159': Rhodesian ridgeback
'160': Afghan hound, Afghan
'161': basset, basset hound
'162': beagle
'163': bloodhound, sleuthhound
'164': bluetick
'165': black-and-tan coonhound
'166': Walker hound, Walker foxhound
'167': English foxhound
'168': redbone
'169': borzoi, Russian wolfhound
'170': Irish wolfhound
'171': Italian greyhound
'172': whippet
'173': Ibizan hound, Ibizan Podenco
'174': Norwegian elkhound, elkhound
'175': otterhound, otter hound
'176': Saluki, gazelle hound
'177': Scottish deerhound, deerhound
'178': Weimaraner
'179': Staffordshire bullterrier, Staffordshire bull terrier
'180': American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
'181': Bedlington terrier
'182': Border terrier
'183': Kerry blue terrier
'184': Irish terrier
'185': Norfolk terrier
'186': Norwich terrier
'187': Yorkshire terrier
'188': wire-haired fox terrier
'189': Lakeland terrier
'190': Sealyham terrier, Sealyham
'191': Airedale, Airedale terrier
'192': cairn, cairn terrier
'193': Australian terrier
'194': Dandie Dinmont, Dandie Dinmont terrier
'195': Boston bull, Boston terrier
'196': miniature schnauzer
'197': giant schnauzer
'198': standard schnauzer
'199': Scotch terrier, Scottish terrier, Scottie
'200': Tibetan terrier, chrysanthemum dog
'201': silky terrier, Sydney silky
'202': soft-coated wheaten terrier
'203': West Highland white terrier
'204': Lhasa, Lhasa apso
'205': flat-coated retriever
'206': curly-coated retriever
'207': golden retriever
'208': Labrador retriever
'209': Chesapeake Bay retriever
'210': German short-haired pointer
'211': vizsla, Hungarian pointer
'212': English setter
'213': Irish setter, red setter
'214': Gordon setter
'215': Brittany spaniel
'216': clumber, clumber spaniel
'217': English springer, English springer spaniel
'218': Welsh springer spaniel
'219': cocker spaniel, English cocker spaniel, cocker
'220': Sussex spaniel
'221': Irish water spaniel
'222': kuvasz
'223': schipperke
'224': groenendael
'225': malinois
'226': briard
'227': kelpie
'228': komondor
'229': Old English sheepdog, bobtail
'230': Shetland sheepdog, Shetland sheep dog, Shetland
'231': collie
'232': Border collie
'233': Bouvier des Flandres, Bouviers des Flandres
'234': Rottweiler
'235': German shepherd, German shepherd dog, German police dog, alsatian
'236': Doberman, Doberman pinscher
'237': miniature pinscher
'238': Greater Swiss Mountain dog
'239': Bernese mountain dog
'240': Appenzeller
'241': EntleBucher
'242': boxer
'243': bull mastiff
'244': Tibetan mastiff
'245': French bulldog
'246': Great Dane
'247': Saint Bernard, St Bernard
'248': Eskimo dog, husky
'249': malamute, malemute, Alaskan malamute
'250': Siberian husky
'251': dalmatian, coach dog, carriage dog
'252': affenpinscher, monkey pinscher, monkey dog
'253': basenji
'254': pug, pug-dog
'255': Leonberg
'256': Newfoundland, Newfoundland dog
'257': Great Pyrenees
'258': Samoyed, Samoyede
'259': Pomeranian
'260': chow, chow chow
'261': keeshond
'262': Brabancon griffon
'263': Pembroke, Pembroke Welsh corgi
'264': Cardigan, Cardigan Welsh corgi
'265': toy poodle
'266': miniature poodle
'267': standard poodle
'268': Mexican hairless
'269': timber wolf, grey wolf, gray wolf, Canis lupus
'270': white wolf, Arctic wolf, Canis lupus tundrarum
'271': red wolf, maned wolf, Canis rufus, Canis niger
'272': coyote, prairie wolf, brush wolf, Canis latrans
'273': dingo, warrigal, warragal, Canis dingo
'274': dhole, Cuon alpinus
'275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
'276': hyena, hyaena
'277': red fox, Vulpes vulpes
'278': kit fox, Vulpes macrotis
'279': Arctic fox, white fox, Alopex lagopus
'280': grey fox, gray fox, Urocyon cinereoargenteus
'281': tabby, tabby cat
'282': tiger cat
'283': Persian cat
'284': Siamese cat, Siamese
'285': Egyptian cat
'286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
'287': lynx, catamount
'288': leopard, Panthera pardus
'289': snow leopard, ounce, Panthera uncia
'290': jaguar, panther, Panthera onca, Felis onca
'291': lion, king of beasts, Panthera leo
'292': tiger, Panthera tigris
'293': cheetah, chetah, Acinonyx jubatus
'294': brown bear, bruin, Ursus arctos
'295': American black bear, black bear, Ursus americanus, Euarctos americanus
'296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
'297': sloth bear, Melursus ursinus, Ursus ursinus
'298': mongoose
'299': meerkat, mierkat
'300': tiger beetle
'301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
'302': ground beetle, carabid beetle
'303': long-horned beetle, longicorn, longicorn beetle
'304': leaf beetle, chrysomelid
'305': dung beetle
'306': rhinoceros beetle
'307': weevil
'308': fly
'309': bee
'310': ant, emmet, pismire
'311': grasshopper, hopper
'312': cricket
'313': walking stick, walkingstick, stick insect
'314': cockroach, roach
'315': mantis, mantid
'316': cicada, cicala
'317': leafhopper
'318': lacewing, lacewing fly
'319': dragonfly, darning needle, devil's darning needle, sewing needle,
snake feeder, snake doctor, mosquito hawk, skeeter hawk
'320': damselfly
'321': admiral
'322': ringlet, ringlet butterfly
'323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
'324': cabbage butterfly
'325': sulphur butterfly, sulfur butterfly
'326': lycaenid, lycaenid butterfly
'327': starfish, sea star
'328': sea urchin
'329': sea cucumber, holothurian
'330': wood rabbit, cottontail, cottontail rabbit
'331': hare
'332': Angora, Angora rabbit
'333': hamster
'334': porcupine, hedgehog
'335': fox squirrel, eastern fox squirrel, Sciurus niger
'336': marmot
'337': beaver
'338': guinea pig, Cavia cobaya
'339': sorrel
'340': zebra
'341': hog, pig, grunter, squealer, Sus scrofa
'342': wild boar, boar, Sus scrofa
'343': warthog
'344': hippopotamus, hippo, river horse, Hippopotamus amphibius
'345': ox
'346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
'347': bison
'348': ram, tup
'349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
'350': ibex, Capra ibex
'351': hartebeest
'352': impala, Aepyceros melampus
'353': gazelle
'354': Arabian camel, dromedary, Camelus dromedarius
'355': llama
'356': weasel
'357': mink
'358': polecat, fitch, foulmart, foumart, Mustela putorius
'359': black-footed ferret, ferret, Mustela nigripes
'360': otter
'361': skunk, polecat, wood pussy
'362': badger
'363': armadillo
'364': three-toed sloth, ai, Bradypus tridactylus
'365': orangutan, orang, orangutang, Pongo pygmaeus
'366': gorilla, Gorilla gorilla
'367': chimpanzee, chimp, Pan troglodytes
'368': gibbon, Hylobates lar
'369': siamang, Hylobates syndactylus, Symphalangus syndactylus
'370': guenon, guenon monkey
'371': patas, hussar monkey, Erythrocebus patas
'372': baboon
'373': macaque
'374': langur
'375': colobus, colobus monkey
'376': proboscis monkey, Nasalis larvatus
'377': marmoset
'378': capuchin, ringtail, Cebus capucinus
'379': howler monkey, howler
'380': titi, titi monkey
'381': spider monkey, Ateles geoffroyi
'382': squirrel monkey, Saimiri sciureus
'383': Madagascar cat, ring-tailed lemur, Lemur catta
'384': indri, indris, Indri indri, Indri brevicaudatus
'385': Indian elephant, Elephas maximus
'386': African elephant, Loxodonta africana
'387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
'388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
'389': barracouta, snoek
'390': eel
'391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus
kisutch
'392': rock beauty, Holocanthus tricolor
'393': anemone fish
'394': sturgeon
'395': gar, garfish, garpike, billfish, Lepisosteus osseus
'396': lionfish
'397': puffer, pufferfish, blowfish, globefish
'398': abacus
'399': abaya
'400': academic gown, academic robe, judge's robe
'401': accordion, piano accordion, squeeze box
'402': acoustic guitar
'403': aircraft carrier, carrier, flattop, attack aircraft carrier
'404': airliner
'405': airship, dirigible
'406': altar
'407': ambulance
'408': amphibian, amphibious vehicle
'409': analog clock
'410': apiary, bee house
'411': apron
'412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
'413': assault rifle, assault gun
'414': backpack, back pack, knapsack, packsack, rucksack, haversack
'415': bakery, bakeshop, bakehouse
'416': balance beam, beam
'417': balloon
'418': ballpoint, ballpoint pen, ballpen, Biro
'419': Band Aid
'420': banjo
'421': bannister, banister, balustrade, balusters, handrail
'422': barbell
'423': barber chair
'424': barbershop
'425': barn
'426': barometer
'427': barrel, cask
'428': barrow, garden cart, lawn cart, wheelbarrow
'429': baseball
'430': basketball
'431': bassinet
'432': bassoon
'433': bathing cap, swimming cap
'434': bath towel
'435': bathtub, bathing tub, bath, tub
'436': beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
'437': beacon, lighthouse, beacon light, pharos
'438': beaker
'439': bearskin, busby, shako
'440': beer bottle
'441': beer glass
'442': bell cote, bell cot
'443': bib
'444': bicycle-built-for-two, tandem bicycle, tandem
'445': bikini, two-piece
'446': binder, ring-binder
'447': binoculars, field glasses, opera glasses
'448': birdhouse
'449': boathouse
'450': bobsled, bobsleigh, bob
'451': bolo tie, bolo, bola tie, bola
'452': bonnet, poke bonnet
'453': bookcase
'454': bookshop, bookstore, bookstall
'455': bottlecap
'456': bow
'457': bow tie, bow-tie, bowtie
'458': brass, memorial tablet, plaque
'459': brassiere, bra, bandeau
'460': breakwater, groin, groyne, mole, bulwark, seawall, jetty
'461': breastplate, aegis, egis
'462': broom
'463': bucket, pail
'464': buckle
'465': bulletproof vest
'466': bullet train, bullet
'467': butcher shop, meat market
'468': cab, hack, taxi, taxicab
'469': caldron, cauldron
'470': candle, taper, wax light
'471': cannon
'472': canoe
'473': can opener, tin opener
'474': cardigan
'475': car mirror
'476': carousel, carrousel, merry-go-round, roundabout, whirligig
'477': carpenter's kit, tool kit
'478': carton
'479': car wheel
'480': cash machine, cash dispenser, automated teller machine, automatic
teller machine, automated teller, automatic teller, ATM
'481': cassette
'482': cassette player
'483': castle
'484': catamaran
'485': CD player
'486': cello, violoncello
'487': cellular telephone, cellular phone, cellphone, cell, mobile phone
'488': chain
'489': chainlink fence
'490': chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
'491': chain saw, chainsaw
'492': chest
'493': chiffonier, commode
'494': chime, bell, gong
'495': china cabinet, china closet
'496': Christmas stocking
'497': church, church building
'498': cinema, movie theater, movie theatre, movie house, picture palace
'499': cleaver, meat cleaver, chopper
'500': cliff dwelling
'501': cloak
'502': clog, geta, patten, sabot
'503': cocktail shaker
'504': coffee mug
'505': coffeepot
'506': coil, spiral, volute, whorl, helix
'507': combination lock
'508': computer keyboard, keypad
'509': confectionery, confectionary, candy store
'510': container ship, containership, container vessel
'511': convertible
'512': corkscrew, bottle screw
'513': cornet, horn, trumpet, trump
'514': cowboy boot
'515': cowboy hat, ten-gallon hat
'516': cradle
'517': crane2
'518': crash helmet
'519': crate
'520': crib, cot
'521': Crock Pot
'522': croquet ball
'523': crutch
'524': cuirass
'525': dam, dike, dyke
'526': desk
'527': desktop computer
'528': dial telephone, dial phone
'529': diaper, nappy, napkin
'530': digital clock
'531': digital watch
'532': dining table, board
'533': dishrag, dishcloth
'534': dishwasher, dish washer, dishwashing machine
'535': disk brake, disc brake
'536': dock, dockage, docking facility
'537': dogsled, dog sled, dog sleigh
'538': dome
'539': doormat, welcome mat
'540': drilling platform, offshore rig
'541': drum, membranophone, tympan
'542': drumstick
'543': dumbbell
'544': Dutch oven
'545': electric fan, blower
'546': electric guitar
'547': electric locomotive
'548': entertainment center
'549': envelope
'550': espresso maker
'551': face powder
'552': feather boa, boa
'553': file, file cabinet, filing cabinet
'554': fireboat
'555': fire engine, fire truck
'556': fire screen, fireguard
'557': flagpole, flagstaff
'558': flute, transverse flute
'559': folding chair
'560': football helmet
'561': forklift
'562': fountain
'563': fountain pen
'564': four-poster
'565': freight car
'566': French horn, horn
'567': frying pan, frypan, skillet
'568': fur coat
'569': garbage truck, dustcart
'570': gasmask, respirator, gas helmet
'571': gas pump, gasoline pump, petrol pump, island dispenser
'572': goblet
'573': go-kart
'574': golf ball
'575': golfcart, golf cart
'576': gondola
'577': gong, tam-tam
'578': gown
'579': grand piano, grand
'580': greenhouse, nursery, glasshouse
'581': grille, radiator grille
'582': grocery store, grocery, food market, market
'583': guillotine
'584': hair slide
'585': hair spray
'586': half track
'587': hammer
'588': hamper
'589': hand blower, blow dryer, blow drier, hair dryer, hair drier
'590': hand-held computer, hand-held microcomputer
'591': handkerchief, hankie, hanky, hankey
'592': hard disc, hard disk, fixed disk
'593': harmonica, mouth organ, harp, mouth harp
'594': harp
'595': harvester, reaper
'596': hatchet
'597': holster
'598': home theater, home theatre
'599': honeycomb
'600': hook, claw
'601': hoopskirt, crinoline
'602': horizontal bar, high bar
'603': horse cart, horse-cart
'604': hourglass
'605': iPod
'606': iron, smoothing iron
'607': jack-o'-lantern
'608': jean, blue jean, denim
'609': jeep, landrover
'610': jersey, T-shirt, tee shirt
'611': jigsaw puzzle
'612': jinrikisha, ricksha, rickshaw
'613': joystick
'614': kimono
'615': knee pad
'616': knot
'617': lab coat, laboratory coat
'618': ladle
'619': lampshade, lamp shade
'620': laptop, laptop computer
'621': lawn mower, mower
'622': lens cap, lens cover
'623': letter opener, paper knife, paperknife
'624': library
'625': lifeboat
'626': lighter, light, igniter, ignitor
'627': limousine, limo
'628': liner, ocean liner
'629': lipstick, lip rouge
'630': Loafer
'631': lotion
'632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
'633': loupe, jeweler's loupe
'634': lumbermill, sawmill
'635': magnetic compass
'636': mailbag, postbag
'637': mailbox, letter box
'638': maillot
'639': maillot, tank suit
'640': manhole cover
'641': maraca
'642': marimba, xylophone
'643': mask
'644': matchstick
'645': maypole
'646': maze, labyrinth
'647': measuring cup
'648': medicine chest, medicine cabinet
'649': megalith, megalithic structure
'650': microphone, mike
'651': microwave, microwave oven
'652': military uniform
'653': milk can
'654': minibus
'655': miniskirt, mini
'656': minivan
'657': missile
'658': mitten
'659': mixing bowl
'660': mobile home, manufactured home
'661': Model T
'662': modem
'663': monastery
'664': monitor
'665': moped
'666': mortar
'667': mortarboard
'668': mosque
'669': mosquito net
'670': motor scooter, scooter
'671': mountain bike, all-terrain bike, off-roader
'672': mountain tent
'673': mouse, computer mouse
'674': mousetrap
'675': moving van
'676': muzzle
'677': nail
'678': neck brace
'679': necklace
'680': nipple
'681': notebook, notebook computer
'682': obelisk
'683': oboe, hautboy, hautbois
'684': ocarina, sweet potato
'685': odometer, hodometer, mileometer, milometer
'686': oil filter
'687': organ, pipe organ
'688': oscilloscope, scope, cathode-ray oscilloscope, CRO
'689': overskirt
'690': oxcart
'691': oxygen mask
'692': packet
'693': paddle, boat paddle
'694': paddlewheel, paddle wheel
'695': padlock
'696': paintbrush
'697': pajama, pyjama, pj's, jammies
'698': palace
'699': panpipe, pandean pipe, syrinx
'700': paper towel
'701': parachute, chute
'702': parallel bars, bars
'703': park bench
'704': parking meter
'705': passenger car, coach, carriage
'706': patio, terrace
'707': pay-phone, pay-station
'708': pedestal, plinth, footstall
'709': pencil box, pencil case
'710': pencil sharpener
'711': perfume, essence
'712': Petri dish
'713': photocopier
'714': pick, plectrum, plectron
'715': pickelhaube
'716': picket fence, paling
'717': pickup, pickup truck
'718': pier
'719': piggy bank, penny bank
'720': pill bottle
'721': pillow
'722': ping-pong ball
'723': pinwheel
'724': pirate, pirate ship
'725': pitcher, ewer
'726': plane, carpenter's plane, woodworking plane
'727': planetarium
'728': plastic bag
'729': plate rack
'730': plow, plough
'731': plunger, plumber's helper
'732': Polaroid camera, Polaroid Land camera
'733': pole
'734': police van, police wagon, paddy wagon, patrol wagon, wagon, black
Maria
'735': poncho
'736': pool table, billiard table, snooker table
'737': pop bottle, soda bottle
'738': pot, flowerpot
'739': potter's wheel
'740': power drill
'741': prayer rug, prayer mat
'742': printer
'743': prison, prison house
'744': projectile, missile
'745': projector
'746': puck, hockey puck
'747': punching bag, punch bag, punching ball, punchball
'748': purse
'749': quill, quill pen
'750': quilt, comforter, comfort, puff
'751': racer, race car, racing car
'752': racket, racquet
'753': radiator
'754': radio, wireless
'755': radio telescope, radio reflector
'756': rain barrel
'757': recreational vehicle, RV, R.V.
