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severo/flores_101 | severo | 2022-10-27T08:37:36Z | 397 | 1 | flores | [
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] | 2022-10-27T08:37:36Z | 2023-06-20T21:40:23.000Z | 2023-06-20T21:40:23 | ---
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paperswithcode_id: flores
pretty_name: flores101
tags:
- conditional-text-generation
---
# Dataset Card for Flores 101
## Table of Contents
- [Dataset Card for Flores 101](#dataset-card-for-flores-101)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Home:** [WMT](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html)
- **Repository:** [Github](https://github.com/facebookresearch/flores)
- **Blogpost:** [FAIR](https://ai.facebook.com/blog/the-flores-101-data-set-helping-build-better-translation-systems-around-the-world)
- **Paper:** [Arxiv](https://arxiv.org/abs/2106.03193)
- **Point of Contact:** [flores@fb.com](mailto:flores@fb.com)
- **Leaderboard** [Dynabench](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL))
### Dataset Summary
FLORES is a benchmark dataset for machine translation between English and low-resource languages.
Abstract from the original paper:
> One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.
**Disclaimer**: *The Flores-101 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
### Supported Tasks and Leaderboards
#### Multilingual Machine Translation
Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html).
### Languages
The dataset contains parallel sentences for 101 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) as in the original dataset.
**New:** Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command.
## Dataset Structure
### Data Instances
A sample from the `dev` split for the Russian language (`rus` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
```python
{
'id': 1,
'sentence': 'В понедельник ученые из Медицинской школы Стэнфордского университета объявили об изобретении нового диагностического инструмента, который может сортировать клетки по их типу; это маленький чип, который можно напечатать, используя стандартный струйный принтер примерно за 1 цент США.',
'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet',
'domain': 'wikinews',
'topic': 'health',
'has_image': 0,
'has_hyperlink': 0
}
```
The text is provided as-in the original dataset, without further preprocessing or tokenization.
### Data Fields
- `id`: Row number for the data entry, starting at 1.
- `sentence`: The full sentence in the specific language.
- `URL`: The URL for the English article from which the sentence was extracted.
- `domain`: The domain of the sentence.
- `topic`: The topic of the sentence.
- `has_image`: Whether the original article contains an image.
- `has_hyperlink`: Whether the sentence contains a hyperlink.
### Data Splits
| config| `dev`| `devtest`|
|-----------------:|-----:|---------:|
|all configurations| 997| 1012:|
### Dataset Creation
Please refer to the original article [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://arxiv.org/abs/2106.03193) for additional information on dataset creation.
## Additional Information
### Dataset Curators
The original authors of FLORES-101 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
Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@inproceedings{flores101,
title={The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela},
journal={arXiv preprint arXiv:2106.03193},
year={2021}
}
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erwanlc/cocktails_recipe_no_brand | erwanlc | 2022-10-25T09:17:08Z | 396 | 2 | null | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:2M<n<3M",
"language:en",
"license:other",
"region:us"
] | 2022-10-25T09:17:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 2M<n<3M
source_datasets: []
task_categories: []
task_ids: []
pretty_name: cocktails_recipe_no_brand
language_bcp47:
- en
- en-US
---
# Dataset Card for cocktails_recipe
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains a list of cocktails and how to do them.
### Languages
The language is english.
## Dataset Structure
### Data Fields
- Title: name of the cocktail
- Glass: type of glass to use
- Garnish: garnish to use for the glass
- Recipe: how to do the cocktail
- Ingredients: ingredients required
- Raw Ingredients: ingredients mapped to their raw ingredients to remove the brand
### Data Splits
Currently, there is no splits.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by scraping the Diffords cocktail website.
### Personal and Sensitive Information
It should not contain any personal or sensitive information.
### Contributions
Thanks to [@github-erwanlc](https://github.com/erwanlc) for adding this dataset. | [
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flax-community/conceptual-captions-12 | flax-community | 2021-07-19T12:40:00Z | 396 | 2 | null | [
"region:us"
] | 2021-07-19T12:40:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This file contains English captions from Conceptual 12M dataset by Google. Since we don't own the images, we have provided the link to images, name of downloaded file, and caption for that image in the TSV file.
We would like to thank [Luke Melas](https://github.com/lukemelas) for helping us get the cleaned CC-12M data on our TPU-VMs. | [
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ywchoi/pubmed_abstract_0 | ywchoi | 2022-09-13T00:53:42Z | 396 | 1 | null | [
"region:us"
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limit | null | 2022-11-18T20:18:52Z | 395 | 3 | limit | [
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|net-activities-captions",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-11-18T20:18:52Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|net-activities-captions
- original
task_categories:
- token-classification
- text-classification
task_ids:
- multi-class-classification
- named-entity-recognition
paperswithcode_id: limit
pretty_name: LiMiT
dataset_info:
features:
- name: id
dtype: int32
- name: sentence
dtype: string
- name: motion
dtype: string
- name: motion_entities
list:
- name: entity
dtype: string
- name: start_index
dtype: int32
splits:
- name: train
num_bytes: 3064208
num_examples: 23559
- name: test
num_bytes: 139742
num_examples: 1000
download_size: 4214925
dataset_size: 3203950
---
# Dataset Card for LiMiT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** -
- **Repository:** [github](https://github.com/ilmgut/limit_dataset)
- **Paper:** [LiMiT: The Literal Motion in Text Dataset](https://www.aclweb.org/anthology/2020.findings-emnlp.88/)
- **Leaderboard:** N/A
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying
motion of physical entities in natural language have not been explored extensively and empirically.
Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences
describing physical occurrence of motion, with annotated physical entities in motion.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English (`en`).
## Dataset Structure
### Data Instances
Example of one instance in the dataset
```
{
"id": 0,
"motion": "yes",
"motion_entities": [
{
"entity": "little boy",
"start_index": 2
},
{
"entity": "ball",
"start_index": 30
}
],
"sentence": " A little boy holding a yellow ball walks by."