'758': reel
'759': reflex camera
'760': refrigerator, icebox
'761': remote control, remote
'762': restaurant, eating house, eating place, eatery
'763': revolver, six-gun, six-shooter
'764': rifle
'765': rocking chair, rocker
'766': rotisserie
'767': rubber eraser, rubber, pencil eraser
'768': rugby ball
'769': rule, ruler
'770': running shoe
'771': safe
'772': safety pin
'773': saltshaker, salt shaker
'774': sandal
'775': sarong
'776': sax, saxophone
'777': scabbard
'778': scale, weighing machine
'779': school bus
'780': schooner
'781': scoreboard
'782': screen, CRT screen
'783': screw
'784': screwdriver
'785': seat belt, seatbelt
'786': sewing machine
'787': shield, buckler
'788': shoe shop, shoe-shop, shoe store
'789': shoji
'790': shopping basket
'791': shopping cart
'792': shovel
'793': shower cap
'794': shower curtain
'795': ski
'796': ski mask
'797': sleeping bag
'798': slide rule, slipstick
'799': sliding door
'800': slot, one-armed bandit
'801': snorkel
'802': snowmobile
'803': snowplow, snowplough
'804': soap dispenser
'805': soccer ball
'806': sock
'807': solar dish, solar collector, solar furnace
'808': sombrero
'809': soup bowl
'810': space bar
'811': space heater
'812': space shuttle
'813': spatula
'814': speedboat
'815': spider web, spider's web
'816': spindle
'817': sports car, sport car
'818': spotlight, spot
'819': stage
'820': steam locomotive
'821': steel arch bridge
'822': steel drum
'823': stethoscope
'824': stole
'825': stone wall
'826': stopwatch, stop watch
'827': stove
'828': strainer
'829': streetcar, tram, tramcar, trolley, trolley car
'830': stretcher
'831': studio couch, day bed
'832': stupa, tope
'833': submarine, pigboat, sub, U-boat
'834': suit, suit of clothes
'835': sundial
'836': sunglass
'837': sunglasses, dark glasses, shades
'838': sunscreen, sunblock, sun blocker
'839': suspension bridge
'840': swab, swob, mop
'841': sweatshirt
'842': swimming trunks, bathing trunks
'843': swing
'844': switch, electric switch, electrical switch
'845': syringe
'846': table lamp
'847': tank, army tank, armored combat vehicle, armoured combat vehicle
'848': tape player
'849': teapot
'850': teddy, teddy bear
'851': television, television system
'852': tennis ball
'853': thatch, thatched roof
'854': theater curtain, theatre curtain
'855': thimble
'856': thresher, thrasher, threshing machine
'857': throne
'858': tile roof
'859': toaster
'860': tobacco shop, tobacconist shop, tobacconist
'861': toilet seat
'862': torch
'863': totem pole
'864': tow truck, tow car, wrecker
'865': toyshop
'866': tractor
'867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
semi
'868': tray
'869': trench coat
'870': tricycle, trike, velocipede
'871': trimaran
'872': tripod
'873': triumphal arch
'874': trolleybus, trolley coach, trackless trolley
'875': trombone
'876': tub, vat
'877': turnstile
'878': typewriter keyboard
'879': umbrella
'880': unicycle, monocycle
'881': upright, upright piano
'882': vacuum, vacuum cleaner
'883': vase
'884': vault
'885': velvet
'886': vending machine
'887': vestment
'888': viaduct
'889': violin, fiddle
'890': volleyball
'891': waffle iron
'892': wall clock
'893': wallet, billfold, notecase, pocketbook
'894': wardrobe, closet, press
'895': warplane, military plane
'896': washbasin, handbasin, washbowl, lavabo, wash-hand basin
'897': washer, automatic washer, washing machine
'898': water bottle
'899': water jug
'900': water tower
'901': whiskey jug
'902': whistle
'903': wig
'904': window screen
'905': window shade
'906': Windsor tie
'907': wine bottle
'908': wing
'909': wok
'910': wooden spoon
'911': wool, woolen, woollen
'912': worm fence, snake fence, snake-rail fence, Virginia fence
'913': wreck
'914': yawl
'915': yurt
'916': web site, website, internet site, site
'917': comic book
'918': crossword puzzle, crossword
'919': street sign
'920': traffic light, traffic signal, stoplight
'921': book jacket, dust cover, dust jacket, dust wrapper
'922': menu
'923': plate
'924': guacamole
'925': consomme
'926': hot pot, hotpot
'927': trifle
'928': ice cream, icecream
'929': ice lolly, lolly, lollipop, popsicle
'930': French loaf
'931': bagel, beigel
'932': pretzel
'933': cheeseburger
'934': hotdog, hot dog, red hot
'935': mashed potato
'936': head cabbage
'937': broccoli
'938': cauliflower
'939': zucchini, courgette
'940': spaghetti squash
'941': acorn squash
'942': butternut squash
'943': cucumber, cuke
'944': artichoke, globe artichoke
'945': bell pepper
'946': cardoon
'947': mushroom
'948': Granny Smith
'949': strawberry
'950': orange
'951': lemon
'952': fig
'953': pineapple, ananas
'954': banana
'955': jackfruit, jak, jack
'956': custard apple
'957': pomegranate
'958': hay
'959': carbonara
'960': chocolate sauce, chocolate syrup
'961': dough
'962': meat loaf, meatloaf
'963': pizza, pizza pie
'964': potpie
'965': burrito
'966': red wine
'967': espresso
'968': cup
'969': eggnog
'970': alp
'971': bubble
'972': cliff, drop, drop-off
'973': coral reef
'974': geyser
'975': lakeside, lakeshore
'976': promontory, headland, head, foreland
'977': sandbar, sand bar
'978': seashore, coast, seacoast, sea-coast
'979': valley, vale
'980': volcano
'981': ballplayer, baseball player
'982': groom, bridegroom
'983': scuba diver
'984': rapeseed
'985': daisy
'986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
Cypripedium parviflorum
'987': corn
'988': acorn
'989': hip, rose hip, rosehip
'990': buckeye, horse chestnut, conker
'991': coral fungus
'992': agaric
'993': gyromitra
'994': stinkhorn, carrion fungus
'995': earthstar
'996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola
frondosa
'997': bolete
'998': ear, spike, capitulum
'999': toilet tissue, toilet paper, bathroom tissue
splits:
- name: train
num_bytes: 11957097.0
num_examples: 100
download_size: 11936960
dataset_size: 11957097.0
---
# Dataset Card for "imagenet-1k_mini_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wentingzhao/anthropic-hh-first-prompt | wentingzhao | 2023-09-25T15:25:22Z | 175 | 0 | null | [
"region:us"
] | 2023-09-25T15:25:22Z | 2023-09-25T15:25:21.000Z | 2023-09-25T15:25:21 | ---
dataset_info:
features:
- name: user
dtype: string
- name: system
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 931647
num_examples: 8552
download_size: 472764
dataset_size: 931647
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "anthropic-hh-first-prompt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
justinphan3110/harmful_harmless_instructions | justinphan3110 | 2023-11-07T23:53:44Z | 175 | 0 | null | [
"region:us"
] | 2023-11-07T23:53:44Z | 2023-11-07T23:53:41.000Z | 2023-11-07T23:53:41 | ---
dataset_info:
features:
- name: sentence
sequence: string
- name: label
sequence: bool
splits:
- name: train
num_bytes: 20636
num_examples: 128
- name: test
num_bytes: 62788
num_examples: 384
download_size: 49815
dataset_size: 83424
---
# Dataset Card for "harmful_harmless_instructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
keylazy/ark-atk | keylazy | 2023-11-19T08:10:25Z | 175 | 0 | null | [
"region:us"
] | 2023-11-19T08:10:25Z | 2023-11-19T07:57:31.000Z | 2023-11-19T07:57:31 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text1
dtype: string
- name: text2
dtype: string
- name: tgt_input_ids
sequence: int64
- name: tgt_attention_mask
sequence: int64
- name: lm_input_ids
sequence: int64
- name: lm_attention_mask
sequence: int64
- name: atk_input_ids
sequence: int64
- name: atk_attention_mask
sequence: int64
- name: atk_token_type_ids
sequence: int64
splits:
- name: train
num_bytes: 7470489671
num_examples: 1000000
download_size: 557393595
dataset_size: 7470489671
---
# Dataset Card for "ark-atk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
zetavg/ShareGPT-Processed | zetavg | 2023-05-21T03:50:14Z | 174 | 25 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"language:es",
"language:ja",
"language:fr",
"license:cc0-1.0",
"conversation",
"rlhf",
"chatgpt",
"gpt-3.5",
"region:us"
] | 2023-05-21T03:50:14Z | 2023-05-16T19:50:04.000Z | 2023-05-16T19:50:04 | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: markdown
dtype: string
- name: opencc_converted_markdown
dtype: string
- name: value
dtype: string
- name: lang
dtype: string
splits:
- name: train
num_bytes: 2772036550
num_examples: 90665
download_size: 1075261393
dataset_size: 2772036550
license: cc0-1.0
task_categories:
- text-generation
language:
- en
- zh
- es
- ja
- fr
tags:
- conversation
- rlhf
- chatgpt
- gpt-3.5
size_categories:
- 10K<n<100K
---
# ShareGPT-Processed
The [RyokoAI/ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K) dataset, converted to Markdown and labeled with the language used.
## Acknowledgements
* [vinta/pangu.js](https://github.com/vinta/pangu.js) — To insert whitespace between CJK (Chinese, Japanese, Korean) and half-width characters (alphabetical letters, numerical digits and symbols).
* [matthewwithanm/python-markdownify](https://github.com/matthewwithanm/python-markdownify) — Provides a starting point to convert HTML to Markdown.
* [BYVoid/OpenCC](https://github.com/BYVoid/OpenCC) — Conversions between Traditional Chinese and Simplified Chinese.
* [aboSamoor/polyglot](https://github.com/aboSamoor/polyglot) — For language detection.
* [RyokoAI/ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K) — The data source.
## Data Structure
Every row in this dataset is a conversation session, consisting of:
* `id` — An ID.
* `lang` — The language code of the conversation, detected with `polyglot`.
* `conversations` — A list of messages, with each message consisting of:
* `from` — `human` or `gpt`.
* `value` — The original message content. HTML.
* `markdown` — The message converted into Markdown format.
* `opencc_converted_markdown` — The Markdown-format message converted into Traditional Chinese using `opencc`. Will be `null` for non-Chinese conversations.
Example:
```json
{
"id": "AZirHBD",
"conversations": [
{
"from": "human",
"markdown": "請問英文過去完成式的用法？",
"opencc_converted_markdown": "請問英文過去完成式的用法？",
"value": "請問英文過去完成式的用法？"
},
{
"from": "gpt",
"markdown": "英文的過去完成式是用來表示一個已經發生的動作或事件，發生在過去的某個時間，並且在這個時間之前，也發生了另一個動作或事件。在語法上，過去完成式是由助動詞 \"had\" 和過去分詞形式的動詞組成。下面是一些常見的用法：\n\n1. 表示一個已經完成的動作在另一個過去的時間點之前發生。\n\n * e.g. By the time I arrived, she had already left.\n2. 表示在過去某個時間之前就已經完成的動作。\n\n * e.g. I had studied English for five years before I moved to the United States.\n3. 表示兩個已經完成的動作中，其中一個發生在另一個之前。\n\n * e.g. After I had finished my homework, I watched TV.\n4. 用來描述一個條件或假設在過去的時間點之前已經發生。\n\n * e.g. If she had known about the party, she would have come.\n5. 用來描述過去已經完成的行動對現在的影響。\n\n * e.g. He had worked as a lawyer for many years, so he knew a lot about the law.\n\n在使用過去完成式時，需要注意時態的一致性和上下文的清晰性，以確保表達的意思清晰明確。",
"opencc_converted_markdown": "英文的過去完成式是用來表示一個已經發生的動作或事件，發生在過去的某個時間，並且在這個時間之前，也發生了另一個動作或事件。在語法上，過去完成式是由助動詞 \"had\" 和過去分詞形式的動片語成。下面是一些常見的用法：\n\n1. 表示一個已經完成的動作在另一個過去的時間點之前發生。\n\n * e.g. By the time I arrived, she had already left.\n2. 表示在過去某個時間之前就已經完成的動作。\n\n * e.g. I had studied English for five years before I moved to the United States.\n3. 表示兩個已經完成的動作中，其中一個發生在另一個之前。\n\n * e.g. After I had finished my homework, I watched TV.\n4. 用來描述一個條件或假設在過去的時間點之前已經發生。\n\n * e.g. If she had known about the party, she would have come.\n5. 用來描述過去已經完成的行動對現在的影響。\n\n * e.g. He had worked as a lawyer for many years, so he knew a lot about the law.\n\n在使用過去完成式時，需要注意時態的一致性和上下文的清晰性，以確保表達的意思清晰明確。",
"value": "<div class=\"markdown prose w-full break-words dark:prose-invert light\"><p>英文的過去完成式是用來表示一個已經發生的動作或事件，發生在過去的某個時間，並且在這個時間之前，也發生了另一個動作或事件。在語法上，過去完成式是由助動詞 \"had\" 和過去分詞形式的動詞組成。下面是一些常見的用法：</p><ol><li><p>表示一個已經完成的動作在另一個過去的時間點之前發生。</p><ul><li>e.g. By the time I arrived, she had already left.</li></ul></li><li><p>表示在過去某個時間之前就已經完成的動作。</p><ul><li>e.g. I had studied English for five years before I moved to the United States.</li></ul></li><li><p>表示兩個已經完成的動作中，其中一個發生在另一個之前。</p><ul><li>e.g. After I had finished my homework, I watched TV.</li></ul></li><li><p>用來描述一個條件或假設在過去的時間點之前已經發生。</p><ul><li>e.g. If she had known about the party, she would have come.</li></ul></li><li><p>用來描述過去已經完成的行動對現在的影響。</p><ul><li>e.g. He had worked as a lawyer for many years, so he knew a lot about the law.</li></ul></li></ol><p>在使用過去完成式時，需要注意時態的一致性和上下文的清晰性，以確保表達的意思清晰明確。</p></div>"
},
{
"from": "human",
"markdown": "英文現在式的用法",
"opencc_converted_markdown": "英文現在式的用法",
"value": "英文現在式的用法"
},
{
"from": "gpt",
"markdown": "英文的現在式表示的是現在正在進行或習慣性發生的動作，也可以表示客觀事實或普遍真理。下面是一些常見的用法：\n\n1. 表示現在正在進行的動作。\n\n * e.g. I am typing on my computer right now.\n2. 表示習慣性的行為或狀態。\n\n * e.g. I usually drink coffee in the morning.\n3. 表示事實、真相或存在的狀態。\n\n * e.g. The earth orbits around the sun.\n4. 用於引述、註解或解釋現在發生的事情。\n\n * e.g. He says he is feeling better today.\n5. 表示未來已經計劃好的事件或行動。\n\n * e.g. I am leaving for Paris tomorrow.\n\n在使用現在式時，需要注意動詞的時態和主語的人稱和數，以確保表達的意思清晰明確。",
"opencc_converted_markdown": "英文的現在式表示的是現在正在進行或習慣性發生的動作，也可以表示客觀事實或普遍真理。下面是一些常見的用法：\n\n1. 表示現在正在進行的動作。\n\n * e.g. I am typing on my computer right now.\n2. 表示習慣性的行為或狀態。\n\n * e.g. I usually drink coffee in the morning.\n3. 表示事實、真相或存在的狀態。\n\n * e.g. The earth orbits around the sun.\n4. 用於引述、註解或解釋現在發生的事情。\n\n * e.g. He says he is feeling better today.\n5. 表示未來已經計劃好的事件或行動。\n\n * e.g. I am leaving for Paris tomorrow.\n\n在使用現在式時，需要注意動詞的時態和主語的人稱和數，以確保表達的意思清晰明確。",
"value": "<div class=\"markdown prose w-full break-words dark:prose-invert light\"><p>英文的現在式表示的是現在正在進行或習慣性發生的動作，也可以表示客觀事實或普遍真理。下面是一些常見的用法：</p><ol><li><p>表示現在正在進行的動作。</p><ul><li>e.g. I am typing on my computer right now.</li></ul></li><li><p>表示習慣性的行為或狀態。</p><ul><li>e.g. I usually drink coffee in the morning.</li></ul></li><li><p>表示事實、真相或存在的狀態。</p><ul><li>e.g. The earth orbits around the sun.</li></ul></li><li><p>用於引述、註解或解釋現在發生的事情。</p><ul><li>e.g. He says he is feeling better today.</li></ul></li><li><p>表示未來已經計劃好的事件或行動。</p><ul><li>e.g. I am leaving for Paris tomorrow.</li></ul></li></ol><p>在使用現在式時，需要注意動詞的時態和主語的人稱和數，以確保表達的意思清晰明確。</p></div>"
}
],
"lang": "zh_Hant"
}
```
## Languages
```json
{
"en": 63940,
"zh_Hant": 3201,
"zh": 6394,
"es": 2080,
"ja": 1525,
"unknown": 4212,
"pt": 778,
"it": 512,
"ko": 2529,
"nl": 195,
"ro": 63,
"fr": 1835,
"vi": 245,
"de": 800,
"cs": 172,
"iw": 123,
"ru": 448,
"id": 369,
"pl": 194,
"no": 65,
"ar": 78,
"tr": 150,
"da": 68,
"sa": 10,
"sv": 87,
"ia": 2,
"fo": 7,
"sq": 4,
"el": 14,
"fa": 22,
"bs": 13,
"rm": 2,
"ms": 45,
"ca": 14,
"hr": 26,
"sk": 23,
"uk": 106,
"th": 33,
"fi": 32,
"tlh": 1,
"hu": 46,
"gl": 21,
"bg": 4,
"sr": 18,
"is": 2,
"ts": 9,
"la": 27,
"sl": 6,
"uz": 5,
"qu": 3,
"ay": 7,
"mi": 3,
"ceb": 1,
"gu": 1,
"oc": 8,
"aa": 2,
"haw": 6,
"xh": 4,
"ny": 4,
"hmn": 2,
"tk": 2,
"sco": 6,
"zzp": 4,
"so": 2,
"mg": 1,
"to": 1,
"mk": 1,
"ha": 2,
"ur": 2,
"nn": 4,
"lv": 2,
"mt": 2,
"gn": 2,
"et": 2,
"ie": 3,
"tl": 3,
"lb": 4,
"bn": 1,
"rw": 4,
"bi": 1,
"ga": 1,
"war": 1,
"sw": 2,
"eo": 2,
"eu": 2,
"lt": 1,
"af": 2,
"ht": 3,
"fj": 1,
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}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
FreedomIntelligence/huatuo26M-testdatasets | FreedomIntelligence | 2023-05-17T03:39:41Z | 174 | 13 | null | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:zh",
"license:apache-2.0",
"medical",
"arxiv:2305.01526",
"region:us"
] | 2023-05-17T03:39:41Z | 2023-05-17T02:31:23.000Z | 2023-05-17T02:31:23 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
tags:
- medical
size_categories:
- 1K<n<10K
---
# Dataset Card for huatuo26M-testdatasets
## Dataset Description
- **Homepage: https://www.huatuogpt.cn/**
- **Repository: https://github.com/FreedomIntelligence/Huatuo-26M**
- **Paper: https://arxiv.org/abs/2305.01526**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
We are pleased to announce the release of our evaluation dataset, a subset of the Huatuo-26M. This dataset contains 6,000 entries that we used for Natural Language Generation (NLG) experimentation in our associated research paper.