}
```
### Data Fields
- `id`: intger index of the example
- `motion`: indicates whether the sentence is literal motion i.e. describes the movement of a physical entity or not
- `motion_entities`: A `list` of `dicts` with following keys
- `entity`: the extracted entity in motion
- `start_index`: index in the sentence for the first char of the entity text
### Data Splits
The dataset is split into a `train`, and `test` split with the following sizes:
| | train | validation |
| ----- |------:|-----------:|
| Number of examples | 23559 | 1000 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
```
@inproceedings{manotas-etal-2020-limit,
title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
author = "Manotas, Irene and
Vo, Ngoc Phuoc An and
Sheinin, Vadim",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88",
doi = "10.18653/v1/2020.findings-emnlp.88",
pages = "991--1000",
abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.",
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/imagine-dragons | huggingartists | 2022-10-25T09:31:53Z | 395 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:31:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/imagine-dragons"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.380929 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/ec1df125fd46ec3ef56f228df021a8cd.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/imagine-dragons">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Imagine Dragons</div>
<a href="https://genius.com/artists/imagine-dragons">
<div style="text-align: center; font-size: 14px;">@imagine-dragons</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/imagine-dragons).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/imagine-dragons")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|272| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/imagine-dragons")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ar_res_reviews | null | 2023-01-25T14:26:30Z | 394 | 5 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | 2023-01-25T14:26:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ArRestReviews
dataset_info:
features:
- name: polarity
dtype:
class_label:
names:
'0': negative
'1': positive
- name: text
dtype: string
- name: restaurant_id
dtype: string
- name: user_id
dtype: string
splits:
- name: train
num_bytes: 3617097
num_examples: 8364
download_size: 3503230
dataset_size: 3617097
---
# Dataset Card for ArRestReviews
## 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:** [Large Arabic Sentiment Analysis Resources](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces)
- **Repository:** [Large Arabic Sentiment Analysis Resources](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces)
- **Paper:** [ Building Large Arabic Multi-domain Resources for Sentiment Analysis](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces/blob/master/Paper%20-%20Building%20Large%20Arabic%20Multi-domain%20Resources%20for%20Sentiment%20Analysis.pdf)
- **Point of Contact:** [hady elsahar](hadyelsahar@gmail.com)
### Dataset Summary
Dataset of 8364 restaurant reviews from qaym.com in Arabic for sentiment analysis
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises of the following:
- "polarity": which is a string value of either 0 or 1 indicating the sentiment around the review
- "text": is the review plain text of a restaurant in Arabic
- "restaurant_id": the restaurant ID on the website
- "user_id": the user ID on the website
example:
```
{
'polarity': 0, # negative
'restaurant_id': '1412',
'text': 'عادي جدا مامن زود',
'user_id': '21294'
}
```
### Data Fields
- "polarity": is a string value of either 0 or 1 indicating the sentiment around the review
- "text": is the review plain text of a restaurant in Arabic
- "restaurant_id": the restaurant ID on the website (string)
- "user_id": the user ID on the website (string)
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
Contains 8364 restaurant reviews from qaym.com
#### Who are the source language producers?
From tweeter.
### Annotations
The polarity field provides a label of 1 or -1 pertaining to the sentiment of the review
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Discussion of Social Impact and Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
@InProceedings{10.1007/978-3-319-18117-2_2,
author="ElSahar, Hady
and El-Beltagy, Samhaa R.",
editor="Gelbukh, Alexander",
title="Building Large Arabic Multi-domain Resources for Sentiment Analysis",
booktitle="Computational Linguistics and Intelligent Text Processing",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="23--34",
isbn="978-3-319-18117-2"
}
### Contributions
Thanks to [@abdulelahsm](https://github.com/abdulelahsm) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/slava-marlow | huggingartists | 2022-10-25T09:45:03Z | 394 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:45:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/slava-marlow"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.471147 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/e308b1bc9eeb159ecfa9d807d715f095.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/slava-marlow">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">SLAVA MARLOW</div>
<a href="https://genius.com/artists/slava-marlow">
<div style="text-align: center; font-size: 14px;">@slava-marlow</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/slava-marlow).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/slava-marlow")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|249| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/slava-marlow")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
taeshahn/ko-lima | taeshahn | 2023-11-28T05:25:02Z | 394 | 9 | null | [
"license:cc-by-nc-sa-4.0",
"arxiv:2305.11206",
"region:us"
] | 2023-11-28T05:25:02Z | 2023-06-13T15:10:24.000Z | 2023-06-13T15:10:24 | ---
license: cc-by-nc-sa-4.0
---
# Dataset Card for KoLIMA
## Dataset Description
KoLIMA는 Meta에서 공개한 [LIMA: Less Is More for Alignment](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023)의 [학습 데이터](https://huggingface.co/datasets/GAIR/lima)를 한국어로 번역한 데이터셋입니다. 번역에는 [DeepL API](https://www.deepl.com/docs-api)를 활용하였고, SK(주) Tech Collaborative Lab으로부터 비용을 지원받았습니다. 전체 텍스트 중에서 code block이나 수식을 나타내는 특수문자 사이의 텍스트는 원문을 유지하는 형태로 번역을 진행하였으며, `train` 데이터셋 1,030건과 `test` 데이터셋 300건으로 구성된 총 1,330건의 데이터를 활용하실 수 있습니다. 현재 동일한 번역 문장을 `plain`, `vicuna` 두 가지 포멧으로 제공합니다.
데이터셋 관련하여 문의가 있으신 경우 [메일](mailto:taes.hahn@gmail.com)을 통해 연락주세요! 🥰
This is Korean LIMA dataset, which is translated from the [LIMA dataset](https://huggingface.co/datasets/GAIR/lima) that Meta's [LIMA model](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023) was trained on. The translation has proceeded through [DeepL API](https://www.deepl.com/docs-api) with financial support from Tech Collaborative Lab in SK Inc.
Please feel free to contact me if you have any question on the dataset.
I'm best reached via [email](mailto:taes.hahn@gmail.com).
#### Changelog
- [29 June 2023] New format added: `vicuna` format with the same translation is now available.
- [16 June 2023] Enhanced Translation: keep the text enclosed in special characters, e.g. `$` and `` ` ``, intact without translation.
- [14 June 2023] First upload.
### Usage
```python
>>> from datasets import load_dataset
>>> ko_lima = load_dataset('taeshahn/ko-lima', 'plain') # or load_dataset('taeshahn/ko-lima')
>>> ko_lima_vicuna = load_dataset('taeshahn/ko-lima', 'vicuna')
```
```python
>>> ko_lima['train'][1025]
{
'conversations': [
'저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?',
'항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.',
'인용된 자료의 출처는 무엇인가요?',
'출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.'
],
'source': 'multi_turn'
}
```
```python
>>> ko_lima_vicuna['train'][1025]
{
'id': '1026-multi_turn',
'conversations': [
{
'from': '사람',
'value': '저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?'
},
{
'from': '어시스턴트',
'value': '항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.'
},
{
'from': '사람',
'value': '인용된 자료의 출처는 무엇인가요?'
},
{
'from': '어시스턴트',
'value': '출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.'