We encourage researchers and developers to use this evaluation dataset to gauge the performance of their own models. This is not only a chance to assess the accuracy and relevancy of generated responses but also an opportunity to investigate their model's proficiency in understanding and generating complex medical language.
Note: All the data points have been anonymized to protect patient privacy, and they adhere strictly to data protection and privacy regulations.
## Citation
```
@misc{li2023huatuo26m,
title={Huatuo-26M, a Large-scale Chinese Medical QA Dataset},
author={Jianquan Li and Xidong Wang and Xiangbo Wu and Zhiyi Zhang and Xiaolong Xu and Jie Fu and Prayag Tiwari and Xiang Wan and Benyou Wang},
year={2023},
eprint={2305.01526},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GATE-engine/omniglot | GATE-engine | 2023-06-05T18:58:27Z | 174 | 0 | null | [
"region:us"
] | 2023-06-05T18:58:27Z | 2023-06-05T18:13:32.000Z | 2023-06-05T18:13:32 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: full
num_bytes: 11924141.5
num_examples: 32460
download_size: 10520482
dataset_size: 11924141.5
---
# Dataset Card for "omniglot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4 | open-llm-leaderboard | 2023-09-28T15:50:12Z | 174 | 0 | null | [
"region:us"
] | 2023-09-28T15:50:12Z | 2023-08-18T00:10:03.000Z | 2023-08-18T00:10:03 | ---
pretty_name: Evaluation run of Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4](https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-28T15:50:00.560199](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4/blob/main/results_2023-09-28T15-50-00.560199.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.018246644295302015,\n\
\ \"em_stderr\": 0.0013706682452812888,\n \"f1\": 0.0714765100671141,\n\
\ \"f1_stderr\": 0.0018411955158404013,\n \"acc\": 0.32543219642729987,\n\
\ \"acc_stderr\": 0.007862138879264232\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.018246644295302015,\n \"em_stderr\": 0.0013706682452812888,\n\
\ \"f1\": 0.0714765100671141,\n \"f1_stderr\": 0.0018411955158404013\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \
\ \"acc_stderr\": 0.0022675371022545044\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6440410418310971,\n \"acc_stderr\": 0.013456740656273959\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_28T15_50_00.560199
path:
- '**/details_harness|drop|3_2023-09-28T15-50-00.560199.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-28T15-50-00.560199.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_28T15_50_00.560199
path:
- '**/details_harness|gsm8k|5_2023-09-28T15-50-00.560199.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-28T15-50-00.560199.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_28T15_50_00.560199
path:
- '**/details_harness|winogrande|5_2023-09-28T15-50-00.560199.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-28T15-50-00.560199.parquet'
- config_name: results
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- results_2023-07-19T14:47:41.742069.parquet
- split: 2023_09_28T15_50_00.560199
path:
- results_2023-09-28T15-50-00.560199.parquet
- split: latest
path:
- results_2023-09-28T15-50-00.560199.parquet
---
# Dataset Card for Evaluation run of Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4](https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-28T15:50:00.560199](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4/blob/main/results_2023-09-28T15-50-00.560199.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.018246644295302015,
"em_stderr": 0.0013706682452812888,
"f1": 0.0714765100671141,
"f1_stderr": 0.0018411955158404013,
"acc": 0.32543219642729987,
"acc_stderr": 0.007862138879264232
},
"harness|drop|3": {
"em": 0.018246644295302015,
"em_stderr": 0.0013706682452812888,
"f1": 0.0714765100671141,
"f1_stderr": 0.0018411955158404013
},
"harness|gsm8k|5": {
"acc": 0.006823351023502654,
"acc_stderr": 0.0022675371022545044
},
"harness|winogrande|5": {
"acc": 0.6440410418310971,
"acc_stderr": 0.013456740656273959
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alfredplpl/simple-zundamon | alfredplpl | 2023-10-21T16:10:17Z | 174 | 1 | null | [
"language:ja",
"license:other",
"region:us"
] | 2023-10-21T16:10:17Z | 2023-10-21T15:16:58.000Z | 2023-10-21T15:16:58 | ---
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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anab/copa-sse | anab | 2022-10-26T01:53:17Z | 173 | 3 | null | [
"task_categories:text2text-generation",
"task_categories:multiple-choice",
"task_ids:explanation-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"commonsense reasoning",
"explanation",
"graph-based reasoning",
"arxiv:2201.06777",
"region:us"
] | 2022-10-26T01:53:17Z | 2022-10-25T07:11:33.000Z | 2022-10-25T07:11:33 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- mit
multilinguality:
- monolingual
pretty_name: Semi-structured Explanations for Commonsense Reasoning
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- commonsense reasoning
- explanation
- graph-based reasoning
task_categories:
- text2text-generation
- multiple-choice
task_ids:
- explanation-generation
---
# Dataset Card for COPA-SSE
## 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://github.com/a-brassard/copa-sse
- **Paper:** [COPA-SSE: Semi-Structured Explanations for Commonsense Reasoning](https://arxiv.org/abs/2201.06777)
- **Point of Contact:** [Ana Brassard](mailto:ana.brassard@riken.jp)
### Dataset Summary
COPA-SSE contains crowdsourced explanations for the [Balanced COPA](https://balanced-copa.github.io/) dataset, a variant of the [Choice of Plausible Alternatives (COPA)](https://people.ict.usc.edu/~gordon/copa.html) benchmark. The explanations are formatted as a set of triple-like common sense statements with [ConceptNet](https://conceptnet.io/) relations but freely written concepts.
### Supported Tasks and Leaderboards
Can be used to train a model for explain+predict or predict+explain settings. Suited for both text-based and graph-based architectures. Base task is COPA (causal QA).
### Languages
English
## Dataset Structure
### Data Instances
Validation and test set each contains Balanced COPA samples with added explanations in `.jsonl` format. The question ids match the original questions of the Balanced COPA validation and test sets, respectively.
### Data Fields
Each entry contains:
- the original question (matching format and ids)
- `human-explanations`: a list of explanations each containing:
- `expl-id`: the explanation id
- `text`: the explanation in plain text (full sentences)
- `worker-id`: anonymized worker id (the author of the explanation)
- `worker-avg`: the average score the author got for their explanations
- `all-ratings`: all collected ratings for the explanation
- `filtered-ratings`: ratings excluding those that failed the control
- `triples`: the triple-form explanation (a list of ConceptNet-like triples)
Example entry:
```
id: 1,
asks-for: cause,
most-plausible-alternative: 1,
p: "My body cast a shadow over the grass.",
a1: "The sun was rising.",
a2: "The grass was cut.",
human-explanations: [
{expl-id: f4d9b407-681b-4340-9be1-ac044f1c2230,
text: "Sunrise causes casted shadows.",
worker-id: 3a71407b-9431-49f9-b3ca-1641f7c05f3b,
worker-avg: 3.5832864694635025,
all-ratings: [1, 3, 3, 4, 3],
filtered-ratings: [3, 3, 4, 3],
filtered-avg-rating: 3.25,
triples: [["sunrise", "Causes", "casted shadows"]]
}, ...]
```
### Data Splits
Follows original Balanced COPA split: 1000 dev and 500 test instances. Each instance has up to nine explanations.
## Dataset Creation
### Curation Rationale
The goal was to collect human-written explanations to supplement an existing commonsense reasoning benchmark. The triple-like format was designed to support graph-based models and increase the overall data quality, the latter being notoriously lacking in freely-written crowdsourced text.
### Source Data
#### Initial Data Collection and Normalization
The explanations in COPA-SSE are fully crowdsourced via the Amazon Mechanical Turk platform. Workers entered explanations by providing one or more concept-relation-concept triples. The explanations were then rated by different annotators with one- to five-star ratings. The final dataset contains explanations with a range of quality ratings. Additional collection rounds guaranteed that each sample has at least one explanation rated 3.5 stars or higher.
#### Who are the source language producers?
The original COPA questions (500 dev+500 test) were initially hand-crafted by experts. Similarly, the additional 500 development samples in Balanced COPA were authored by a small team of NLP researchers. Finally, the added explanations and quality ratings in COPA-SSE were collected with the help of Amazon Mechanical Turk workers who passed initial qualification rounds.
### Annotations
#### Annotation process
Workers were shown a Balanced COPA question, its answer, and a short instructional text. Then, they filled in free-form text fields for head and tail concepts and selected the relation from a drop-down menu with a curated selection of ConceptNet relations. Each explanation was rated by five different workers who were shown the same question and answer with five candidate explanations.
#### Who are the annotators?
The workers were restricted to persons located in the U.S. or G.B., with a HIT approval of 98% or more, and 500 or more approved HITs. Their identity and further personal information are not available.
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
Models trained to output similar explanations as those in COPA-SSE may not necessarily provide convincing or faithful explanations. Researchers should carefully evaluate the resulting explanations before considering any real-world applications.
### Discussion of Biases
COPA questions ask for causes or effects of everyday actions or interactions, some of them containing gendered language. Some explanations may reinforce harmful stereotypes if their reasoning is based on biased assumptions. These biases were not verified during collection.
### Other Known Limitations
The data was originally intended to be explanation *graphs*, i.e., hypothetical "ideal" subgraphs of a commonsense knowledge graph. While they can still function as valid natural language explanations, their wording may be at times unnatural to a human and may be better suited for graph-based implementations.
## Additional Information
### Dataset Curators
This work was authored by Ana Brassard, Benjamin Heinzerling, Pride Kavumba, and Kentaro Inui. All are both members of the Riken AIP Natural Language Understanding Team and the Tohoku NLP Lab under Tohoku University.
### Licensing Information
COPA-SSE is released under the [MIT License](https://mit-license.org/).
### Citation Information
```
@InProceedings{copa-sse:LREC2022,
author = {Brassard, Ana and Heinzerling, Benjamin and Kavumba, Pride and Inui, Kentaro},
title = {COPA-SSE: Semi-structured Explanations for Commonsense Reasoning},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {3994--4000},
url = {https://aclanthology.org/2022.lrec-1.425}
}
```
### Contributions
Thanks to [@a-brassard](https://github.com/a-brassard) for adding this dataset. | [
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bigbio/nlm_gene | bigbio | 2023-03-31T02:10:39Z | 173 | 1 | null | [
"multilinguality:monolingual",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2023-03-31T02:10:39Z | 2022-11-13T22:10:56.000Z | 2022-11-13T22:10:56 |
---
language:
- en
bigbio_language:
- English
license: cc0-1.0
multilinguality: monolingual
bigbio_license_shortname: CC0_1p0
pretty_name: NLM-Gene
homepage: https://zenodo.org/record/5089049
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for NLM-Gene
## Dataset Description
- **Homepage:** https://zenodo.org/record/5089049
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
NLM-Gene consists of 550 PubMed articles, from 156 journals, and contains more than 15 thousand unique gene names, corresponding to more than five thousand gene identifiers (NCBI Gene taxonomy). This corpus contains gene annotation data from 28 organisms. The annotated articles contain on average 29 gene names, and 10 gene identifiers per article. These characteristics demonstrate that this article set is an important benchmark dataset to test the accuracy of gene recognition algorithms both on multi-species and ambiguous data. The NLM-Gene corpus will be invaluable for advancing text-mining techniques for gene identification tasks in biomedical text.
## Citation Information
```
@article{islamaj2021nlm,
title = {
NLM-Gene, a richly annotated gold standard dataset for gene entities that
addresses ambiguity and multi-species gene recognition
},
author = {
Islamaj, Rezarta and Wei, Chih-Hsuan and Cissel, David and Miliaras,
Nicholas and Printseva, Olga and Rodionov, Oleg and Sekiya, Keiko and Ward,
Janice and Lu, Zhiyong
},
year = 2021,
journal = {Journal of Biomedical Informatics},
publisher = {Elsevier},
volume = 118,
pages = 103779
}
```
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tomas-gajarsky/cifar10-lt | tomas-gajarsky | 2023-06-24T20:22:30Z | 173 | 1 | cifar-10 | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:cifar10",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-06-24T20:22:30Z | 2023-05-06T11:56:18.000Z | 2023-05-06T11:56:18 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- cifar10
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-10
pretty_name: Cifar10-LT
dataset_info:
features:
- name: img
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': automobile
'2': bird
'3': cat
'4': deer
'5': dog
'6': frog
'7': horse
'8': ship
'9': truck
config_name: cifar10
splits:
- name: train
- name: test
num_bytes: 22772838
num_examples: 10000
download_size: 170498071
---
# Dataset Card for CIFAR-10-LT (Long Tail)
## 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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Paper:** [Paper imbalanced example](https://openaccess.thecvf.com/content_CVPR_2019/papers/Cui_Class-Balanced_Loss_Based_on_Effective_Number_of_Samples_CVPR_2019_paper.pdf)
- **Leaderboard:** [r-10](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-10) [r-100](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-100)
### Dataset Summary
The CIFAR-10-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 10 distinct classes.
The number of samples within each class decreases exponentially with factors of 10 and 100.
The dataset includes 10,000 test images, with 1000 images per class,
and fewer than 50,000 training images.
Each image is assigned one label.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-100).
### Languages
English
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'label': 0
}
```
### Data Fields
- img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- label: 0-9 with the following correspondence
0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck
### Data Splits
| name |train|test|
|----------|----:|---------:|
|cifar10|<50000| 10000|
### Licensing Information
Apache License 2.0
### Citation Information
```
@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchablani) and all contributors for adding the original balanced cifar10 dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cawoylel/FulaNewsTextCorporaTTS | cawoylel | 2023-11-07T16:38:40Z | 173 | 1 | null | [
"region:us"
] | 2023-11-07T16:38:40Z | 2023-11-07T14:57:46.000Z | 2023-11-07T14:57:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: dialect
dtype: string
splits:
- name: train
num_bytes: 101260362980.12
num_examples: 142447
download_size: 35094241210
dataset_size: 101260362980.12
---
# Dataset Card for "FulaNewsTextCorporaTTS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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lasha-nlp/CONDAQA | lasha-nlp | 2022-11-08T07:04:12Z | 172 | 3 | null | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"negation",
"reading comprehension",
"arxiv:2211.00295",
"region:us"
] | 2022-11-08T07:04:12Z | 2022-11-08T05:41:56.000Z | 2022-11-08T05:41:56 |
---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
- crowdsourced
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: condaqa
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- negation
- reading comprehension
task_categories:
- question-answering
task_ids: []
---
# Dataset Card for CondaQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation
## Dataset Description
- **Repository:** [https://github.com/AbhilashaRavichander/CondaQA](https://github.com/AbhilashaRavichander/CondaQA)
- **Paper:** [https://arxiv.org/abs/2211.00295](https://arxiv.org/abs/2211.00295)
- **Point of Contact:** aravicha@andrew.cmu.edu
## Dataset Summary
Data from the EMNLP 2022 paper by Ravichander et al.: "CondaQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation".
If you use this dataset, we would appreciate you citing our work:
```
@inproceedings{ravichander-et-al-2022-condaqa,
title={CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation},
author={Ravichander, Abhilasha and Gardner, Matt and Marasovi\'{c}, Ana},
proceedings={EMNLP 2022},
year={2022}
}
```
From the paper: "We introduce CondaQA to facilitate the future development of models that can process negation effectively. This is the first English reading comprehension dataset which requires reasoning about the implications of negated statements in paragraphs. We collect paragraphs with diverse negation cues, then have crowdworkers ask questions about the _implications_ of the negated statement in the passage. We also have workers make three kinds of edits to the passage---paraphrasing the negated statement, changing the scope of the negation, and reversing the negation---resulting in clusters of question-answer pairs that are difficult for models to answer with spurious shortcuts. CondaQA features 14,182 question-answer pairs with over 200 unique negation cues."
### Supported Tasks and Leaderboards
The task is to answer a question given a Wikipedia passage that includes something being negated. There is no official leaderboard.
### Language
English
## Dataset Structure
### Data Instances
Here's an example instance:
```
{"QuestionID": "q10",
"original cue": "rarely",
"PassageEditID": 0,
"original passage": "Drug possession is the crime of having one or more illegal drugs in one's possession, either for personal use, distribution, sale or otherwise. Illegal drugs fall into different categories and sentences vary depending on the amount, type of drug, circumstances, and jurisdiction. In the U.S., the penalty for illegal drug possession and sale can vary from a small fine to a prison sentence. In some states, marijuana possession is considered to be a petty offense, with the penalty being comparable to that of a speeding violation. In some municipalities, possessing a small quantity of marijuana in one's own home is not punishable at all. Generally, however, drug possession is an arrestable offense, although first-time offenders rarely serve jail time. Federal law makes even possession of \"soft drugs\", such as cannabis, illegal, though some local governments have laws contradicting federal laws.",
"SampleID": 5294,
"label": "YES",
"original sentence": "Generally, however, drug possession is an arrestable offense, although first-time offenders rarely serve jail time.",
"sentence2": "If a drug addict is caught with marijuana, is there a chance he will be jailed?",
"PassageID": 444,
"sentence1": "Drug possession is the crime of having one or more illegal drugs in one's possession, either for personal use, distribution, sale or otherwise. Illegal drugs fall into different categories and sentences vary depending on the amount, type of drug, circumstances, and jurisdiction. In the U.S., the penalty for illegal drug possession and sale can vary from a small fine to a prison sentence. In some states, marijuana possession is considered to be a petty offense, with the penalty being comparable to that of a speeding violation. In some municipalities, possessing a small quantity of marijuana in one's own home is not punishable at all. Generally, however, drug possession is an arrestable offense, although first-time offenders rarely serve jail time. Federal law makes even possession of \"soft drugs\", such as cannabis, illegal, though some local governments have laws contradicting federal laws."
}
```
### Data Fields
* `QuestionID`: unique ID for this question (might be asked for multiple passages)
* `original cue`: Negation cue that was used to select this passage from Wikipedia
* `PassageEditID`: 0 = original passage, 1 = paraphrase-edit passage, 2 = scope-edit passage, 3 = affirmative-edit passage
* `original passage`: Original Wikipedia passage the passage is based on (note that the passage might either be the original Wikipedia passage itself, or an edit based on it)
* `SampleID`: unique ID for this passage-question pair
* `label`: answer
* `original sentence`: Sentence that contains the negated statement
* `sentence2`: question
* `PassageID`: unique ID for the Wikipedia passage
* `sentence1`: passage
### Data Splits
Data splits can be accessed as:
```
from datasets import load_dataset
train_set = load_dataset("condaqa", "train")
dev_set = load_dataset("condaqa", "dev")
test_set = load_dataset("condaqa", "test")
```
## Dataset Creation
Full details are in the paper.
### Curation Rationale
From the paper: "Our goal is to evaluate models on their ability to process the contextual implications of negation. We have the following desiderata for our question-answering dataset:
1. The dataset should include a wide variety of negation cues, not just negative particles.
2. Questions should be targeted towards the _implications_ of a negated statement, rather than the factual content of what was or wasn't negated, to remove common sources of spurious cues in QA datasets (Kaushik and Lipton, 2018; Naik et al., 2018; McCoy et al., 2019).
3. Questions should come in closely-related, contrastive groups, to further reduce the possibility of models' reliance on spurious cues in the data (Gardner et al., 2020). This will result in sets of passages that are similar to each other in terms of the words that they contain, but that may admit different answers to questions.
4. Questions should probe the extent to which models are sensitive to how the negation is expressed. In order to do this, there should be contrasting passages that differ only in their negation cue or its scope."
### Source Data
From the paper: "To construct CondaQA, we first collected passages from a July 2021 version of English Wikipedia that contained negation cues, including single- and multi-word negation phrases, as well as affixal negation."
"We use negation cues from [Morante et al. (2011)](https://aclanthology.org/L12-1077/) and [van Son et al. (2016)](https://aclanthology.org/W16-5007/) as a starting point which we extend."
#### Initial Data Collection and Normalization
We show ten passages to crowdworkers and allow them to choose a passage they would like to work on.
#### Who are the source language producers?
Original passages come from volunteers who contribute to Wikipedia. Passage edits, questions, and answers are produced by crowdworkers.
### Annotations
#### Annotation process
From the paper: "In the first stage of the task, crowdworkers made three types of modifications to the original passage: (1) they paraphrased the negated statement, (2) they modified the scope of the negated statement (while retaining the negation cue), and (3) they undid the negation. In the second stage, we instruct crowdworkers to ask challenging questions about the implications of the negated statement. The crowdworkers then answered the questions they wrote previously for the original and edited passages."
Full details are in the paper.
#### Who are the annotators?
From the paper: "Candidates took a qualification exam which consisted of 12 multiple-choice questions that evaluated comprehension of the instructions. We recruit crowdworkers who answer >70% of the questions correctly for the next stage of the dataset construction task." We use the CrowdAQ platform for the exam and Amazon Mechanical Turk for annotations.
### Personal and Sensitive Information
We expect that such information has already been redacted from Wikipedia.
## Considerations for Using the Data
### Social Impact of Dataset
A model that solves this dataset might be (mis-)represented as an evidence that the model understands the entirety of English language and consequently deployed where it will have immediate and/or downstream impact on stakeholders.
### Discussion of Biases
We are not aware of societal biases that are exhibited in this dataset.
### Other Known Limitations
From the paper: "Though CondaQA currently represents the largest NLU dataset that evaluates a model’s ability to process the implications of negation statements, it is possible to construct a larger dataset, with more examples spanning different answer types. Further CONDAQA is an English dataset, and it would be useful to extend our data collection procedures to build high-quality resources in other languages. Finally, while we attempt to extensively measure and control for artifacts in our dataset, it is possible that our dataset has hidden artifacts that we did not study."
## Additional Information
### Dataset Curators
From the paper: "In order to estimate human performance, and to construct a high-quality evaluation with fewer ambiguous examples, we have five verifiers provide answers for each question in the development and test sets." The first author has been manually checking the annotations throughout the entire data collection process that took ~7 months.
### Licensing Information
license: apache-2.0
### Citation Information
```
@inproceedings{ravichander-et-al-2022-condaqa,
title={CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation},
author={Ravichander, Abhilasha and Gardner, Matt and Marasovi\'{c}, Ana},
proceedings={EMNLP 2022},
year={2022}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ThraggBilly/flickr30k_dataset | ThraggBilly | 2023-05-09T17:31:11Z | 172 | 0 | null | [
"region:us"
] | 2023-05-09T17:31:11Z | 2023-05-09T17:26:42.000Z | 2023-05-09T17:26:42 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4178820473.876
num_examples: 31783
download_size: 4402850196
dataset_size: 4178820473.876
---
# Dataset Card for "test_dataset3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TJUNLP/M3KE | TJUNLP | 2023-06-19T04:07:29Z | 172 | 2 | null | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:multiple-choice",
"size_categories:10K<n<100K",
"language:zh",
"license:apache-2.0",
"arxiv:2305.10263",
"region:us"
] | 2023-06-19T04:07:29Z | 2023-06-16T02:42:59.000Z | 2023-06-16T02:42:59 | ---
license: apache-2.0
task_categories:
- text-classification
- question-answering
- multiple-choice
language:
- zh
size_categories:
- 10K<n<100K
arxiv:
- 2305.10263
---
M3KE, or Massive Multi-Level Multi-Subject Knowledge Evaluation, is a benchmark developed to assess the knowledge acquired by large Chinese language models by evaluating their multitask accuracy in both zero- and few-shot settings. The benchmark comprises 20,477 questions spanning 71 tasks. For further information about M3KE, please consult our [paper](https://arxiv.org/abs/2305.10263) or visit our [GitHub](https://github.com/tjunlp-lab/M3KE) page.
## Load the data
```python
from datasets import load_dataset
ds = load_dataset(
path="TJUNLP/M3KE",
name="Computer Programming Language-Natural Sciences-Other"
)
print(ds)
"""
DatasetDict({
test: Dataset({
features: ['id', 'question', 'A', 'B', 'C', 'D', 'answer'],
num_rows: 236
})
dev: Dataset({
features: ['id', 'question', 'A', 'B', 'C', 'D', 'answer'],
num_rows: 5
})
})
"""
print(ds["test"][0])
"""
{'id': 0, 'question': '下面判断正确的是？', 'A': 'char str[10]={"china"}; 等价于 char str[10];str[]="china";', 'B': 'char *s="china"; 等价于 char *s;s="china"; ', 'C': 'char *a="china"; 等价于 char *a;*a="china";', 'D': 'char c[6]="china",d[6]="china"; 等 价 于 char c[6]=d[6]="china"; ', 'answer': ''}
"""
```
```
@misc{liu2023m3ke,
title={M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models},
author={Chuang Liu and Renren Jin and Yuqi Ren and Linhao Yu and Tianyu Dong and Xiaohan Peng and Shuting Zhang and Jianxiang Peng and Peiyi Zhang and Qingqing Lyu and Xiaowen Su and Qun Liu and Deyi Xiong},
year={2023},
eprint={2305.10263},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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paoloitaliani/privacyqa | paoloitaliani | 2023-10-19T13:03:41Z | 172 | 0 | null | [
"region:us"
] | 2023-10-19T13:03:41Z | 2023-10-17T08:55:48.000Z | 2023-10-17T08:55:48 | Entry not found | [
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sunjun/mimic_data | sunjun | 2023-11-22T12:21:38Z | 172 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
shailja/Verilog_GitHub | shailja | 2023-09-20T17:14:18Z | 171 | 7 | null | [
"license:mit",
"arxiv:2212.11140",
"region:us"
] | 2023-09-20T17:14:18Z | 2022-12-19T15:19:55.000Z | 2022-12-19T15:19:55 | ---
license: mit
---
---
pipeline_tag: text-generation
tags:
- code
model-index:
- name: VeriGen
results:
- task:
type: text-generation
dataset:
type:
name:
extra_gated_prompt: >-
## Model License Agreement
Please read the BigCode [OpenRAIL-M
license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
agreement before accepting it.
extra_gated_fields:
I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox
---
# VeriGen
## Table of Contents
1. [Dataset Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [License](##license)
5. [Citation](##citation)
## Dataset Summary
- The dataset comprises Verilog modules as entries. The entries were retrieved from the GitHub dataset on BigQuery.
- For training [models (https://huggingface.co/shailja/fine-tuned-codegen-2B-Verilog)], we filtered entries with no of characters exceeding 20000 and duplicates (exact duplicates ignoring whitespaces).
- **Paper:** [ Benchmarking Large Language Models for Automated Verilog RTL Code Generation](https://arxiv.org/abs/2212.11140)
- **Point of Contact:** [contact@shailja](mailto:shailja.thakur90@gmail.com)
- **Languages:** Verilog (Hardware Description Language)
### Data Splits
The dataset only contains a train split.
### Use
```python
# pip install datasets
from datasets import load_dataset
ds = load_dataset("shailja/Verilog_GitHub", streaming=True, split="train")
print(next(iter(ds)))
#OUTPUT:
```
### Intended Use
The dataset consists of source code from a range of GitHub repositories. As such, they can potentially include non-compilable, low-quality, and vulnerable code.
### Attribution & Other Requirements
The pretraining dataset of the model was not filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected.
# License
The dataset is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
# Citation
```
@misc{https://doi.org/10.48550/arxiv.2212.11140,
doi = {10.48550/ARXIV.2212.11140},
url = {https://arxiv.org/abs/2212.11140},
author = {Thakur, Shailja and Ahmad, Baleegh and Fan, Zhenxing and Pearce, Hammond and Tan, Benjamin and Karri, Ramesh and Dolan-Gavitt, Brendan and Garg, Siddharth},
title = {Benchmarking Large Language Models for Automated Verilog RTL Code Generation},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` | [
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MohamedRashad/characters_backstories | MohamedRashad | 2023-04-03T06:42:29Z | 171 | 2 | null | [
"task_categories:text-generation",
"language:en",
"license:openrail",
"region:us"
] | 2023-04-03T06:42:29Z | 2023-04-03T05:14:52.000Z | 2023-04-03T05:14:52 | ---
license: openrail
task_categories:
- text-generation
language:
- en
pretty_name: Dungeons & Dragons Characters Backstory
---
This dataset is made from this repo [here](https://github.com/janelleshane/DnD_bios)
and it contains 2322 character bios to be used | [
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zhengyun21/PMC-Patients | zhengyun21 | 2023-11-06T16:32:30Z | 171 | 61 | null | [
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"patient summary",
"medical",
"biology",
"arxiv:2202.13876",
"region:us"
] | 2023-11-06T16:32:30Z | 2023-04-25T13:20:16.000Z | 2023-04-25T13:20:16 | ---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- patient summary
- medical
- biology
size_categories:
- 100K<n<1M
---
# Dataset Card for PMC-Patients
## Dataset Description
- **Homepage:** https://github.com/pmc-patients/pmc-patients
- **Repository:** https://github.com/pmc-patients/pmc-patients
- **Paper:** https://arxiv.org/pdf/2202.13876.pdf
- **Leaderboard:** https://pmc-patients.github.io/
- **Point of Contact:** zhengyun21@mails.tsinghua.edu.cn
### Dataset Summary
**PMC-Patients** is a first-of-its-kind dataset consisting of 167k patient summaries extracted from case reports in PubMed Central (PMC), 3.1M patient-article relevance and 293k patient-patient similarity annotations defined by PubMed citation graph.
### Supported Tasks and Leaderboards
**This is purely the patient summary dataset with relational annotations. For ReCDS benchmark, refer to [this dataset](https://huggingface.co/datasets/zhengyun21/PMC-Patients-ReCDS)**
Based on PMC-Patients, we define two tasks to benchmark Retrieval-based Clinical Decision Support (ReCDS) systems: Patient-to-Article Retrieval (PAR) and Patient-to-Patient Retrieval (PPR).
For details, please refer to [our paper](https://arxiv.org/pdf/2202.13876.pdf) and [leaderboard](https://pmc-patients.github.io/).
### Languages
English (en).
## Dataset Structure
### PMC-Paitents.csv
This file contains all information about patients summaries in PMC-Patients, with the following columns:
- `patient_id`: string. A continuous id of patients, starting from 0.
- `patient_uid`: string. Unique ID for each patient, with format PMID-x, where PMID is the PubMed Identifier of the source article of the patient and x denotes index of the patient in source article.
- `PMID`: string. PMID for source article.
- `file_path`: string. File path of xml file of source article.
- `title`: string. Source article title.
- `patient`: string. Patient summary.
- `age`: list of tuples. Each entry is in format `(value, unit)` where value is a float number and unit is in 'year', 'month', 'week', 'day' and 'hour' indicating age unit. For example, `[[1.0, 'year'], [2.0, 'month']]` indicating the patient is a one-year- and two-month-old infant.
- `gender`: 'M' or 'F'. Male or Female.
- `relevant_articles`: dict. The key is PMID of the relevant articles and the corresponding value is its relevance score (2 or 1 as defined in the ``Methods'' section).
- `similar_patients`: dict. The key is patient_uid of the similar patients and the corresponding value is its similarity score (2 or 1 as defined in the ``Methods'' section).
## Dataset Creation
If you are interested in the collection of PMC-Patients and reproducing our baselines, please refer to [this reporsitory](https://github.com/zhao-zy15/PMC-Patients).
### Citation Information
If you find PMC-Patients helpful in your research, please cite our work by:
```
@misc{zhao2023pmcpatients,
title={PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systems},
author={Zhengyun Zhao and Qiao Jin and Fangyuan Chen and Tuorui Peng and Sheng Yu},
year={2023},
eprint={2202.13876},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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paoloitaliani/ace_attorney | paoloitaliani | 2023-11-22T14:21:19Z | 171 | 0 | null | [
"region:us"
] | 2023-11-22T14:21:19Z | 2023-11-02T15:11:54.000Z | 2023-11-02T15:11:54 | ---
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---
# Dataset Card for "ace_attorney"
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jasmeeetsingh/sql-spider-kaggledbqa-with-context | jasmeeetsingh | 2023-11-07T02:11:19Z | 171 | 0 | null | [
"region:us"
] | 2023-11-07T02:11:19Z | 2023-11-07T02:01:16.000Z | 2023-11-07T02:01:16 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Whispering-GPT/lex-fridman-podcast | Whispering-GPT | 2023-07-13T14:47:17Z | 170 | 3 | null | [
"task_categories:automatic-speech-recognition",
"language:en",
"whisper",
"whispering",
"medium",
"region:us"
] | 2023-07-13T14:47:17Z | 2022-12-12T09:09:49.000Z | 2022-12-12T09:09:49 | ---
language: en
task_categories:
- automatic-speech-recognition
dataset_info:
features:
- name: id
dtype: string
- name: channel
dtype: string
- name: channel_id
dtype: string
- name: title
dtype: string
- name: categories
sequence: string
- name: tags
sequence: string
- name: description
dtype: string
- name: text
dtype: string
- name: segments
list:
- name: start
dtype: float64
- name: end
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 102530760
num_examples: 346
download_size: 57264732
dataset_size: 102530760
tags:
- whisper
- whispering
- medium
---
# Dataset Card for "lexFridmanPodcast-transcript-audio"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Whispering-GPT](https://github.com/matallanas/whisper_gpt_pipeline)
- **Repository:** [whisper_gpt_pipeline](https://github.com/matallanas/whisper_gpt_pipeline)
- **Paper:** [whisper](https://cdn.openai.com/papers/whisper.pdf) and [gpt](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
- **Point of Contact:** [Whispering-GPT organization](https://huggingface.co/Whispering-GPT)
### Dataset Summary
This dataset is created by applying whisper to the videos of the Youtube channel [Lex Fridman Podcast](https://www.youtube.com/watch?v=FhfmGM6hswI&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4&ab_channel=LexFridman). The dataset was created a medium size whisper model.