}
]
}
```
### Citation Information
```
@InProceedings{kolimadataset,
title = {KoLIMA: Korean LIMA Dataset for Efficient Instruction-tuning},
author = {Hahn, Taeseung},
year = {2023}
}
```
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zhen-dong-nexusflow/reformatted_singleapi | zhen-dong-nexusflow | 2023-10-23T22:19:11Z | 394 | 0 | null | [
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clarin-pl/cst-wikinews | clarin-pl | 2021-07-12T18:51:43Z | 393 | 2 | null | [
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izumi-lab/wikipedia-ja-20230720 | izumi-lab | 2023-07-29T03:05:36Z | 393 | 2 | null | [
"language:ja",
"license:cc-by-sa-3.0",
"region:us"
] | 2023-07-29T03:05:36Z | 2023-07-28T02:11:33.000Z | 2023-07-28T02:11:33 | ---
dataset_info:
features:
- name: curid
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3653518687
num_examples: 1362415
download_size: 2130533065
dataset_size: 3653518687
license: cc-by-sa-3.0
language:
- ja
---
# Dataset Card for "wikipedia-ja-20230720"
[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 | |
KATANABRAVE/stories | KATANABRAVE | 2023-08-25T06:37:13Z | 393 | 0 | null | [
"license:llama2",
"region:us"
] | 2023-08-25T06:37:13Z | 2023-08-25T03:49:19.000Z | 2023-08-25T03:49:19 | ---
license: llama2
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: title
dtype: string
- name: article
dtype: string
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 110879624
num_examples: 8500
- name: validation
num_bytes: 3383807
num_examples: 277
download_size: 48437278
dataset_size: 114263431
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
victorcosta/pt_legislation | victorcosta | 2023-11-28T23:38:21Z | 393 | 0 | null | [
"language:pt",
"region:us"
] | 2023-11-28T23:38:21Z | 2023-10-23T19:06:23.000Z | 2023-10-23T19:06:23 | ---
language: pt
configs:
- config_name: ria_ground_truth
data_files: ria_ground_truth/train.parquet
version: 1.0.3
- config_name: unlabeled
data_files: unlabeled/train.parquet
version: 1.0.0
---
# Portuguese Legislation dataset
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cornell_movie_dialog | null | 2023-04-05T10:02:37Z | 391 | 12 | cornell-movie-dialogs-corpus | [
"language:en",
"region:us"
] | 2023-04-05T10:02:37Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: cornell-movie-dialogs-corpus
pretty_name: Cornell Movie-Dialogs Corpus
dataset_info:
features:
- name: movieID
dtype: string
- name: movieTitle
dtype: string
- name: movieYear
dtype: string
- name: movieIMDBRating
dtype: string
- name: movieNoIMDBVotes
dtype: string
- name: movieGenres
sequence: string
- name: characterID1
dtype: string
- name: characterID2
dtype: string
- name: characterName1
dtype: string
- name: characterName2
dtype: string
- name: utterance
sequence:
- name: text
dtype: string
- name: LineID
dtype: string
splits:
- name: train
num_bytes: 19548840
num_examples: 83097
download_size: 9916637
dataset_size: 19548840
---
# Dataset Card for "cornell_movie_dialog"
## 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:** [http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html](http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 9.92 MB
- **Size of the generated dataset:** 19.55 MB
- **Total amount of disk used:** 29.46 MB
### Dataset Summary
This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:
- 220,579 conversational exchanges between 10,292 pairs of movie characters
- involves 9,035 characters from 617 movies
- in total 304,713 utterances
- movie metadata included:
- genres
- release year
- IMDB rating
- number of IMDB votes
- IMDB rating
- character metadata included:
- gender (for 3,774 characters)
- position on movie credits (3,321 characters)
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 9.92 MB
- **Size of the generated dataset:** 19.55 MB
- **Total amount of disk used:** 29.46 MB
An example of 'train' looks as follows.
```
{
"characterID1": "u0 ",
"characterID2": " u2 ",
"characterName1": " m0 ",
"characterName2": " m0 ",
"movieGenres": ["comedy", "romance"],
"movieID": " m0 ",
"movieIMDBRating": " 6.90 ",
"movieNoIMDBVotes": " 62847 ",
"movieTitle": " f ",
"movieYear": " 1999 ",
"utterance": {
"LineID": ["L1"],
"text": ["L1 "]
}
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `movieID`: a `string` feature.
- `movieTitle`: a `string` feature.
- `movieYear`: a `string` feature.
- `movieIMDBRating`: a `string` feature.
- `movieNoIMDBVotes`: a `string` feature.
- `movieGenres`: a `list` of `string` features.
- `characterID1`: a `string` feature.
- `characterID2`: a `string` feature.
- `characterName1`: a `string` feature.
- `characterName2`: a `string` feature.
- `utterance`: a dictionary feature containing:
- `text`: a `string` feature.
- `LineID`: a `string` feature.
### Data Splits
| name |train|
|-------|----:|
|default|83097|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{Danescu-Niculescu-Mizil+Lee:11a,
author={Cristian Danescu-Niculescu-Mizil and Lillian Lee},
title={Chameleons in imagined conversations:
A new approach to understanding coordination of linguistic style in dialogs.},
booktitle={Proceedings of the
Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011},
year={2011}
}
```
### Contributions
Thanks to [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gutenberg_time | null | 2022-11-03T16:32:34Z | 391 | 3 | gutenberg-time-dataset | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2011.04124",
"region:us"
] | 2022-11-03T16:32:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: gutenberg-time-dataset
pretty_name: the Gutenberg Time dataset
dataset_info:
features:
- name: guten_id
dtype: string
- name: hour_reference
dtype: string
- name: time_phrase
dtype: string
- name: is_ambiguous
dtype: bool_
- name: time_pos_start
dtype: int64
- name: time_pos_end
dtype: int64
- name: tok_context
dtype: string
config_name: gutenberg
splits:
- name: train
num_bytes: 108550391
num_examples: 120694
download_size: 35853781
dataset_size: 108550391
---
# Dataset Card for the Gutenberg Time dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Repository](https://github.com/allenkim/what-time-is-it)**
- **[Paper](https://arxiv.org/abs/2011.04124)**
### Dataset Summary
A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Time-of-the-day classification from excerpts.
## Dataset Structure
### Data Instances
```
{
"guten_id": 28999,
"hour_reference": 12,
"time_phrase": "midday",
"is_ambiguous": False,
"time_pos_start": 133,
"time_pos_end": 134,
"tok_context": "Sorrows and trials she had had in plenty in her life , but these the sweetness of her nature had transformed , so that from being things difficult to bear , she had built up with them her own character . Sorrow had increased her own power of sympathy ; out of trials she had learnt patience ; and failure and the gradual sinking of one she had loved into the bottomless slough of evil habit had but left her with an added dower of pity and tolerance . So the past had no sting left , and if iron had ever entered into her soul it now but served to make it strong . She was still young , too ; it was not near sunset with her yet , nor even midday , and the future that , humanly speaking , she counted to be hers was almost dazzling in its brightness . For love had dawned for her again , and no uncertain love , wrapped in the mists of memory , but one that had ripened through liking and friendship and intimacy into the authentic glory . He was in England , too ; she was going back to him . And before very long she would never go away from him again ."
}
```
### Data Fields
```
guten_id - Gutenberg ID number
hour_reference - hour from 0 to 23
time_phrase - the phrase corresponding to the referenced hour
is_ambiguous - boolean whether it is clear whether time is AM or PM
time_pos_start - token position where time_phrase begins
time_pos_end - token position where time_phrase ends (exclusive)
tok_context - context in which time_phrase appears as space-separated tokens
```
### Data Splits
No data splits.
## Dataset Creation
### Curation Rationale
The flow of time is an indispensable guide for our actions, and provides a framework in which to see a logical progression of events. Just as in real life,the clock provides the background against which literary works play out: when characters wake, eat,and act. In most works of fiction, the events of the story take place during recognizable time periods over the course of the day. Recognizing a story’s flow through time is essential to understanding the text.In this paper, we try to capture the flow of time through novels by attempting to recognize what time of day each event in the story takes place at.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Novel authors.
### Annotations
#### Annotation process
Manually annotated.
#### Who are the annotators?
Two of the authors.
### Personal and Sensitive Information
No Personal or sensitive information.