### Languages
- **Language**: English
## Dataset Structure
The dataset contains all the transcripts plus the audio of the different videos of Lex Fridman Podcast.
### Data Fields
The dataset is composed by:
- **id**: Id of the youtube video.
- **channel**: Name of the channel.
- **channel\_id**: Id of the youtube channel.
- **title**: Title given to the video.
- **categories**: Category of the video.
- **description**: Description added by the author.
- **text**: Whole transcript of the video.
- **segments**: A list with the time and transcription of the video.
- **start**: When started the trancription.
- **end**: When the transcription ends.
- **text**: The text of the transcription.
### Data Splits
- Train split.
## Dataset Creation
### Source Data
The transcriptions are from the videos of [Lex Fridman Podcast](https://www.youtube.com/watch?v=FhfmGM6hswI&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4&ab_channel=LexFridman)
### Contributions
Thanks to [Whispering-GPT](https://huggingface.co/Whispering-GPT) organization for adding this dataset. | [
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kuroneko5943/weibo16 | kuroneko5943 | 2023-01-10T16:01:32Z | 170 | 4 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"weibo",
"sentiment",
"region:us"
] | 2023-01-10T16:01:32Z | 2023-01-10T14:39:35.000Z | 2023-01-10T14:39:35 | ---
annotations_creators:
- machine-generated
language:
- zh
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: weibo16
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- weibo
- sentiment
task_categories:
- text-classification
task_ids:
- sentiment-classification
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jayantdocplix/falcon-small-test-dataset | jayantdocplix | 2023-07-16T08:45:53Z | 170 | 0 | null | [
"region:us"
] | 2023-07-16T08:45:53Z | 2023-07-14T18:00:34.000Z | 2023-07-14T18:00:34 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
CyberHarem/x95_girlsfrontline | CyberHarem | 2023-09-17T16:41:09Z | 170 | 0 | null | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-17T16:41:09Z | 2023-08-02T07:32:18.000Z | 2023-08-02T07:32:18 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of x95_girlsfrontline
This is the dataset of x95_girlsfrontline, containing 22 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 22 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 57 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 22 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 22 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 22 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 22 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 22 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 57 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 57 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 57 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
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JetBrains-Research/commit-chronicle | JetBrains-Research | 2023-10-05T10:50:00Z | 170 | 2 | null | [
"task_categories:text-generation",
"task_categories:summarization",
"size_categories:1M<n<10M",
"language:code",
"language:en",
"license:other",
"code",
"commit_message_generation",
"arxiv:2308.07655",
"region:us"
] | 2023-10-05T10:50:00Z | 2023-08-08T15:54:44.000Z | 2023-08-08T15:54:44 | ---
license: other
language:
- code
- en
task_categories:
- text-generation
- summarization
tags:
- code
- commit_message_generation
pretty_name: CommitChronicle
size_categories:
- 1M<n<10M
dataset_info:
- config_name: default
features:
- name: author
dtype: int64
- name: date
dtype: string
- name: timezone
dtype: int64
- name: hash
dtype: string
- name: message
dtype: string
- name: mods
list:
- name: change_type
dtype: string
- name: old_path
dtype: string
- name: new_path
dtype: string
- name: diff
dtype: string
- name: language
dtype: string
- name: license
dtype: string
- name: repo
dtype: string
- name: original_message
dtype: string
splits:
- name: test
num_bytes: 5760117409
num_examples: 1486267
- name: train
num_bytes: 30084265848
num_examples: 7659458
- name: validation
num_bytes: 5905326070
num_examples: 1554042
download_size: 14168436205
dataset_size: 41749709327
- config_name: subset_cmg
features:
- name: author
dtype: int64
- name: date
dtype: string
- name: timezone
dtype: int64
- name: hash
dtype: string
- name: message
dtype: string
- name: mods
list:
- name: change_type
dtype: string
- name: old_path
dtype: string
- name: new_path
dtype: string
- name: diff
dtype: string
- name: language
dtype: string
- name: license
dtype: string
- name: repo
dtype: string
- name: original_message
dtype: string
splits:
- name: test
num_bytes: 772774959
num_examples: 204336
download_size: 258151047
dataset_size: 772774959
- config_name: subset_llm
features:
- name: author
dtype: int64
- name: date
dtype: string
- name: timezone
dtype: int64
- name: hash
dtype: string
- name: message
dtype: string
- name: mods
list:
- name: change_type
dtype: string
- name: old_path
dtype: string
- name: new_path
dtype: string
- name: diff
dtype: string
- name: language
dtype: string
- name: license
dtype: string
- name: repo
dtype: string
- name: original_message
dtype: string
splits:
- name: test
num_bytes: 15121048
num_examples: 4025
download_size: 5068039
dataset_size: 15121048
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- config_name: subset_cmg
data_files:
- split: test
path: subset_cmg/test-*
- config_name: subset_llm
data_files:
- split: test
path: subset_llm/test-*
---
# 📜 CommitChronicle 🔮
This is the dataset for commit message generation (and/or completion), introduced in the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023.
Its key features:
* *large-scale and multilingual*: contains 10.7M commits from 11.9k GitHub repositories in 20 programming languages;
* *diverse*: avoids restrictive filtering on commit messages or commit diffs structure;
* *suitable for experiments with commit history*: provides metadata about commit authors and dates and uses split-by-project.
## Dataset Creation
> 🔍 For further details, please refer to:
> * **Paper**: [https://arxiv.org/abs/2308.07655](https://arxiv.org/abs/2308.07655)
> * **Repository**: [https://github.com/JetBrains-Research/commit_message_generation](https://github.com/JetBrains-Research/commit_message_generation)
We used [GitHub Search](https://seart-ghs.si.usi.ch/) tool and official GitHub API to select relevant repositories with permissive licenses (Apache, BSD 3-clause, MIT).
On February 9th, 2023, we collected all commits made since 2017 from these repositories via [PyDriller](https://github.com/ishepard/pydriller).
Next, we extensively cleaned the data, including filtering outliers, dropping commits from bot authors, and dropping duplicates. Note: to avoid disclosing personal information, we replaced the commit authors' names and emails with unique identifiers.
## Dataset Structure
### Data Instances
Each data instance in the dataset is a commit. [A commit example](https://github.com/saridormi/commit_chronicle/commit/a7fb3b64184f0af5b08285cce14b9139baa94049) would look like the following:
```
{
'repo': 'saridormi/commit_chronicle',
'hash': 'a7fb3b64184f0af5b08285cce14b9139baa94049',
'author': 123,
'date': '05.07.2021 15:10:07',
'timezone': 0,
'license': 'MIT License',
'language': 'Jupyter Notebook',
'message': 'Add license badge to readme',
'original_message': 'Add license badge to readme',
'mods': [{'change_type': 'MODIFY',
'new_path': 'README.md',
'old_path': 'README.md'
'diff': '@@ -1,6 +1,6 @@\n'
' # Commits dataset\n'
' \n'
'-> :heavy_exclamation_mark: **TODO:** license\n'
'+\n'}],
}
```
### Data Fields
Each example has the following fields:
| **Field** | **Description** |
|:------------------:|:----------------------------------------:|
| `repo` | Commit repository. |
| `hash` | Commit hash. |
| `author` | Unique id for commit author |
| `date` | Commit date (from author). |
| `timezone` | Commit timezone (from author). |
| `license` | Commit repository's license. |
| `language` | Commit repository's main language. |
| `message` | Commit message (after processing). |
| `original_message` | Commit message (without any processing). |
| `mods` | List of file modifications from commit. |
Each file modification has the following fields:
| **Field** | **Description** |
|:-------------:|:-------------------------------------------------------------------------------------------------:|
| `change_type` | Type of change to current file. One of: `ADD`, `COPY`, `RENAME`, `DELETE`, `MODIFY` or `UNKNOWN`. |
| `old_path` | Path to file before change (might be empty). |
| `new_path` | Path to file after change (might be empty). |
| `diff` | `git diff` for current file. |
### Data Splits
We provide the following configurations:
* `default`
* `train`: full training split (7.66M commits)
* `validation`: full validation split (1.55M commits)
* `test`: full test split (1.49M commits)
* `subset_cmg`
* `test`: test subset used for experiments with CMG approaches (204k commits)
* `subset_llm`
* `test`: test subset used for experiments with a LLM (4k commits)
## Considerations for Using the Data
> Adopted from [the Stack](https://huggingface.co/datasets/bigcode/the-stack).
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their open-access research.
Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information.
The dataset is a collection of commits from repositories with various licenses. Any use of all or part of the code gathered in this dataset must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.
## Citation
```
TODO
``` | [
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Team-PIXEL/bigrams_wiki-en_529 | Team-PIXEL | 2023-10-02T09:46:56Z | 170 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-02T09:46:56Z | 2023-10-02T07:29:26.000Z | 2023-10-02T07:29:26 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: title
dtype: string
- name: data
dtype: string
splits:
- name: train
num_bytes: 12552650836
num_examples: 18376812
download_size: 6490731290
dataset_size: 12552650836
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tasksource/babi_nli | tasksource | 2023-06-05T09:05:59Z | 169 | 1 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:bsd",
"logical reasoning",
"nli",
"natural-language-inference",
"reasoning",
"logic",
"region:us"
] | 2023-06-05T09:05:59Z | 2023-01-01T14:39:33.000Z | 2023-01-01T14:39:33 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license: bsd
multilinguality:
- monolingual
pretty_name: babi_nli
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
tags:
- logical reasoning
- nli
- natural-language-inference
- reasoning
- logic
---
# bAbi_nli
bAbI tasks recasted as natural language inference.
https://github.com/facebookarchive/bAbI-tasks
tasksource recasting code:
https://colab.research.google.com/drive/1J_RqDSw9iPxJSBvCJu-VRbjXnrEjKVvr?usp=sharing
```bibtex
@article{weston2015towards,
title={Towards ai-complete question answering: A set of prerequisite toy tasks},
author={Weston, Jason and Bordes, Antoine and Chopra, Sumit and Rush, Alexander M and Van Merri{\"e}nboer, Bart and Joulin, Armand and Mikolov, Tomas},
journal={arXiv preprint arXiv:1502.05698},
year={2015}
}
``` | [
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atokforps/chunk-t1 | atokforps | 2023-03-09T20:48:30Z | 169 | 1 | null | [
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tomas-gajarsky/cifar100-lt | tomas-gajarsky | 2023-06-24T20:25:07Z | 169 | 0 | cifar-100 | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:cifar100",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-06-24T20:25:07Z | 2023-05-05T15:43:58.000Z | 2023-05-05T15:43:58 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- cifar100
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-100
pretty_name: Cifar100-LT
dataset_info:
features:
- name: img
dtype: image
- name: fine_label
dtype:
class_label:
names:
'0': apple
'1': aquarium_fish
'2': baby
'3': bear
'4': beaver
'5': bed
'6': bee
'7': beetle
'8': bicycle
'9': bottle
'10': bowl
'11': boy
'12': bridge
'13': bus
'14': butterfly
'15': camel
'16': can
'17': castle
'18': caterpillar
'19': cattle
'20': chair
'21': chimpanzee
'22': clock
'23': cloud
'24': cockroach
'25': couch
'26': cra
'27': crocodile
'28': cup
'29': dinosaur
'30': dolphin
'31': elephant
'32': flatfish
'33': forest
'34': fox
'35': girl
'36': hamster
'37': house
'38': kangaroo
'39': keyboard
'40': lamp
'41': lawn_mower
'42': leopard
'43': lion
'44': lizard
'45': lobster
'46': man
'47': maple_tree
'48': motorcycle
'49': mountain
'50': mouse
'51': mushroom
'52': oak_tree
'53': orange
'54': orchid
'55': otter
'56': palm_tree
'57': pear
'58': pickup_truck
'59': pine_tree
'60': plain
'61': plate
'62': poppy
'63': porcupine
'64': possum
'65': rabbit
'66': raccoon
'67': ray
'68': road
'69': rocket
'70': rose
'71': sea
'72': seal
'73': shark
'74': shrew
'75': skunk
'76': skyscraper
'77': snail
'78': snake
'79': spider
'80': squirrel
'81': streetcar
'82': sunflower
'83': sweet_pepper
'84': table
'85': tank
'86': telephone
'87': television
'88': tiger
'89': tractor
'90': train
'91': trout
'92': tulip
'93': turtle
'94': wardrobe
'95': whale
'96': willow_tree
'97': wolf
'98': woman
'99': worm
- name: coarse_label
dtype:
class_label:
names:
'0': aquatic_mammals
'1': fish
'2': flowers
'3': food_containers
'4': fruit_and_vegetables
'5': household_electrical_devices
'6': household_furniture
'7': insects
'8': large_carnivores
'9': large_man-made_outdoor_things
'10': large_natural_outdoor_scenes
'11': large_omnivores_and_herbivores
'12': medium_mammals
'13': non-insect_invertebrates
'14': people
'15': reptiles
'16': small_mammals
'17': trees
'18': vehicles_1
'19': vehicles_2
config_name: cifar100
splits:
- name: train
- name: test
num_bytes: 22605519
num_examples: 10000
download_size: 169001437
---
# Dataset Card for CIFAR-100-LT (Long Tail)
## 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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Paper:** [Paper imbalanced example](https://openaccess.thecvf.com/content_CVPR_2019/papers/Cui_Class-Balanced_Loss_Based_on_Effective_Number_of_Samples_CVPR_2019_paper.pdf)
- **Leaderboard:** [r-10](https://paperswithcode.com/sota/long-tail-learning-on-cifar-100-lt-r-10) [r-100](https://paperswithcode.com/sota/long-tail-learning-on-cifar-100-lt-r-100)
### Dataset Summary
The CIFAR-100-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 100 distinct classes.
The number of samples within each class decreases exponentially with factors of 10 and 100.
The dataset includes 10,000 test images, with 100 images per class,
and fewer than 50,000 training images.
These 100 classes are further organized into 20 overarching superclasses.
Each image is assigned two labels: a fine label denoting the specific class,
and a coarse label representing the associated superclass.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/long-tail-learning-on-cifar-100-lt-r-100).
### Languages
English
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19,
'coarse_label': 11
}
```
### Data Fields
- `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `fine_label`: an `int` classification label with the following mapping:
`0`: apple
`1`: aquarium_fish
`2`: baby
`3`: bear
`4`: beaver
`5`: bed
`6`: bee
`7`: beetle
`8`: bicycle
`9`: bottle
`10`: bowl
`11`: boy
`12`: bridge
`13`: bus
`14`: butterfly
`15`: camel
`16`: can
`17`: castle
`18`: caterpillar
`19`: cattle
`20`: chair
`21`: chimpanzee
`22`: clock
`23`: cloud
`24`: cockroach
`25`: couch
`26`: cra
`27`: crocodile
`28`: cup
`29`: dinosaur
`30`: dolphin
`31`: elephant
`32`: flatfish
`33`: forest
`34`: fox
`35`: girl
`36`: hamster
`37`: house
`38`: kangaroo
`39`: keyboard
`40`: lamp
`41`: lawn_mower
`42`: leopard
`43`: lion
`44`: lizard
`45`: lobster
`46`: man
`47`: maple_tree
`48`: motorcycle
`49`: mountain
`50`: mouse
`51`: mushroom
`52`: oak_tree
`53`: orange
`54`: orchid
`55`: otter
`56`: palm_tree
`57`: pear
`58`: pickup_truck
`59`: pine_tree
`60`: plain
`61`: plate
`62`: poppy
`63`: porcupine
`64`: possum
`65`: rabbit
`66`: raccoon
`67`: ray
`68`: road
`69`: rocket
`70`: rose
`71`: sea
`72`: seal
`73`: shark
`74`: shrew
`75`: skunk
`76`: skyscraper
`77`: snail
`78`: snake
`79`: spider
`80`: squirrel
`81`: streetcar
`82`: sunflower
`83`: sweet_pepper
`84`: table
`85`: tank
`86`: telephone
`87`: television
`88`: tiger
`89`: tractor
`90`: train
`91`: trout
`92`: tulip
`93`: turtle
`94`: wardrobe
`95`: whale
`96`: willow_tree
`97`: wolf
`98`: woman
`99`: worm
- `coarse_label`: an `int` coarse classification label with following mapping:
`0`: aquatic_mammals
`1`: fish
`2`: flowers
`3`: food_containers
`4`: fruit_and_vegetables
`5`: household_electrical_devices
`6`: household_furniture
`7`: insects
`8`: large_carnivores
`9`: large_man-made_outdoor_things
`10`: large_natural_outdoor_scenes
`11`: large_omnivores_and_herbivores
`12`: medium_mammals
`13`: non-insect_invertebrates
`14`: people
`15`: reptiles
`16`: small_mammals
`17`: trees
`18`: vehicles_1
`19`: vehicles_2
### Data Splits
| name |train|test|
|----------|----:|---------:|
|cifar100|<50000| 10000|
### Licensing Information
Apache License 2.0
### Citation Information
```
@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchablani) and all contributors for adding the original balanced cifar100 dataset. | [
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WiktorS/polish-news | WiktorS | 2023-06-05T20:57:34Z | 169 | 5 | null | [
"task_categories:text-classification",
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:pl",
"license:apache-2.0",
"region:us"
] | 2023-06-05T20:57:34Z | 2023-05-22T20:14:43.000Z | 2023-05-22T20:14:43 | ---
license: apache-2.0
task_categories:
- text-classification
- summarization
- text-generation
language:
- pl
size_categories:
- 100K<n<1M
---
This dataset contains more than 250k articles obtained from polish news site `tvp.info.pl`.
Main purpouse of collecting the data was to create a transformer-based model for text summarization.
Columns:
* `link` - link to article
* `title` - original title of the article
* `headline` - lead/headline of the article - first paragraph of the article visible directly from the page
* `content` - full textual contents of the article
Link to original repo: https://github.com/WiktorSob/scraper-tvp
Download the data:
```python
from datasets import load_dataset
dataset = load_dataset("WiktorS/polish-news")
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/wsc | SetFit | 2022-06-10T13:59:09Z | 168 | 0 | null | [
"region:us"
] | 2022-06-10T13:59:09Z | 2022-06-10T13:57:36.000Z | 2022-06-10T13:57:36 | # Glue WSC
This dataset is a port of the official [`wsc` dataset](https://huggingface.co/datasets/super_glue) on the Hub.
Also, the test split is not labeled; the label column values are always -1.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
fahamu/ioi | fahamu | 2022-11-28T03:58:50Z | 168 | 2 | null | [
"license:mit",
"doi:10.57967/hf/0142",
"region:us"
] | 2022-11-28T03:58:50Z | 2022-11-28T01:10:59.000Z | 2022-11-28T01:10:59 | ---
license: mit
---
# Dataset Release: Indirect Object Identification
`mecha_ioi` is a pair of datasets tailored for the Indirect Object Identification task, where sentences are generated from the following set of templates:
- BABA
```
baba_templates = [
"Then, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} had a lot of fun at the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} were working at the {PLACE}. {B} decided to give a {OBJECT} to {A}",
"Then, {B} and {A} were thinking about going to the {PLACE}. {B} wanted to give a {OBJECT} to {A}",
"Then, {B} and {A} had a long argument, and afterwards {B} said to {A}",
"After {B} and {A} went to the {PLACE}, {B} gave a {OBJECT} to {A}",
"When {B} and {A} got a {OBJECT} at the {PLACE}, {B} decided to give it to {A}",
"When {B} and {A} got a {OBJECT} at the {PLACE}, {B} decided to give the {OBJECT} to {A}",
"While {B} and {A} were working at the {PLACE}, {B} gave a {OBJECT} to {A}",
"While {B} and {A} were commuting to the {PLACE}, {B} gave a {OBJECT} to {A}",
"After the lunch, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Afterwards, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} had a long argument. Afterwards {B} said to {A}",
"The {PLACE} {B} and {A} went to had a {OBJECT}. {B} gave it to {A}",
"Friends {B} and {A} found a {OBJECT} at the {PLACE}. {B} gave it to {A}",
]
```
- ABBA
```
abba_templates = [
"Then, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} had a lot of fun at the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} were working at the {PLACE}. {B} decided to give a {OBJECT} to {A}",
"Then, {A} and {B} were thinking about going to the {PLACE}. {B} wanted to give a {OBJECT} to {A}",
"Then, {A} and {B} had a long argument, and afterwards {B} said to {A}",
"After {A} and {B} went to the {PLACE}, {B} gave a {OBJECT} to {A}",
"When {A} and {B} got a {OBJECT} at the {PLACE}, {B} decided to give it to {A}",
"When {A} and {B} got a {OBJECT} at the {PLACE}, {B} decided to give the {OBJECT} to {A}",
"While {A} and {B} were working at the {PLACE}, {B} gave a {OBJECT} to {A}",
"While {A} and {B} were commuting to the {PLACE}, {B} gave a {OBJECT} to {A}",
"After the lunch, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Afterwards, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} had a long argument. Afterwards {B} said to {A}",
"The {PLACE} {A} and {B} went to had a {OBJECT}. {B} gave it to {A}",
"Friends {A} and {B} found a {OBJECT} at the {PLACE}. {B} gave it to {A}",
]
```
The purpose of this dataset is to facilitate interpretability research, inspired by the paper
_Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small_, from Redwood Research. We are not affiliated with Redwood Research,
and release this dataset to contribute to the collective research effort behind understanding how Transformer language models perform this task.
### BibTex
```
@misc {fahamu_2022,
author = { {Fahamu} },
title = { ioi (Revision 223da8b) },
year = 2022,
url = { https://huggingface.co/datasets/fahamu/ioi },
doi = { 10.57967/hf/0142 },
publisher = { Hugging Face }
}
``` | [
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umarbutler/open-australian-legal-corpus | umarbutler | 2023-11-14T13:17:49Z | 168 | 28 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-retrieval",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:document-retrieval",
"annotations_creators:no-annotation",
"language_creators:found",
"size_categories:100K<n<1M",
"source_datasets:Federal Register of Legislation",
"source_datasets:Federal Court of Australia",
"source_datasets:High Court of Australia",
"source_datasets:NSW Caselaw",
"source_datasets:NSW Legislation",
"source_datasets:Queensland Legislation",
"source_datasets:Western Australian Legislation",
"source_datasets:South Australian Legislation",
"source_datasets:Tasmanian Legislation",
"language:en",
"license:other",
"law",
"legal",
"australia",
"doi:10.57967/hf/1306",
"region:us"
] | 2023-11-14T13:17:49Z | 2023-06-25T08:53:25.000Z | 2023-06-25T08:53:25 | ---
language:
- en
license: other
license_name: open-australian-legal-corpus
license_link: https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md
tags:
- law
- legal
- australia
annotations_creators:
- no-annotation
language_creators:
- found
language_details: en-AU, en-GB
pretty_name: Open Australian Legal Corpus
size_categories:
- 100K<n<1M
source_datasets:
- Federal Register of Legislation
- Federal Court of Australia
- High Court of Australia
- NSW Caselaw
- NSW Legislation
- Queensland Legislation
- Western Australian Legislation
- South Australian Legislation
- Tasmanian Legislation
task_categories:
- text-generation
- fill-mask
- text-retrieval
task_ids:
- language-modeling
- masked-language-modeling
- document-retrieval
viewer: true
dataset_info:
config_name: train
features:
- name: version_id
dtype: string
- name: type
dtype: string
- name: jurisdiction
dtype: string
- name: source
dtype: string
- name: citation
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 19275851785
num_examples: 228612
download_size: 19354860844
dataset_size: 19275851785
---
<!-- To update the above `dataset_info` section, please run the following command: `datasets-cli test open_australian_legal_corpus.py --save_info --all_configs`. -->
# **Open Australian Legal Corpus ⚖️**
<a href="https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus" alt="Release"><img src="https://img.shields.io/badge/release-v4.2.0-green"></a>
The Open Australian Legal Corpus is the first and only multijurisdictional open corpus of Australian legislative and judicial documents.
Comprised of 228,612 texts totalling over 60 million lines and 1.4 billion tokens, the Corpus includes every in force statute and regulation in the Commonwealth, New South Wales, Queensland, Western Australia, South Australia, Tasmania and Norfolk Island, in addition to thousands of bills and hundreds of thousands of court and tribunal decisions.
As the largest free and open database of its kind to date, the Corpus is intended to progress the burgeoning field of legal AI research in Australia by allowing researchers to pretrain and finetune machine learning models for downstream natural language processing tasks applied to the Australian legal domain such as document classification, summarisation, information retrieval and question answering.
To ensure its accessibility to as wide an audience as possible, the Corpus and all its documents are distributed under open source licences that, in most cases, allow for both non-commercial and commercial usage (see the [Licence 📄](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md)).
Those interested in learning more about the Corpus are encouraged to read Umar Butler's accompanying article, [*How I built the largest open database of Australian law*](https://umarbutler.com/how-i-built-the-largest-open-database-of-australian-law/).
## Usage 👩💻
The below code snippet illustrates how the Corpus may be streamed or loaded with the [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) Python library:
```python
from datasets import load_dataset
corpus = load_dataset('umarbutler/open_australian_legal_corpus', split='train', streaming=True) # Set `streaming` to `False` if you wish to load the entire corpus into memory.
```
To speed up the loading of the Corpus, you may wish to install [`orjson`](https://github.com/ijl/orjson).
## Statistics 📊
The Corpus is comprised of 228,612 documents, totalling 60,446,124 lines and 1,423,441,232 tokens.
A breakdown of the number of documents by type and source is provided below:
| Source | Primary Legislation | Secondary Legislation | Bills | Decisions | **Total** |
|:--------------------------------|----------------------:|------------------------:|--------:|------------:|--------:|
| Federal Register of Legislation | 3,468 | 25,743 | 7,900 | 0 |**37,111**|
| Federal Court of Australia | 0 | 0 | 0 | 61,455 |**61,455**|
| High Court of Australia | 0 | 0 | 0 | 8,659 |**8,659**|
| NSW Caselaw | 0 | 0 | 0 | 110,666 |**110,666**|
| NSW Legislation | 1,429 | 799 | 0 | 0 |**2,228**|
| Queensland Legislation | 564 | 427 | 2,234 | 0 |**3,225**|
| Western Australian Legislation | 813 | 757 | 0 | 0 |**1,570**|
| South Australian Legislation | 555 | 471 | 138 | 0 |**1,164**|
| Tasmanian Legislation | 861 | 1,673 | 0 | 0 |**2,534**|
| **Total** |**7,690**|**29,870**|**10,272**|**180,780**|**228,612**|
## Structure 🗂️
The Corpus is stored in [corpus.jsonl](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/corpus.jsonl), a json lines file where each line represents a document consisting of six keys:
| Key | Description |
| --- | --- |
| version_id | A unique identifier for the current version of the document. |
| type | The type of the document. Possible values are `primary_legislation`, `secondary_legislation`, `bill` and `decision`. |
| jurisdiction | The jurisdiction of the document. Possible values are `commonwealth`, `new_south_wales`, `queensland`, `western_australia`, `south_australia`, `tasmania` and `norfolk_island`. |
| source | The source of the document. Possible values are `federal_register_of_legislation`, `federal_court_of_australia`, `high_court_of_australia`, `nsw_caselaw`, `nsw_legislation`, `queensland_legislation`, `western_australian_legislation`, `south_australian_legislation` and `tasmanian_legislation`. |
| citation | The title of the document with, in the case of legislation and bills, an abbreviated form of the document's jurisdiction enclosed in parentheses appended. |
| url | A hyperlink to the document. |
| text | The UTF-8 encoded text of the document. |
## Collection 📥
Documents were sourced from the [Federal Register of Legislation](https://www.legislation.gov.au/), [Federal Court of Australia](https://www.fedcourt.gov.au/digital-law-library/judgments/search), [High Court of Australia](https://eresources.hcourt.gov.au/), [NSW Caselaw](https://www.caselaw.nsw.gov.au/), [NSW Legislation](https://legislation.nsw.gov.au/), [Queensland Legislation](https://www.legislation.qld.gov.au/), [Western Australian Legislation](https://www.legislation.wa.gov.au/), [South Australian Legislation](https://www.legislation.sa.gov.au/) and [Tasmanian Legislation](https://www.legislation.tas.gov.au/) databases.
[`Inscriptis`](https://github.com/weblyzard/inscriptis) was used to extract the text of documents stored as HTML, [`pdfplumber`](https://github.com/jsvine/pdfplumber) for PDFs, [`striprtf`](https://github.com/joshy/striprtf) for RTFs and finally [`mammoth`](https://github.com/mwilliamson/python-mammoth) was used to convert DOCs and DOCXs to HTML before also extracting their text with `Inscriptis`.
The below table provides the date each source was last updated and the types of documents collected:
| Source | Date | Documents |
| --- | --- | --- |
| [Federal Register of Legislation](https://www.legislation.gov.au/) | 29 October 2023 | <ul><li>The most recent versions of all in force acts and the Constitution (primary legislation);</li> <li>The most recent versions of all in force legislative instruments, notifiable instruments, administrative arrangements orders and prerogative instruments (secondary legislation); and</li> <li>The as made versions of all bills.</li></ul> |
| [Federal Court of Australia](https://www.fedcourt.gov.au/digital-law-library/judgments/search) | 29 October 2023 | <ul><li>All decisions of the Federal Court of Australia, Industrial Relations Court of Australia, Australian Competition Tribunal, Copyright Tribunal, Defence Force Discipline Appeal Tribunal, Federal Police Disciplinary Tribunal, Trade Practices Tribunal and Supreme Court of Norfolk Island.</li></ul> |
| [High Court of Australia](https://eresources.hcourt.gov.au/) | 9 November 2023 | <ul><li>All decisions of the High Court of Australia.</li></ul> |
| [NSW Caselaw](https://www.caselaw.nsw.gov.au/) | 2 November 2023 | <ul><li>All decisions of the NSW Children's Court, Compensation Court, Court of Appeal, Court of Criminal Appeal, District Court, Drug Court, Industrial Relations Commission, Land and Environment Court, Local Court, Supreme Court, Administrative Decisions Tribunal, Civil and Administrative Tribunal, Dust Diseases Tribunal, Equal Opportunity Tribunal, Fair Trading Tribunal, Legal Services Tribunal, Medical Tribunal and Transport Appeals Boards.</li></ul> |
| [NSW Legislation](https://legislation.nsw.gov.au/) | 29 October 2023 | <ul><li>The most recent versions of all in force public and private acts (primary legislation); and</li> <li>The most recent versions of all in force statutory instruments and environmental planning instruments (secondary legislation).</li></ul> |
| [Queensland Legislation](https://www.legislation.qld.gov.au/) | 29 October 2023 | <ul><li>The most recent versions of all in force acts (primary legislation);</li> <li>The most recent versions of all in force statutory instruments (secondary legislation); and</li> <li>The as introduced versions of all bills.</li></ul> |
| [Western Australian Legislation](https://www.legislation.wa.gov.au/) | 29 October 2023 | <ul><li>The most recent versions of all in force acts (primary legislation); and</li> <li>The most recent versions of all in force subsidiary legislation (secondary legislation).</li></ul> |
| [South Australian Legislation](https://www.legislation.sa.gov.au/) | 29 October 2023 | <ul><li>The most recent versions of all in force acts (primary legislation); and</li> <li>The most recent versions of all in force proclamations, policies and regulations (secondary legislation).</li></ul> |
| [Tasmanian Legislation](https://www.legislation.tas.gov.au/) | 29 October 2023 | <ul><li>The most recent versions of all in force acts (primary legislation); and</li> <li>The most recent versions of all in force statutory rules (secondary legislation).</li></ul> |
The code used to create and update the Corpus can be found [here](https://github.com/umarbutler/open-australian-legal-corpus-creator).
Those interested in learning more about how the Corpus was built are encouraged to read Umar Butler's accompanying article, [*How I built the largest open database of Australian law*](https://umarbutler.com/how-i-built-the-largest-open-database-of-australian-law/).
## Changelog 🔄
All notable changes to the Corpus are documented in its [Changelog 🔄](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/CHANGELOG.md).
This project adheres to [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## Licence 📜
As a work constituting a collection of documents that have been cleaned, structured, annotated and otherwise processed, the Corpus itself is licensed under the [Creative Commons Attribution 4.0 International Licence](https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, on the condition that you give appropriate credit to the original author and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Documents contained within the Corpus are distributed under relatively equally permissive licences that, in most cases, allow for both non-commercial and commercial usage and are available in the complete version of this licence [here](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Citation 🔖
If you've relied on the Corpus for your work, please cite:
```bibtex
@misc{butler-2023-open-australian-legal-corpus,
author = {Butler, Umar},
year = {2023},
title = {Open Australian Legal Corpus},
publisher = {Hugging Face},
version = {4.2.0},
doi = {10.57967/hf/1306},
url = {https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus}
}
```
## Acknowledgements 🙏
In the spirit of reconciliation, the author acknowledges the Traditional Custodians of Country throughout Australia and their connections to land, sea and community. He pays his respect to their Elders past and present and extends that respect to all Aboriginal and Torres Strait Islander peoples today.
The author thanks the [Federal Register of Legislation](https://www.legislation.gov.au/), [Federal Court of Australia](https://www.fedcourt.gov.au/digital-law-library/judgments/search), [High Court of Australia](https://eresources.hcourt.gov.au/), [NSW Caselaw](https://www.caselaw.nsw.gov.au/), [NSW Legislation](https://legislation.nsw.gov.au/), [Queensland Legislation](https://www.legislation.qld.gov.au/), [Western Australian Legislation](https://www.legislation.wa.gov.au/), [South Australian Legislation](https://www.legislation.sa.gov.au/) and [Tasmanian Legislation](https://www.legislation.tas.gov.au/) for all granting him permission to scrape their data.
The author also acknowledges the creators of the many Python libraries relied upon in the creation of the Corpus, as well as the makers of the [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law), which served as a great source of inspiration for this project.