## 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
Allen Kim, Charuta Pethe and Steven Skiena, Stony Brook University
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{kim2020time,
title={What time is it? Temporal Analysis of Novels},
author={Allen Kim and Charuta Pethe and Steven Skiena},
year={2020},
eprint={2011.04124},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset. | [
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mwsc | null | 2023-04-05T13:33:22Z | 391 | 0 | null | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|winograd_wsc",
"language:en",
"license:cc-by-4.0",
"arxiv:1806.08730",
"region:us"
] | 2023-04-05T13:33:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Modified Winograd Schema Challenge (MWSC)
size_categories:
- n<1K
source_datasets:
- extended|winograd_wsc
task_categories:
- multiple-choice
task_ids:
- multiple-choice-coreference-resolution
paperswithcode_id: null
dataset_info:
features:
- name: sentence
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 11022
num_examples: 80
- name: test
num_bytes: 15220
num_examples: 100
- name: validation
num_bytes: 13109
num_examples: 82
download_size: 19197
dataset_size: 39351
---
# Dataset Card for The modified Winograd Schema Challenge (MWSC)
## 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:** [http://decanlp.com](http://decanlp.com)
- **Repository:** https://github.com/salesforce/decaNLP
- **Paper:** [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730)
- **Point of Contact:** [Bryan McCann](mailto:bmccann@salesforce.com), [Nitish Shirish Keskar](mailto:nkeskar@salesforce.com)
- **Size of downloaded dataset files:** 19.20 kB
- **Size of the generated dataset:** 39.35 kB
- **Total amount of disk used:** 58.55 kB
### Dataset Summary
Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
This Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.04 MB
- **Total amount of disk used:** 0.06 MB
An example looks as follows:
```
{
"sentence": "The city councilmen refused the demonstrators a permit because they feared violence.",
"question": "Who feared violence?",
"options": [ "councilmen", "demonstrators" ],
"answer": "councilmen"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `sentence`: a `string` feature.
- `question`: a `string` feature.
- `options`: a `list` of `string` features.
- `answer`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 80| 82| 100|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
Our code for running decaNLP has been open sourced under BSD-3-Clause.
We chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.
From the [Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html):
> Both versions of the collections are licenced under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
If you use this in your work, please cite:
```
@article{McCann2018decaNLP,
title={The Natural Language Decathlon: Multitask Learning as Question Answering},
author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:1806.08730},
year={2018}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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allegro/klej-cbd | allegro | 2021-11-29T19:14:20Z | 391 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
rcds/wikipedia-for-mask-filling | rcds | 2023-03-08T12:22:02Z | 391 | 0 | null | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-03-08T12:22:02Z | 2023-01-23T15:14:48.000Z | 2023-01-23T15:14:48 | ---
annotations_creators:
- other
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "wikipedia pages chunked for fill-mask"
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
---
# preprocessed version of rcds/wikipedia-persons-masked
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Contains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text
is masked with a <mask> token. The ground truth for every mask is provided.
Each row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk.
for each chunk the expected name for each mask is given.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering,
e.g. "Who is <mask>?"
### Languages
*english only*
## Dataset Structure
In /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/wikipedia-persons-masked', split='train', type='original', size='512')
```
### Data Fields
Columns are:
- texts: the text chunks
- masks: the names for each of the masks in the chunks
### Data Splits
There are no splits, only a default train.
## Dataset Creation
Created by using the tokenizer from allenai/longformer-base-4096 for the 4096 token per chunk version,
and the xml-roberta-large tokenizer for the 512 token version. Chunks are split to fit those token sizes,
with the splits ensuring no words are split in half.
Possible improvements: Last chunk of a page might be much shorter, could join part of the previous one to have more tokens
in the last chunk.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
TODO add citation
```
### Contributions
Thanks to [@skatinger](https://github.com/skatinger) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
multi_nli_mismatch | null | 2023-04-05T10:10:18Z | 390 | 1 | multinli | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"license:cc-by-sa-3.0",
"license:mit",
"license:other",
"region:us"
] | 2023-04-05T10:10:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
license_details: Open Portion of the American National Corpus
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: multinli
pretty_name: Multi-Genre Natural Language Inference
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 75601459
num_examples: 392702
- name: validation
num_bytes: 2009444
num_examples: 10000
download_size: 226850426
dataset_size: 77610903
---
# Dataset Card for Multi-Genre Natural Language Inference (Mismatched only)
## 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.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 77.62 MB
- **Total amount of disk used:** 304.46 MB
### Dataset Summary
The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre generalization evaluation. The corpus served as the
basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 77.62 MB
- **Total amount of disk used:** 304.46 MB
An example of 'train' looks as follows.
```
{
"hypothesis": "independence",
"label": "contradiction",
"premise": "correlation"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a `string` feature.
### Data Splits
| name |train |validation|
|----------|-----:|---------:|
|plain_text|392702| 10000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{N18-1101,
author = "Williams, Adina
and Nangia, Nikita
and Bowman, Samuel",
title = "A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference",
booktitle = "Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1112--1122",
location = "New Orleans, Louisiana",
url = "http://aclweb.org/anthology/N18-1101"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. | [
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lmqg/qg_jaquad | lmqg | 2022-12-02T18:51:27Z | 390 | 4 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:SkelterLabsInc/JaQuAD",
"language:ja",
"license:cc-by-sa-3.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2022-12-02T18:51:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: cc-by-sa-3.0
pretty_name: JaQuAD for question generation
language: ja
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: SkelterLabsInc/JaQuAD
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qg_jaquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
This is [JaQuAD](https://github.com/SkelterLabsInc/JaQuAD) dataset compiled for question generation (QG) task. The test set of the original
data is not publicly released, so we randomly sampled test questions from the training set. There are no overlap in terms of the paragraph across train, test, and validation split.
### Supported Tasks and Leaderboards
* `question-generation`: The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Japanese (ja)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"question": "新型車両として6000系が構想されたのは、製造費用のほか、どんな費用を抑えるためだったの?",
"paragraph": "三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。",
"answer": "保守費用",
"sentence": "三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。",
"paragraph_sentence": "<hl>三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、保守費用を抑えた新型車両として6000系が構想された。<hl>新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。",
"paragraph_answer": "三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、<hl>保守費用<hl>を抑えた新型車両として6000系が構想された。新宿線建設に際してはすでに1号線(後の浅草線)を1,435mm軌間で開業させていた東京都は京成電鉄と1号線との乗り入れにあたり京成電鉄の路線を1,372mmから1,435mmに改軌させた事例や、1,372mm軌間の特殊性から運輸省(当時、2001年から国土交通省)と共に京王にも改軌を求めたが、改軌工事中の輸送力確保が困難なことを理由に改軌しないことで決着している。",
"sentence_answer": "三多摩地区開発による沿線人口の増加、相模原線延伸による多摩ニュータウン乗り入れ、都営地下鉄10号線(現都営地下鉄新宿線、以下新宿線と表記する)乗入構想により、京王線の利用客増加が見込まれ、相当数の車両を準備する必要に迫られるなか、製造費用、<hl>保守費用<hl>を抑えた新型車両として6000系が構想された。"
}
```
The data fields are the same among all splits.
- `question`: a `string` feature.
- `paragraph`: a `string` feature.
- `answer`: a `string` feature.
- `sentence`: a `string` feature.
- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
`paragraph_sentence` feature is for sentence-aware question generation.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|27809| 3939| 3939|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
IlyaGusev/ru_turbo_alpaca_evol_instruct | IlyaGusev | 2023-06-02T11:19:37Z | 390 | 9 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"region:us"
] | 2023-06-02T11:19:37Z | 2023-05-20T23:43:27.000Z | 2023-05-20T23:43:27 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: iteration
dtype: uint32
splits:
- name: train
num_bytes: 105428021
num_examples: 47793
download_size: 27572163
dataset_size: 105428021
license: cc-by-4.0
task_categories:
- text-generation
language:
- ru
size_categories:
- 10K<n<100K
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
flexthink/librig2p-nostress-space | flexthink | 2022-06-24T01:23:49Z | 389 | 0 | null | [
"region:us"
] | 2022-06-24T01:23:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # librig2p-nostress - Grapheme-To-Phoneme Dataset
This dataset contains samples that can be used to train a Grapheme-to-Phoneme system **without** stress information.
The dataset is derived from the following pre-existing datasets:
* [LibriSpeech ASR Corpus](https://www.openslr.org/12)
* [LibriSpeech Alignments](https://github.com/CorentinJ/librispeech-alignments)
* [Wikipedia Homograph Disambiguation Data](https://github.com/google/WikipediaHomographData) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ghadeermobasher/BC5CDR-Chemical-Disease | ghadeermobasher | 2022-01-25T10:31:51Z | 389 | 4 | null | [
"region:us"
] | 2022-01-25T10:31:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
paperswithcode_id: bc4chemd
pretty_name: BC4CHEMD
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
# Dataset Card for BC4CHEMD
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:**
https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/
- **Repository:** https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD
- **Paper:** BioCreative V CDR task corpus: a resource for chemical disease relation extraction
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Zhiyong Lu] (mailto: Zhiyong.Lu@nih.gov)
### Dataset Summary
A corpus for both named entity recognition and chemical-disease relations in the literature. A total of 1500 articles have been annotated with automated assistance from PubTator. Jaccard agreement results and corpus statistics verified the reliability of the corpus.
### Supported Tasks and Leaderboards
named-entity-recognition
### Languages
en
## Dataset Structure
### Data Instances
Instances of the dataset contain an array of `tokens`, `ner_tags` and an `id`. An example of an instance of the dataset:
{
'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],
'id': '0'
}
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a chemical and `2` the subsequent chemical tokens.
### Data Splits
The data is split into a train (3500 instances), validation (3500 instances) and test set (3000 instances).
## Dataset Creation
### Curation Rationale
The goal of the dataset consists on improving the state-of-the-art in chemical name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.
### Source Data
#### Initial Data Collection and Normalization
The dataset consists on abstracts extracted from PubMed.
#### Who are the source language producers?
The source language producers are the authors of publication abstracts hosted in PubMed.
### Annotations
#### Annotation process
The curators were trained to mark up the text according to the labels specified in the guidelines. The raw text was not tokenized prior to the annotation and only the title was distinguished from the PubMed abstract. The selection of text spans was done at the character level, they did not allow nested annotations and distinct entity mentions should not overlap. Each text span was selected according to the annotation guidelines and classified manually into one of the CEM classes.
#### Who are the annotators?
The group of curators used for preparing the annotations was composed mainly of organic chemistry postgraduates with an average experience of 3-4 years in the annotation of chemical names and chemical structures.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets.
### Discussion of Biases
The used CHEMDNER document set had to be representative and balanced in order to reflect the kind of documents that might mention the entity of interest.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | [
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eduagarcia/OSCAR-2301-pt_dedup | eduagarcia | 2023-08-28T16:55:02Z | 389 | 0 | null | [
"region:us"
] | 2023-08-28T16:55:02Z | 2023-08-27T23:52:48.000Z | 2023-08-27T23:52:48 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 61846407893
num_examples: 10888966
download_size: 28809168123
dataset_size: 61846407893
---
# Dataset Card for "OSCAR-2301_dedup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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code_x_glue_cc_clone_detection_poj104 | null | 2023-03-13T11:02:07Z | 388 | 3 | null | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | 2023-03-13T11:02:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: CodeXGlueCcCloneDetectionPoj104
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
dataset_info:
features:
- name: id
dtype: int32
- name: code
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 20179075
num_examples: 32500
- name: validation
num_bytes: 6382433
num_examples: 8500
- name: test
num_bytes: 7227506
num_examples: 12000
download_size: 8658581
dataset_size: 33789014
---
# Dataset Card for "code_x_glue_cc_clone_detection_poj_104"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104
### Dataset Summary
CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104
Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task.
### Supported Tasks and Leaderboards
- `document-retrieval`: The dataset can be used to train a model for retrieving top-k codes with the same semantics.
### Languages
- C++ **programming** language
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"code": "\nint f(int shu,int min)\n{ \n int k=1;\n if(shu < min)\n { \n k= 0; \n return k;\n } \n else\n {\n for(int i = min;i<shu;i++)\n { \n if(shu%i == 0)\n { \n k=k+ f(shu/i,i); \n } \n \n \n } \n return k; \n}\n} \n\nmain()\n{\n int n,i,a;\n scanf(\"%d\",&n);\n \n for(i=0;i<n;i++)\n {\n scanf(\"%d\",&a);\n \n if(i!=n-1) \n printf(\"%d\\n\",f(a,2));\n else\n printf(\"%d\",f(a,2)); \n \n \n \n } \n \n \n }",
"id": 0,
"label": "home"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### default
|field name| type | description |
|----------|------|----------------------------------------------|
|id |int32 | Index of the sample |
|code |string| The full text of the function |
|label |string| The id of problem that the source code solves|
### Data Splits
| name |train|validation|test |
|-------|----:|---------:|----:|
|default|32000| 8000|12000|
## 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
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@inproceedings{mou2016convolutional,
title={Convolutional neural networks over tree structures for programming language processing},
author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
pages={1287--1293},
year={2016}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | [
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SophieTr/reddit_clean | SophieTr | 2022-08-13T20:26:31Z | 388 | 3 | null | [
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jamescalam/ai-arxiv-chunked | jamescalam | 2023-10-10T12:56:09Z | 388 | 17 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
imvladikon/hebrew_speech_kan | imvladikon | 2023-05-05T09:12:15Z | 387 | 3 | null | [
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:he",
"region:us"
] | 2023-05-05T09:12:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
task_categories:
- automatic-speech-recognition
language:
- he
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1569850175.0
num_examples: 8000
- name: validation
num_bytes: 394275049.0
num_examples: 2000
download_size: 1989406585
dataset_size: 1964125224.0
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Hebrew Dataset for ASR
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```json
{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/8ce7402f6482c6053251d7f3000eec88668c994beb48b7ca7352e77ef810a0b6/train/e429593fede945c185897e378a5839f4198.wav',
'array': array([-0.00265503, -0.0018158 , -0.00149536, ..., -0.00135803,
-0.00231934, -0.00190735]),
'sampling_rate': 16000},
'sentence': 'היא מבינה אותי יותר מכל אחד אחר'}
```
### Data Fields
[More Information Needed]
### Data Splits
| | train | validation |
| ---- | ----- | ---------- |
| number of samples | 8000 | 2000 |
| hours | 6.92 | 1.73 |
## Dataset Creation
### Curation Rationale
scraped data from youtube (channel כאן) with removing outliers (by length and ratio between length of the audio and sentences)
### Source Data
#### Initial Data Collection and Normalization
#### 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
```
@misc{imvladikon2022hebrew_speech_kan,
author = {Gurevich, Vladimir},
title = {Hebrew Speech Recognition Dataset: Kan},
year = {2022},
howpublished = \url{https://huggingface.co/datasets/imvladikon/hebrew_speech_kan},
}
```
### Contributions
[More Information Needed] | [
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juletxara/xquad_xtreme | juletxara | 2022-10-12T08:43:41Z | 387 | 6 | xquad | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:en",
"language:es",
"language:de",
"language:el",
"language:hi",
"language:th",
"language:ru",
"language:tr",
"language:ar",
"language:vi",
"language:zh",
"language:ro",
"license:cc-by-sa-4.0",
"arxiv:1910.11856",
"region:us"
] | 2022-10-12T08:43:41Z | 2022-05-30T10:49:17.000Z | 2022-05-30T10:49:17 | ---
pretty_name: XQuAD-XTREME
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- es
- de
- el
- hi
- th
- ru
- tr
- ar
- vi
- zh
- ro
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: xquad
---
# Dataset Card for XQuAD-XTREME
## 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/deepmind/xquad](https://github.com/deepmind/xquad)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 139.53 MB
- **Size of the generated dataset:** 18.09 MB
- **Total amount of disk used:** 157.62 MB
### Dataset Summary
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten language: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages.