Finally, the author is eternally grateful for the endless support of his wife and her willingness to put up with many a late night spent writing code and quashing bugs. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cesarali/test_ipp50 | cesarali | 2023-08-28T17:28:36Z | 168 | 0 | null | [
"region:us"
] | 2023-08-28T17:28:36Z | 2023-08-28T17:28:33.000Z | 2023-08-28T17:28:33 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: choices
sequence: string
- name: value
dtype: float64
splits:
- name: train
num_bytes: 8439
num_examples: 50
download_size: 4060
dataset_size: 8439
---
# Dataset Card for "test_ipp50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tomaarsen/MultiCoNER | tomaarsen | 2023-10-01T19:39:19Z | 168 | 0 | null | [
"task_categories:token-classification",
"size_categories:100K<n<1M",
"language:bn",
"language:de",
"language:en",
"language:es",
"language:fa",
"language:hi",
"language:ko",
"language:nl",
"language:ru",
"language:tr",
"language:zh",
"language:multilingual",
"license:cc-by-4.0",
"multiconer",
"ner",
"multilingual",
"named entity recognition",
"arxiv:2208.14536",
"region:us"
] | 2023-10-01T19:39:19Z | 2023-10-01T18:44:19.000Z | 2023-10-01T18:44:19 | ---
license: cc-by-4.0
task_categories:
- token-classification
language:
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- de
- en
- es
- fa
- hi
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tags:
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- named entity recognition
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- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-CORP
'6': I-CORP
'7': B-GRP
'8': I-GRP
'9': B-PROD
'10': I-PROD
'11': B-CW
'12': I-CW
splits:
- name: train
num_bytes: 4076774
num_examples: 15300
- name: validation
num_bytes: 213017
num_examples: 800
- name: test
num_bytes: 14779846
num_examples: 136935
download_size: 22825291
dataset_size: 19069637
- config_name: zh
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-CORP
'6': I-CORP
'7': B-GRP
'8': I-GRP
'9': B-PROD
'10': I-PROD
'11': B-CW
'12': I-CW
splits:
- name: train
num_bytes: 5899475
num_examples: 15300
- name: validation
num_bytes: 310396
num_examples: 800
- name: test
num_bytes: 29349271
num_examples: 151661
download_size: 36101525
dataset_size: 35559142
---
# Multilingual Complex Named Entity Recognition (MultiCoNER)
## Dataset Summary
MultiCoNER (version 1) is a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation.
See the [AWS Open Data Registry entry for MultiCoNER](https://registry.opendata.aws/multiconer/) for more information.
## Labels
* `PER`: Person, i.e. names of people
* `LOC`: Location, i.e. locations/physical facilities
* `CORP`: Corporation, i.e. corporations/businesses
* `GRP`: Groups, i.e. all other groups
* `PROD`: Product, i.e. consumer products
* `CW`: Creative Work, i.e. movies/songs/book titles
### Dataset Structure
The dataset follows the IOB format of CoNLL. In particular, it uses the following label to ID mapping:
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-CORP": 5,
"I-CORP": 6,
"B-GRP": 7,
"I-GRP": 8,
"B-PROD": 9,
"I-PROD": 10,
"B-CW": 11,
"I-CW": 12,
}
```
## Languages
The MultiCoNER dataset consists of the following languages: Bangla, German, English, Spanish, Farsi, Hindi, Korean, Dutch, Russian, Turkish and Chinese.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset('tomaarsen/MultiCoNER', 'multi')
```
## License
CC BY 4.0
## Citation
```
@misc{malmasi2022multiconer,
title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition},
author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko},
year={2022},
eprint={2208.14536},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
NextDayAI/LabeledTestDataset_20231121_20231123 | NextDayAI | 2023-11-25T15:26:14Z | 168 | 0 | null | [
"region:us"
] | 2023-11-25T15:26:14Z | 2023-11-25T15:26:09.000Z | 2023-11-25T15:26:09 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: Fantastic
num_bytes: 57265482
num_examples: 10000
- name: Great
num_bytes: 59035717
num_examples: 10000
- name: Bad
num_bytes: 58671252
num_examples: 10000
- name: Terrible
num_bytes: 56874627
num_examples: 10000
download_size: 104028322
dataset_size: 231847078
configs:
- config_name: default
data_files:
- split: Fantastic
path: data/Fantastic-*
- split: Great
path: data/Great-*
- split: Bad
path: data/Bad-*
- split: Terrible
path: data/Terrible-*
---
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gsarti/mt_geneval | gsarti | 2022-11-21T14:52:09Z | 167 | 2 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:it",
"language:fr",
"language:ar",
"language:de",
"language:hi",
"language:pt",
"language:ru",
"language:es",
"license:cc-by-sa-3.0",
"gender",
"constrained mt",
"arxiv:2211.01355",
"region:us"
] | 2022-11-21T14:52:09Z | 2022-11-21T10:50:15.000Z | 2022-11-21T10:50:15 | ---
annotations_creators:
- expert-generated
language:
- en
- it
- fr
- ar
- de
- hi
- pt
- ru
- es
language_creators:
- expert-generated
license:
- cc-by-sa-3.0
multilinguality:
- translation
pretty_name: mt_geneval
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- gender
- constrained mt
task_categories:
- translation
task_ids: []
---
# Dataset Card for MT-GenEval
## Table of Contents
- [Dataset Card for MT-GenEval](#dataset-card-for-mt-geneval)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Machine Translation](#machine-translation)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [Github](https://github.com/amazon-science/machine-translation-gender-eval)
- **Paper:** [EMNLP 2022](https://arxiv.org/abs/2211.01355)
- **Point of Contact:** [Anna Currey](mailto:ancurrey@amazon.com)
### Dataset Summary
The MT-GenEval benchmark evaluates gender translation accuracy on English -> {Arabic, French, German, Hindi, Italian, Portuguese, Russian, Spanish}. The dataset contains individual sentences with annotations on the gendered target words, and contrastive original-invertend translations with additional preceding context.
**Disclaimer**: *The MT-GenEval benchmark was released in the EMNLP 2022 paper [MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation](https://arxiv.org/abs/2211.01355) by Anna Currey, Maria Nadejde, Raghavendra Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, and Georgiana Dinu and is hosted through Github by the [Amazon Science](https://github.com/amazon-science?type=source) organization. The dataset is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/).*
### Supported Tasks and Leaderboards
#### Machine Translation
Refer to the [original paper](https://arxiv.org/abs/2211.01355) for additional details on gender accuracy evaluation with MT-GenEval.
### Languages
The dataset contains source English sentences extracted from Wikipedia translated into the following languages: Arabic (`ar`), French (`fr`), German (`de`), Hindi (`hi`), Italian (`it`), Portuguese (`pt`), Russian (`ru`), and Spanish (`es`).
## Dataset Structure
### Data Instances
The dataset contains two configuration types, `sentences` and `context`, mirroring the original repository structure, with source and target language specified in the configuration name (e.g. `sentences_en_ar`, `context_en_it`) The `sentences` configurations contains masculine and feminine versions of individual sentences with gendered word annotations. Here is an example entry of the `sentences_en_it` split (all `sentences_en_XX` splits have the same structure):
```json
{
{
"orig_id": 0,
"source_feminine": "Pagratidis quickly recanted her confession, claiming she was psychologically pressured and beaten, and until the moment of her execution, she remained firm in her innocence.",
"reference_feminine": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era stata picchiata, e fino al momento della sua esecuzione, rimase ferma sulla sua innocenza.",
"source_masculine": "Pagratidis quickly recanted his confession, claiming he was psychologically pressured and beaten, and until the moment of his execution, he remained firm in his innocence.",
"reference_masculine": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era stato picchiato, e fino al momento della sua esecuzione, rimase fermo sulla sua innocenza.",
"source_feminine_annotated": "Pagratidis quickly recanted <F>her</F> confession, claiming <F>she</F> was psychologically pressured and beaten, and until the moment of <F>her</F> execution, <F>she</F> remained firm in <F>her</F> innocence.",
"reference_feminine_annotated": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era <F>stata picchiata</F>, e fino al momento della sua esecuzione, rimase <F>ferma</F> sulla sua innocenza.",
"source_masculine_annotated": "Pagratidis quickly recanted <M>his</M> confession, claiming <M>he</M> was psychologically pressured and beaten, and until the moment of <M>his</M> execution, <M>he</M> remained firm in <M>his</M> innocence.",
"reference_masculine_annotated": "Pagratidis subito ritrattò la sua confessione, affermando che era aveva subito pressioni psicologiche e era <M>stato picchiato</M>, e fino al momento della sua esecuzione, rimase <M>fermo</M> sulla sua innocenza.",
"source_feminine_keywords": "her;she;her;she;her",
"reference_feminine_keywords": "stata picchiata;ferma",
"source_masculine_keywords": "his;he;his;he;his",
"reference_masculine_keywords": "stato picchiato;fermo",
}
}
```
The `context` configuration contains instead different English sources related to stereotypical professional roles with additional preceding context and contrastive original-inverted translations. Here is an example entry of the `context_en_it` split (all `context_en_XX` splits have the same structure):
```json
{
"orig_id": 0,
"context": "Pierpont told of entering and holding up the bank and then fleeing to Fort Wayne, where the loot was divided between him and three others.",
"source": "However, Pierpont stated that Skeer was the planner of the robbery.",
"reference_original": "Comunque, Pierpont disse che Skeer era il pianificatore della rapina.",
"reference_flipped": "Comunque, Pierpont disse che Skeer era la pianificatrice della rapina."
}
```
### Data Splits
All `sentences_en_XX` configurations have 1200 examples in the `train` split and 300 in the `test` split. For the `context_en_XX` configurations, the number of example depends on the language pair:
| Configuration | # Train | # Test |
| :-----------: | :--------: | :-----: |
| `context_en_ar` | 792 | 1100 |
| `context_en_fr` | 477 | 1099 |
| `context_en_de` | 598 | 1100 |
| `context_en_hi` | 397 | 1098 |
| `context_en_it` | 465 | 1904 |
| `context_en_pt` | 574 | 1089 |
| `context_en_ru` | 583 | 1100 |
| `context_en_es` | 534 | 1096 |
### Dataset Creation
From the original paper:
>In developing MT-GenEval, our goal was to create a realistic, gender-balanced dataset that naturally incorporates a diverse range of gender phenomena. To this end, we extracted English source sentences from Wikipedia as the basis for our dataset. We automatically pre-selected relevant sentences using EN gender-referring words based on the list provided by [Zhao et al. (2018)](https://doi.org/10.18653/v1/N18-2003).
Please refer to the original article [MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation](https://arxiv.org/abs/2211.01355) for additional information on dataset creation.
## Additional Information
### Dataset Curators
The original authors of MT-GenEval are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com).
### Licensing Information
The dataset is licensed under the [Creative Commons Attribution-ShareAlike 3.0 International License](https://creativecommons.org/licenses/by-sa/3.0/).
### Citation Information
Please cite the authors if you use these corpora in your work.
```bibtex
@inproceedings{currey-etal-2022-mtgeneval,
title = "{MT-GenEval}: {A} Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation",
author = "Currey, Anna and
Nadejde, Maria and
Pappagari, Raghavendra and
Mayer, Mia and
Lauly, Stanislas, and
Niu, Xing and
Hsu, Benjamin and
Dinu, Georgiana",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2211.01355",
}
``` | [
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indonlp/NusaX-senti | indonlp | 2023-01-24T17:02:06Z | 167 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ace",
"language:ban",
"language:bjn",
"language:bug",
"language:en",
"language:id",
"language:jv",
"language:mad",
"language:min",
"language:nij",
"language:su",
"language:bbc",
"license:cc-by-sa-4.0",
"arxiv:2205.15960",
"region:us"
] | 2023-01-24T17:02:06Z | 2023-01-24T09:28:21.000Z | 2023-01-24T09:28:21 | ---
pretty_name: NusaX-senti
annotations_creators:
- expert-generated
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
language:
- ace
- ban
- bjn
- bug
- en
- id
- jv
- mad
- min
- nij
- su
- bbc
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: lang
dtype: string
- name: label
dtype:
class_label:
names:
0: negative
1: neutral
2: positive
---
# Dataset Card for NusaX-Senti
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment)
- **Paper:** [EACL 2022](https://arxiv.org/abs/2205.15960)
- **Point of Contact:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/sentiment)
### Dataset Summary
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment analysis dataset for 10 Indonesian local languages + Indonesian and English.
### Supported Tasks and Leaderboards
- Sentiment analysis for Indonesian languages
### Languages
- ace: acehnese,
- ban: balinese,
- bjn: banjarese,
- bug: buginese,
- eng: english,
- ind: indonesian,
- jav: javanese,
- mad: madurese,
- min: minangkabau,
- nij: ngaju,
- sun: sundanese,
- bbc: toba_batak,
## Dataset Creation
### Curation Rationale
There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.
### Source Data
#### Initial Data Collection and Normalization
NusaX-senti is a dataset for sentiment analysis in Indonesian that has been expertly translated by native speakers.
#### Who are the source language producers?
The data was produced by humans (native speakers).
### Annotations
#### Annotation process
NusaX-senti is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.
#### Who are the annotators?
Native speakers of both Indonesian and the corresponding languages.
Annotators were compensated based on the number of translated samples.
### Personal and Sensitive Information
Personal information is removed.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
NusaX is created from review text. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Licensing Information
CC-BY-SA 4.0.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please contact authors for any information on the dataset.
### Citation Information
```
@misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
Jey Han and Sennrich, Rico and Ruder, Sebastian},
year={2022},
eprint={2205.15960},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
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marianna13/libgen | marianna13 | 2023-04-13T22:32:38Z | 167 | 1 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Jing24/val_oneanswer | Jing24 | 2023-08-19T00:35:46Z | 167 | 0 | null | [
"region:us"
] | 2023-08-19T00:35:46Z | 2023-08-19T00:35:44.000Z | 2023-08-19T00:35:44 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int32
- name: text
sequence: string
splits:
- name: train
num_bytes: 9832949
num_examples: 10570
download_size: 1675804
dataset_size: 9832949
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "val_oneanswer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kejian/MultiCite-classification-gold-context | kejian | 2023-11-07T06:22:46Z | 167 | 0 | null | [
"region:us"
] | 2023-11-07T06:22:46Z | 2023-11-07T06:22:43.000Z | 2023-11-07T06:22:43 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: x
dtype: string
- name: y
dtype: string
splits:
- name: train
num_bytes: 1644148
num_examples: 5491
- name: test
num_bytes: 971485
num_examples: 3313
- name: validation
num_bytes: 761342
num_examples: 2447
download_size: 1478485
dataset_size: 3376975
---
# Dataset Card for "MultiCite-classification-gold-context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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AmazonScience/mintaka | AmazonScience | 2022-10-28T10:55:50Z | 166 | 7 | mintaka | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:ar",
"multilinguality:de",
"multilinguality:ja",
"multilinguality:hi",
"multilinguality:pt",
"multilinguality:en",
"multilinguality:es",
"multilinguality:it",
"multilinguality:fr",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:cc-by-4.0",
"region:us"
] | 2022-10-28T10:55:50Z | 2022-10-27T18:38:30.000Z | 2022-10-27T18:38:30 | ---
annotations_creators:
- expert-generated
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- ar
- de
- ja
- hi
- pt
- en
- es
- it
- fr
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: mintaka
pretty_name: Mintaka
language_bcp47:
- ar-SA
- de-DE
- ja-JP
- hi-HI
- pt-PT
- en-EN
- es-ES
- it-IT
- fr-FR
---
# Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/amazon-science/mintaka
- **Repository:** https://github.com/amazon-science/mintaka
- **Paper:** https://aclanthology.org/2022.coling-1.138/
- **Point of Contact:** [GitHub](https://github.com/amazon-science/mintaka)
### Dataset Summary
Mintaka is a complex, natural, and multilingual question answering (QA) dataset composed of 20,000 question-answer pairs elicited from MTurk workers and annotated with Wikidata question and answer entities. Full details on the Mintaka dataset can be found in our paper: https://aclanthology.org/2022.coling-1.138/
To build Mintaka, we explicitly collected questions in 8 complexity types, as well as generic questions:
- Count (e.g., Q: How many astronauts have been elected to Congress? A: 4)
- Comparative (e.g., Q: Is Mont Blanc taller than Mount Rainier? A: Yes)
- Superlative (e.g., Q: Who was the youngest tribute in the Hunger Games? A: Rue)
- Ordinal (e.g., Q: Who was the last Ptolemaic ruler of Egypt? A: Cleopatra)
- Multi-hop (e.g., Q: Who was the quarterback of the team that won Super Bowl 50? A: Peyton Manning)
- Intersection (e.g., Q: Which movie was directed by Denis Villeneuve and stars Timothee Chalamet? A: Dune)
- Difference (e.g., Q: Which Mario Kart game did Yoshi not appear in? A: Mario Kart Live: Home Circuit)
- Yes/No (e.g., Q: Has Lady Gaga ever made a song with Ariana Grande? A: Yes.)
- Generic (e.g., Q: Where was Michael Phelps born? A: Baltimore, Maryland)
- We collected questions about 8 categories: Movies, Music, Sports, Books, Geography, Politics, Video Games, and History
Mintaka is one of the first large-scale complex, natural, and multilingual datasets that can be used for end-to-end question-answering models.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for question answering.
To ensure comparability, please refer to our evaluation script here: https://github.com/amazon-science/mintaka#evaluation
### Languages
All questions were written in English and translated into 8 additional languages: Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```json
{
"id": "a9011ddf",
"lang": "en",
"question": "What is the seventh tallest mountain in North America?",
"answerText": "Mount Lucania",
"category": "geography",
"complexityType": "ordinal",
"questionEntity":
[
{
"name": "Q49",
"entityType": "entity",
"label": "North America",
"mention": "North America",
"span": [40, 53]
},
{
"name": 7,
"entityType": "ordinal",
"mention": "seventh",
"span": [12, 19]
}
],
"answerEntity":
[
{
"name": "Q1153188",
"label": "Mount Lucania",
}
],
}
```
### Data Fields
The data fields are the same among all splits.
`id`: a unique ID for the given sample.
`lang`: the language of the question.
`question`: the original question elicited in the corresponding language.
`answerText`: the original answer text elicited in English.
`category`: the category of the question. Options are: geography, movies, history, books, politics, music, videogames, or sports
`complexityType`: the complexity type of the question. Options are: ordinal, intersection, count, superlative, yesno comparative, multihop, difference, or generic
`questionEntity`: a list of annotated question entities identified by crowd workers.