We also include "translate-train", "translate-dev", and "translate-test"
splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings. https://proceedings.mlr.press/v119/hu20b/hu20b.pdf
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### ar
- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 1.64 MB
- **Total amount of disk used:** 14.33 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
#### de
- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 1.23 MB
- **Total amount of disk used:** 13.91 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
#### el
- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 2.11 MB
- **Total amount of disk used:** 14.79 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
#### en
- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 1.07 MB
- **Total amount of disk used:** 13.75 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
#### es
- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 1.22 MB
- **Total amount of disk used:** 13.90 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
### Data Fields
The data fields are the same among all splits.
#### ar
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### de
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### el
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### en
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### es
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | validation |
| -------- | ---------: |
| ar | 1190 |
| de | 1190 |
| el | 1190 |
| en | 1190 |
| es | 1190 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.11856}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pythainlp/final_training_set_v1 | pythainlp | 2023-04-29T07:06:04Z | 387 | 1 | null | [
"task_categories:conversational",
"task_categories:text-generation",
"language:en",
"region:us"
] | 2023-04-29T07:06:04Z | 2023-04-13T16:52:49.000Z | 2023-04-13T16:52:49 | ---
dataset_info:
features:
- name: text
dtype: string
- name: metadata
struct:
- name: source
dtype: string
- name: nb_token
dtype: int64
splits:
- name: train
num_bytes: 337155434.9768474
num_examples: 405760
- name: test
num_bytes: 1277960.0231525812
num_examples: 1538
download_size: 191404581
dataset_size: 338433395
task_categories:
- conversational
- text-generation
language:
- en
---
# Dataset Card for "final_training_set_v1"
Finetuning datasets for [WangChanGLM](https://github.com/pythainlp/wangchanglm) sourced from [LAION OIG chip2 and infill_dbpedia](https://huggingface.co/datasets/laion/OIG) ([Apache-2.0](https://github.com/pythainlp/wangchanglm/blob/main/LICENSE)), [DataBricks Dolly v2](https://github.com/databrickslabs/dolly) ([Apache-2.0](https://github.com/pythainlp/wangchanglm/blob/main/LICENSE)), [OpenAI TL;DR](https://github.com/openai/summarize-from-feedback) ([MIT](https://opensource.org/license/mit/)), and [Hello-SimpleAI HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) ([CC-BY SA](https://creativecommons.org/licenses/by-sa/4.0/)) | [
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Icaruas/Instruct_clean | Icaruas | 2023-11-18T03:32:47Z | 387 | 0 | null | [
"region:us"
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newspop | null | 2022-11-03T16:31:06Z | 386 | 2 | null | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"social-media-shares-prediction",
"arxiv:1801.07055",
"region:us"
] | 2022-11-03T16:31:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: News Popularity in Multiple Social Media Platforms
tags:
- social-media-shares-prediction
dataset_info:
features:
- name: id
dtype: int32
- name: title
dtype: string
- name: headline
dtype: string
- name: source
dtype: string
- name: topic
dtype: string
- name: publish_date
dtype: string
- name: facebook
dtype: int32
- name: google_plus
dtype: int32
- name: linked_in
dtype: int32
splits:
- name: train
num_bytes: 27927641
num_examples: 93239
download_size: 30338277
dataset_size: 27927641
---
# Dataset Card for News Popularity in Multiple Social Media Platforms
## 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:** [UCI](https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms)
- **Repository:**
- **Paper:** [Arxiv](https://arxiv.org/abs/1801.07055)
- **Leaderboard:** [Kaggle](https://www.kaggle.com/nikhiljohnk/news-popularity-in-multiple-social-media-platforms/code)
- **Point of Contact:**
### Dataset Summary
Social sharing data across Facebook, Google+ and LinkedIn for 100k news items on the topics of: economy, microsoft, obama and palestine.
### Supported Tasks and Leaderboards
Popularity prediction/shares prediction
### Languages
English
## Dataset Structure
### Data Instances
```
{ "id": 35873,
"title": "Microsoft's 'teen girl' AI turns into a Hitler-loving sex robot within 24 ...",
"headline": "Developers at Microsoft created 'Tay', an AI modelled to speak 'like a teen girl', in order to improve the customer service on their voice",
"source": "Telegraph.co.uk",
"topic": "microsoft",
"publish_date": "2016-03-24 09:53:54",
"facebook": 22346,
"google_plus": 973,
"linked_in": 1009
}
```
### Data Fields
- id: the sentence id in the source dataset
- title: the title of the link as shared on social media
- headline: the headline, or sometimes the lede of the story
- source: the source news site
- topic: the topic: one of "economy", "microsoft", "obama" and "palestine"
- publish_date: the date the original article was published
- facebook: the number of Facebook shares, or -1 if this data wasn't collected
- google_plus: the number of Google+ likes, or -1 if this data wasn't collected
- linked_in: the number of LinkedIn shares, or -1 if if this data wasn't collected
### Data Splits
None
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
The source headlines were by journalists, while the titles were written by the
people sharing it on social media.
### Annotations
#### Annotation process
The 'annotations' are simply the number of shares, or likes in the case of
Google+ as collected from various API endpoints.
#### Who are the annotators?
Social media users.
### 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: Creative Commons Attribution 4.0 International License (CC-BY)
### Citation Information
```
@article{Moniz2018MultiSourceSF,
title={Multi-Source Social Feedback of Online News Feeds},
author={N. Moniz and L. Torgo},
journal={ArXiv},
year={2018},
volume={abs/1801.07055}
}
```
### Contributions
Thanks to [@frankier](https://github.com/frankier) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TurkuNLP/register_oscar | TurkuNLP | 2023-09-25T11:30:50Z | 386 | 5 | null | [
"region:us"
] | 2023-09-25T11:30:50Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Dataset Card for register_oscar
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
### Dataset Summary
The Register Oscar dataset is a multilingual dataset, containing languaegs from the Oscar dataset that have been tagged with register information.
8 main-level registers:
* Narrative (NA)
* Informational Description (IN)
* Opinion (OP)
* Interactive Discussion (ID)
* How-to/Instruction (HI)
* Informational Persuasion (IP)
* Lyrical (LY)
* Spoken (SP)
For further description of the labels, see (Douglas Biber and Jesse Egbert. 2018. Register variation online)
Code used to tag Register Oscar can be found at https://github.com/TurkuNLP/register-labeling
### Languages
Currently contains the following languages: Arabic, Bengali, Catalan, English, Spanish, Basque, French, Hindi, Indonesian, Portuguese, Swahili, Urdu, Vietnamese and Chinese.
For further information on the languages and data, see https://huggingface.co/datasets/oscar
## Dataset Structure
### Data Instances
```
{"id": "0", "labels": ["NA"], "text": "Zarif: Iran inajua mpango wa Saudia wa kufanya mauaji ya kigaidi dhidi ya maafisa wa ngazi za juu wa Iran\n"}
```
### Data Fields
* id: unique id of the document (from the Oscar dataset)
* labels: the list of labels assigned to the text
* text: the original text of the document (as appears in the Oscar dataset)
### Citing
```
@inproceedings{laippala-etal-2022-towards,
title = "Towards better structured and less noisy Web data: Oscar with Register annotations",
author = {Laippala, Veronika and
Salmela, Anna and
R{\"o}nnqvist, Samuel and
Aji, Alham Fikri and
Chang, Li-Hsin and
Dhifallah, Asma and
Goulart, Larissa and
Kortelainen, Henna and
P{\`a}mies, Marc and
Prina Dutra, Deise and
Skantsi, Valtteri and
Sutawika, Lintang and
Pyysalo, Sampo},
booktitle = "Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wnut-1.23",
pages = "215--221",
abstract = {Web-crawled datasets are known to be noisy, as they feature a wide range of language use covering both user-generated and professionally edited content as well as noise originating from the crawling process. This article presents one solution to reduce this noise by using automatic register (genre) identification -whether the texts are, e.g., forum discussions, lyrical or how-to pages. We apply the multilingual register identification model by R{\"o}nnqvist et al. (2021) and label the widely used Oscar dataset. Additionally, we evaluate the model against eight new languages, showing that the performance is comparable to previous findings on a restricted set of languages. Finally, we present and apply a machine learning method for further cleaning text files originating from Web crawls from remains of boilerplate and other elements not belonging to the main text of the Web page. The register labeled and cleaned dataset covers 351 million documents in 14 languages and is available at https://huggingface.co/datasets/TurkuNLP/register{\_}oscar.},
}
``` | [
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allegro/klej-nkjp-ner | allegro | 2021-11-29T19:14:56Z | 386 | 0 | null | [
"region:us"
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FreedomIntelligence/alpaca-gpt4-deutsch | FreedomIntelligence | 2023-08-06T08:08:37Z | 386 | 3 | null | [
"license:apache-2.0",
"region:us"
] | 2023-08-06T08:08:37Z | 2023-06-26T08:17:40.000Z | 2023-06-26T08:17:40 | ---
license: apache-2.0
---
The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
natyou/freshqa_10_06 | natyou | 2023-10-11T15:26:10Z | 386 | 0 | null | [
"region:us"
] | 2023-10-11T15:26:10Z | 2023-10-11T15:23:22.000Z | 2023-10-11T15:23:22 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: id
dtype: int64
- name: split
dtype: string
- name: question
dtype: string
- name: effective_year
dtype: string
- name: next_review
dtype: string
- name: false_premise
dtype: bool
- name: num_hops
dtype: string
- name: fact_type
dtype: string
- name: source
dtype: string
- name: answer_0
dtype: string
- name: answer_1
dtype: string
- name: answer_2
dtype: string
- name: answer_3
dtype: string
- name: answer_4
dtype: string
- name: answer_5
dtype: string
- name: answer_6
dtype: string
- name: answer_7
dtype: string
- name: answer_8
dtype: string
- name: answer_9
dtype: string
- name: note
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: test
num_bytes: 192891
num_examples: 500
- name: dev
num_bytes: 39203
num_examples: 100
download_size: 129810
dataset_size: 232094
---
# Dataset Card for "freshqa_10_06"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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bookcorpusopen | null | 2023-11-24T14:42:08Z | 385 | 26 | bookcorpus | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2105.05241",
"region:us"
] | 2023-11-24T14:42:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BookCorpusOpen
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: bookcorpus
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 6643435392
num_examples: 17868
download_size: 2404269430
dataset_size: 6643435392
viewer: false
---
# Dataset Card for BookCorpusOpen
## 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/soskek/bookcorpus/issues/27](https://github.com/soskek/bookcorpus/issues/27)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 2.40 GB
- **Size of the generated dataset:** 6.64 GB
- **Total amount of disk used:** 9.05 GB
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Defunct:</b> Dataset "bookcorpusopen" is defunct and no longer accessible due to unavailability of the source data.</p>
</div>
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.
This version of bookcorpus has 17868 dataset items (books). Each item contains two fields: title and text. The title is the name of the book (just the file name) while text contains unprocessed book text. The bookcorpus has been prepared by Shawn Presser and is generously hosted by The-Eye. The-Eye is a non-profit, community driven platform dedicated to the archiving and long-term preservation of any and all data including but by no means limited to... websites, books, games, software, video, audio, other digital-obscura and ideas.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 2.40 GB
- **Size of the generated dataset:** 6.64 GB
- **Total amount of disk used:** 9.05 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "\"\\n\\nzONE\\n\\n## The end and the beginning\\n\\nby\\n\\nPhilip F. Blood\\n\\nSMASHWORDS EDITION\\n\\nVersion 3.55\\n\\nPUBLISHED BY:\\n\\nPhi...",
"title": "zone-the-end-and-the-beginning.epub.txt"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `title`: a `string` feature.
- `text`: a `string` feature.
### Data Splits
| name |train|
|----------|----:|
|plain_text|17868|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The books have been crawled from smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information.