```
{
"name": The Wikidata Q-code or numerical value of the entity
"entityType": The type of the entity. Options are:
entity, cardinal, ordinal, date, time, percent, quantity, or money
"label": The label of the Wikidata Q-code
"mention": The entity as it appears in the English question text. Will be empty for non-English samples.
"span": The start and end characters of the mention in the English question text. Will be empty for non-English samples.
}
```
`answerEntity`: a list of annotated answer entities identified by crowd workers.
```
{
"name": The Wikidata Q-code or numerical value of the entity
"label": The label of the Wikidata Q-code
}
```
### Data Splits
For each language, we split into train (14,000 samples), dev (2,000 samples), and test (4,000 samples) sets.
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## 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
Amazon Alexa AI.
### Licensing Information
This project is licensed under the CC-BY-4.0 License.
### Citation Information
Please cite the following papers when using this dataset.
```latex
@inproceedings{sen-etal-2022-mintaka,
title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
author = "Sen, Priyanka and
Aji, Alham Fikri and
Saffari, Amir",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.138",
pages = "1604--1619"
}
```
### Contributions
Thanks to [@afaji](https://github.com/afaji) for adding this dataset. | [
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gtfintechlab/finer-ord | gtfintechlab | 2023-02-23T22:17:44Z | 166 | 4 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-02-23T22:17:44Z | 2023-02-07T22:03:57.000Z | 2023-02-07T22:03:57 | ---
license: cc-by-nc-4.0
task_categories:
- token-classification
language:
- en
pretty_name: FiNER
size_categories:
- 1K<n<10K
multilinguality:
- monolingual
task_ids:
- named-entity-recognition
---
# Dataset Card for "FiNER-ORD"
## 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 and Annotation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact Information](#contact-information)
## Dataset Description
- **Homepage:** [https://github.com/gtfintechlab/FiNER](https://github.com/gtfintechlab/FiNER)
- **Repository:** [https://github.com/gtfintechlab/FiNER](https://github.com/gtfintechlab/FiNER)
- **Paper:** [Arxiv Link]()
- **Point of Contact:** [Agam A. Shah](https://shahagam4.github.io/)
- **Size of train dataset file:** 1.08 MB
- **Size of validation dataset file:** 135 KB
- **Size of test dataset file:** 336 KB
### Dataset Summary
The FiNER-Open Research Dataset (FiNER-ORD) consists of a manually annotated dataset of financial news articles (in English)
collected from [webz.io] (https://webz.io/free-datasets/financial-news-articles/).
In total, there are 47851 news articles available in this data at the point of writing this paper.
Each news article is available in the form of a JSON document with various metadata information like
the source of the article, publication date, author of the article, and the title of the article.
For the manual annotation of named entities in financial news, we randomly sampled 220 documents from the entire set of news articles.
We observed that some articles were empty in our sample, so after filtering the empty documents, we were left with a total of 201 articles.
We use [Doccano](https://github.com/doccano/doccano), an open-source annotation tool,
to ingest the raw dataset and manually label person (PER), location (LOC), and organization (ORG) entities.
For our experiments, we use the manually labeled FiNER-ORD to benchmark model performance.
Thus, we make a train, validation, and test split of FiNER-ORD.
To avoid biased results, manual annotation is performed by annotators who have no knowledge about the labeling functions for the weak supervision framework.
The train and validation sets are annotated by two separate annotators and validated by a third annotator.
The test dataset is annotated by another annotator. We present a manual annotation guide in the Appendix of the paper detailing the procedures used to create the manually annotated FiNER-ORD.
After manual annotation, the news articles are split into sentences.
We then tokenize each sentence, employing a script to tokenize multi-token entities into separate tokens (e.g. PER_B denotes the beginning token of a person (PER) entity
and PER_I represents intermediate PER tokens). We exclude white spaces when tokenizing multi-token entities.
The descriptive statistics on the resulting FiNER-ORD are available in the Table of [Data Splits](#data-splits) section.
For more details check [information in paper]()
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
- It is a monolingual English dataset
## Dataset Structure
### Data Instances
#### FiNER-ORD
- **Size of train dataset file:** 1.08 MB
- **Size of validation dataset file:** 135 KB
- **Size of test dataset file:** 336 KB
### Data Fields
The data fields are the same among all splits.
#### conll2003
- `doc_idx`: Document ID (`int`)
- `sent_idx`: Sentence ID within each document (`int`)
- `gold_token`: Token (`string`)
- `gold_label`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'PER_B': 1, 'PER_I': 2, 'LOC_B': 3, 'LOC_I': 4, 'ORG_B': 5, 'ORG_I': 6}
```
### Data Splits
| **FiNER-ORD** | **Train** | **Validation** | **Test** |
|------------------|----------------|---------------------|---------------|
| # Articles | 135 | 24 | 42 |
| # Tokens | 80,531 | 10,233 | 25,957 |
| # LOC entities | 1,255 | 267 | 428 |
| # ORG entities | 3,440 | 524 | 933 |
| # PER entities | 1,374 | 222 | 466 |
## Dataset Creation and Annotation
[Information in paper ]()
## Additional Information
### Licensing Information
[Information in paper ]()
### Citation Information
```
@article{shah2023finer,
title={FiNER: Financial Named Entity Recognition Dataset and Weak-supervision Model},
author={Agam Shah and Ruchit Vithani and Abhinav Gullapalli and Sudheer Chava},
journal={arXiv preprint arXiv:2302.11157},
year={2023}
}
```
### Contact Information
Please contact Agam Shah (ashah482[at]gatech[dot]edu) or Ruchit Vithani (rvithani6[at]gatech[dot]edu) about any FiNER-related issues and questions.
GitHub: [@shahagam4](https://github.com/shahagam4), [@ruchit2801](https://github.com/ruchit2801)
Website: [https://shahagam4.github.io/](https://shahagam4.github.io/)
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ahmed-masry/chartqa_without_images | ahmed-masry | 2023-07-26T00:46:27Z | 165 | 0 | null | [
"region:us"
] | 2023-07-26T00:46:27Z | 2023-07-26T00:46:17.000Z | 2023-07-26T00:46:17 | ---
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: imgname
dtype: string
- name: query
dtype: string
- name: label
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 3169833
num_examples: 28299
- name: val
num_bytes: 213437
num_examples: 1920
- name: test
num_bytes: 272309
num_examples: 2500
download_size: 1596635
dataset_size: 3655579
---
# Dataset Card for "chartqa_without_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mlnsio/audio1 | mlnsio | 2023-11-08T07:24:56Z | 165 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-08T07:24:56Z | 2023-11-01T11:10:26.000Z | 2023-11-01T11:10:26 | ---
license: apache-2.0
dataset_info:
features:
- name: file_name
dtype: string
- name: genre
dtype: string
splits:
- name: train
num_bytes: 2235
num_examples: 50
download_size: 1997
dataset_size: 2235
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Laurent1/MedQuad-MedicalQnADataset_128tokens_max | Laurent1 | 2023-11-03T06:52:06Z | 165 | 0 | null | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-11-03T06:52:06Z | 2023-11-02T15:43:47.000Z | 2023-11-02T15:43:47 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
Reference : "A Question-Entailment Approach to Question Answering". Asma Ben Abacha and Dina Demner-Fushman. BMC Bioinformatics, 2019."
<br/>This is an update of Keivalya Pandya's dataset (keivalya/MedQuad-MedicalQnADataset).
<h1> Content </h1>
There are medical questions and corresponding responses in a prompt format for chat or instruct model types
<br/>In order to fine tuned LLM with small HW (1 or 2 GPU with 14 Go)
<br/>Rows above 128 tokens have been deleted.
<br/>Rows have been truncated to a line break or a sentence end in order to keep a correct meaning
<h2> Script to download the dataset </h2>
<br/>from datasets import load_dataset
<br/>dataset_name = "Laurent1/MedQuad-MedicalQnADataset_128tokens_max"
<br/>dataset = load_dataset(dataset_name, split="train") | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
scikit-learn/iris | scikit-learn | 2022-06-20T14:17:01Z | 164 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2022-06-20T14:17:01Z | 2022-06-20T14:10:10.000Z | 2022-06-20T14:10:10 | ---
license: cc0-1.0
---
## Iris Species Dataset
The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.
It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.
The dataset is taken from [UCI Machine Learning Repository's Kaggle](https://www.kaggle.com/datasets/uciml/iris).
The following description is taken from UCI Machine Learning Repository.
This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
Predicted attribute: class of iris plant.
This is an exceedingly simple domain.
This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.
Features in this dataset are the following:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris Setosa
- Iris Versicolour
- Iris Virginica
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jordanparker6/publaynet | jordanparker6 | 2022-07-19T04:20:00Z | 164 | 9 | null | [
"task_categories:image-to-text",
"size_categories:100B<n<1T",
"language:en",
"license:other",
"arxiv:1908.07836",
"region:us"
] | 2022-07-19T04:20:00Z | 2022-07-17T23:32:26.000Z | 2022-07-17T23:32:26 | ---
title: PubLayNet
license: other
annotations_creators: []
language:
- en
size_categories:
- 100B<n<1T
source_datasets: []
task_categories:
- image-to-text
task_ids: []
---
# PubLayNet
PubLayNet is a large dataset of document images, of which the layout is annotated with both bounding boxes and polygonal segmentations. The source of the documents is [PubMed Central Open Access Subset (commercial use collection)](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). The annotations are automatically generated by matching the PDF format and the XML format of the articles in the PubMed Central Open Access Subset. More details are available in our paper ["PubLayNet: largest dataset ever for document layout analysis."](https://arxiv.org/abs/1908.07836).
The public dataset is in tar.gz format which doesn't fit nicely with huggingface streaming. Modifications have been made to optimise the delivery of the dataset for the hugginface datset api. The original files can be found [here](https://developer.ibm.com/exchanges/data/all/publaynet/).
Licence: [Community Data License Agreement – Permissive – Version 1.0 License](https://cdla.dev/permissive-1-0/)
Author: IBM
GitHub: https://github.com/ibm-aur-nlp/PubLayNet
@article{ zhong2019publaynet,
title = { PubLayNet: largest dataset ever for document layout analysis },
author = { Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno },
journal = { arXiv preprint arXiv:1908.07836},
year. = { 2019 }
} | [
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gigant/oldbookillustrations | gigant | 2022-08-03T17:35:37Z | 164 | 16 | null | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:image-to-image",
"task_ids:image-captioning",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:fr",
"language:de",
"license:cc-by-nc-4.0",
"lam",
"1800-1900",
"region:us"
] | 2022-08-03T17:35:37Z | 2022-07-28T08:31:19.000Z | 2022-07-28T08:31:19 | ---
annotations_creators:
- expert-generated
language:
- en
- fr
- de
language_creators:
- expert-generated
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: Old Book Illustrations
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- lam
- 1800-1900
task_categories:
- text-to-image
- image-to-text
- image-to-image
task_ids:
- image-captioning
---
# Dataset Card for Old Book Illustrations
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://www.oldbookillustrations.com/)**
### Dataset Summary
The Old Book Illustrations contains 4172 illustrations scanned from old books, this collection was collected & curated by the team of the website [Old Book Illustrations](https://www.oldbookillustrations.com/).
The webmaster of Old Book Illustrations kindly allowed us to scrap these information in order to create this dataset for the [BigLAM initiative](https://huggingface.co/biglam).
### Languages
The captions and descriptions are mostly in English but can contain some sentences from other languages such as French or German.
For instance you can find this description that contains a French sentence:
>The caption reads in the original French: Vue de l’aqueduc de Salones qui conduisait l’eau à Spalatro.
## Dataset Structure
Each row contains information gathered from the page of an illustration on the website [Old Book Illustrations](https://www.oldbookillustrations.com/). As of July 2022, there are 4172 illustrations in this dataset.
### Data Fields
* `rawscan`: the image as originally scanned from the book, without further processing
* `1600px`: the cleaned image, resized to a width of 1600 pixels (height can vary)
* `info_url`: URL to the illustration page on oldbookillustrations.com
* `ìnfo_src`: URL to an icon-sized version of the image
* `info_alt`: short description of the image
* `artist_name`: artist name
* `artist_date`: birth date of the artist
* `artist_countries`: list of the countries the artist is from
* `book_title`: original title of the book the illustration is extracted from
* `book_authors`: list of the authors of the book
* `book_publishers`: list of the publishers of the book
* `openlibrary-url`: URL to the openlibrary entry for the book
* `tags`: list of keywords for this illustration on oldbookillustrations.com
* `illustration_source_name`: list of the sources for this illustration
* `illustration_source_url`: list of the URL for these sources
* `illustration_subject`: category of the subject represented in the illustration
* `illustration_format`: category of the format of the illustration
* `image_title`: title of the image
* `image_caption`: caption of the image. Seems to be the caption that appears next to the image in the book, translated to English if in another language
* `image_description`: longer description of the image. If there is one, it also quotes the caption in the original language
* `rawscan_url`: URL to the rawscan image on oldbookillustration.com
* `1600px_url`: URL to the cleaned image on oldbookillustration.com
## Dataset Creation
### Curation Rationale
This collection was collected & curated by the team of the website [Old Book Illustrations](https://www.oldbookillustrations.com/).
This version contains all the data that was available on the website as of July 2022, but the website is being actively maintained so if you want more old book illustrations, make sure to check [Old Book Illustrations](https://www.oldbookillustrations.com/).
### Source Data
#### Initial Data Collection and Normalization
Initial data is gathered from the website [Old Book Illustrations](https://www.oldbookillustrations.com/). The sources of the illustration scans are specified for each entry in the columns `illustration_source_name` and `illustration_source_url`.
### Personal and Sensitive Information
The Old Book Illustrations' Terms and conditions reads:
>OBI [Old Book Illustrations] explores the art of book illustrations within boundaries defined by time and age, not by subject, treatment, or intent. This means that some illustrations might be deemed offensive, disturbing, misleading, or otherwise objectionable. We do not endorse views or opinions the Illustrations may express, neither do we guarantee that the information conveyed by any Illustration is accurate.
## Considerations for Using the Data
### Discussion of Biases
The Old Book Illustrations' Terms and conditions reads:
>OBI [Old Book Illustrations] explores the art of book illustrations within boundaries defined by time and age, not by subject, treatment, or intent. This means that some illustrations might be deemed offensive, disturbing, misleading, or otherwise objectionable. We do not endorse views or opinions the Illustrations may express, neither do we guarantee that the information conveyed by any Illustration is accurate.
## Additional Information
### Dataset Curators
The Old Book Illustrations collection is curated and maintained by the team of the [Old Book Illustrations website](https://www.oldbookillustrations.com/).
### Licensing Information
[Old Book Illustrations](https://www.oldbookillustrations.com/) website reads:
>We don’t limit the use of the illustrations available on our site, but we accept no responsibility regarding any problem, legal or otherwise, which might result from such use. More specifically, we leave it up to users to make sure that their project complies with the copyright laws of their country of residence. Text content (descriptions, translations, etc.) is published under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The Old Book Illustrations webmaster mentioned that most images are public domain in the US and Europe, but there can be some exceptions. An example are the illustrations from [*Early poems of William Morris*](https://www.oldbookillustrations.com/titles/early-poems-of-william-morris/) as the illustrator died 1955, so her work is not public domain in Europe as of 2022, or [*Under the hill*](https://www.oldbookillustrations.com/titles/under-the-hill/) which was published in the US in 1928 and therefore is not public domain there.
### Citation Information
```bibtex
@misc{old book illustrations_2007,
url={https://www.oldbookillustrations.com/},
journal={Old Book Illustrations}, year={2007}}
```
### Contributions
Thanks to [@gigant](https://huggingface.co/gigant) ([@giganttheo](https://github.com/giganttheo)) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ai-forever/school_notebooks_RU | ai-forever | 2023-02-09T18:27:24Z | 164 | 8 | null | [
"task_categories:image-segmentation",
"task_categories:object-detection",
"source_datasets:original",
"language:ru",
"license:mit",
"optical-character-recognition",
"text-detection",
"ocr",
"region:us"
] | 2023-02-09T18:27:24Z | 2022-09-08T10:06:32.000Z | 2022-09-08T10:06:32 | ---
language:
- ru
license:
- mit
source_datasets:
- original
task_categories:
- image-segmentation
- object-detection
task_ids: []
tags:
- optical-character-recognition
- text-detection
- ocr
---
# School Notebooks Dataset
The images of school notebooks with handwritten notes in Russian.
The dataset annotation contain end-to-end markup for training detection and OCR models, as well as an end-to-end model for reading text from pages.
## Annotation format
The annotation is in COCO format. The `annotation.json` should have the following dictionaries:
- `annotation["categories"]` - a list of dicts with a categories info (categotiy names and indexes).
- `annotation["images"]` - a list of dictionaries with a description of images, each dictionary must contain fields:
- `file_name` - name of the image file.
- `id` for image id.
- `annotation["annotations"]` - a list of dictioraties with a murkup information. Each dictionary stores a description for one polygon from the dataset, and must contain the following fields:
- `image_id` - the index of the image on which the polygon is located.
- `category_id` - the polygon’s category index.
- `attributes` - dict with some additional annotation information. In the `translation` subdict you can find text translation for the line.
- `segmentation` - the coordinates of the polygon, a list of numbers - which are coordinate pairs x and y. | [
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ywchoi/pubmed_abstract_3 | ywchoi | 2022-09-13T01:01:39Z | 164 | 1 | null | [
"region:us"
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0.14084689319133759,
0.8338428735733032,
0.31260576844215393,
-0.17026856541633606,
0.2698982357978821,
-0.4559198319911957,
-0.02893332578241825,
-0.05796259641647339,
0.3101596236228943,
-1.026215672492981
] | null | null | null | null | null | null | null | null | null | null | null | null | null |
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