A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241)
### Citation Information
```
@InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}
```
### Contributions
Thanks to [@vblagoje](https://github.com/vblagoje) for adding this dataset. | [
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sem_eval_2014_task_1 | null | 2023-01-25T14:43:53Z | 385 | 1 | null | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-01-25T14:43:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions
task_categories:
- text-classification
task_ids:
- text-scoring
- natural-language-inference
- semantic-similarity-scoring
pretty_name: SemEval 2014 - Task 1
dataset_info:
features:
- name: sentence_pair_id
dtype: int64
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: relatedness_score
dtype: float32
- name: entailment_judgment
dtype:
class_label:
names:
'0': NEUTRAL
'1': ENTAILMENT
'2': CONTRADICTION
splits:
- name: train
num_bytes: 540296
num_examples: 4500
- name: test
num_bytes: 592320
num_examples: 4927
- name: validation
num_bytes: 60981
num_examples: 500
download_size: 197230
dataset_size: 1193597
---
# Dataset Card for SemEval 2014 - Task 1
## 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:** [SemEval-2014 Task 1](https://alt.qcri.org/semeval2014/task1/)
- **Repository:**
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/S14-2001/)
- **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 [@ashmeet13](https://github.com/ashmeet13) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
imvladikon/hebrew_speech_coursera | imvladikon | 2023-05-05T09:05:00Z | 384 | 5 | null | [
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:he",
"region:us"
] | 2023-05-05T09:05:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
task_categories:
- automatic-speech-recognition
language:
- he
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 6670706136.352
num_examples: 20306
- name: validation
num_bytes: 1648062261.28
num_examples: 5076
download_size: 7726933856
dataset_size: 8318768397.632
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
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- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```json
{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/89efd3a0fa3ead3f0b8e432e8796697a738d4561b24ff91f4fb2cc25d86e9fb0/train/ccef55189b7843d49110228cb0a71bfa115.wav',
'array': array([-0.01217651, -0.04351807, -0.06278992, ..., -0.00018311,
-0.00146484, -0.00349426]),
'sampling_rate': 16000},
'sentence': 'מצד אחד ובתנועה הציונית הצעירה'}
```
### Data Fields
[More Information Needed]
### Data Splits
| | train | validation |
| ---- | ----- | ---------- |
| number of samples | 20306 | 5076 |
| hours | 28.88 | 7.23 |
## 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
```
@misc{imvladikon2022hebrew_speech_coursera,
author = {Gurevich, Vladimir},
title = {Hebrew Speech Recognition Dataset: Coursera},
year = {2022},
howpublished = \url{https://huggingface.co/datasets/imvladikon/hebrew_speech_coursera},
}
```
### Contributions
[More Information Needed] | [
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distilled-from-one-sec-cv12/chunk_230 | distilled-from-one-sec-cv12 | 2023-05-29T00:50:28Z | 384 | 0 | null | [
"region:us"
] | 2023-05-29T00:50:28Z | 2023-05-29T00:49:19.000Z | 2023-05-29T00:49:19 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1017249644
num_examples: 198217
download_size: 1037402846
dataset_size: 1017249644
---
# Dataset Card for "chunk_230"
[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 | |
maf917/dzdzdz | maf917 | 2023-11-03T05:49:51Z | 384 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-03T05:49:51Z | 2023-11-03T05:47:44.000Z | 2023-11-03T05:47:44 | ---
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/ca332ef1 | result-kand2-sdxl-wuerst-karlo | 2023-11-08T20:31:09Z | 384 | 0 | null | [
"region:us"
] | 2023-11-08T20:31:09Z | 2023-11-08T20:31:08.000Z | 2023-11-08T20:31:08 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 192
num_examples: 10
download_size: 1372
dataset_size: 192
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ca332ef1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hlgd | null | 2023-01-25T14:32:19Z | 383 | 2 | null | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"headline-grouping",
"region:us"
] | 2023-01-25T14:32:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Headline Grouping (HLGD)
tags:
- headline-grouping
dataset_info:
features:
- name: timeline_id
dtype:
class_label:
names:
'0': 0
'1': 1
'2': 2
'3': 3
'4': 4
'5': 5
'6': 6
'7': 7
'8': 8
'9': 9
- name: headline_a
dtype: string
- name: headline_b
dtype: string
- name: date_a
dtype: string
- name: date_b
dtype: string
- name: url_a
dtype: string
- name: url_b
dtype: string
- name: label
dtype:
class_label:
names:
'0': same_event
'1': different_event
splits:
- name: train
num_bytes: 6447212
num_examples: 15492
- name: test
num_bytes: 941145
num_examples: 2495
- name: validation
num_bytes: 798302
num_examples: 2069
download_size: 1858948
dataset_size: 8186659
---
# Dataset Card for Headline Grouping (HLGD)
## 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/tingofurro/headline_grouping](https://github.com/tingofurro/headline_grouping)
- **Repository:** [https://github.com/tingofurro/headline_grouping](https://github.com/tingofurro/headline_grouping)
- **Paper:** [https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf](https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** phillab (at) berkeley (dot) edu
### Dataset Summary
HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating whether the two headlines describe the same underlying world event or not. The dataset comes with an existing split between `train`, `validation` and `test` (60-20-20).
### Supported Tasks and Leaderboards
The paper (NAACL2021) introducing HLGD proposes three challenges making use of various amounts of data:
- Challenge 1: Headline-only. Models must make predictions using only the text of both headlines.
- Challenge 2: Headline + Time. Models must make predictions using the headline and publication date of the two headlines.
- Challenge 3: Headline + Time + Other. Models can make predictions using the headline, publication date as well as any other relevant meta-data that can be obtained through the URL attached to the headline (full article content, authors, news source, etc.)
### Languages
Dataset is in english.
## Dataset Structure
### Data Instances
A typical dataset consists of a timeline_id, two headlines (A/B), each associated with a URL, and a date. Finally, a label indicates whether the two headlines describe the same underlying event (1) or not (0). Below is an example from the training set:
```
{'timeline_id': 4,
'headline_a': 'France fines Google nearly $57 million for first major violation of new European privacy regime',
'headline_b': "France hits Google with record EUR50mn fine over 'forced consent' data collection",
'date_a': '2019-01-21',
'date_b': '2019-01-21',
'url_a': 'https://www.chicagotribune.com/business/ct-biz-france-fines-google-privacy-20190121-story.html',
'url_b': 'https://www.rt.com/news/449369-france-hits-google-with-record-fine/',
'label': 1}
```
### Data Fields
- `timeline_id`: Represents the id of the timeline that the headline pair belongs to (values 0 to 9). The dev set is composed of timelines 0 and 5, and the test set timelines 7 and 8
- `headline_a`, `headline_b`: Raw text for the headline pair being compared
- `date_a`, `date_b`: Publication date of the respective headlines, in the `YYYY-MM-DD` format
- `url_a`, `url_b`: Original URL of the respective headlines. Can be used to retrieve additional meta-data on the headline.
- `label`: 1 if the two headlines are part of the the same headline group and describe the same underlying event, 0 otherwise.
### Data Splits
| | Train | Dev | Test |
| --------------------------- | ------- | ------ | ----- |
| Number of examples | 15,492 | 2,069 | 2,495 |
## Dataset Creation
### Curation Rationale
The task of grouping headlines from diverse news sources discussing a same underlying event is important to enable interfaces that can present the diversity of coverage of unfolding news events. Many news aggregators (such as Google or Yahoo news) present several sources for a given event, with an objective to highlight coverage diversity.
Automatic grouping of news headlines and articles remains challenging as headlines are short, heavily-stylized texts.
The HeadLine Grouping Dataset introduces the first benchmark to evaluate NLU model's ability to group headlines according to the underlying event they describe.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by collecting 10 news timelines from the NewsLens project by selecting timelines diversified in topic each contained between 80 and 300 news articles.
#### Who are the source language producers?
The source language producers are journalists or members of the newsroom of 34 news organizations listed in the paper.
### Annotations
#### Annotation process
Each timeline was annotated for group IDs by 5 independent annotators. The 5 annotations were merged into a single annotation named the global groups.
The global group IDs are then used to generate all pairs of headlines within timelines with binary labels: 1 if two headlines are part of the same global group, and 0 otherwise. A heuristic is used to remove negative examples to obtain a final dataset that has class imbalance of 1 positive example to 5 negative examples.
#### Who are the annotators?
Annotators were authors of the papers and 8 crowd-workers on the Upwork platform. The crowd-workers were native English speakers with experience either in proof-reading or data-entry.
### Personal and Sensitive Information
Annotators identity has been anonymized. Due to the public nature of news headline, it is not expected that the headlines will contain personal sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to facilitate applications that present diverse news coverage.
By simplifying the process of developing models that can group headlines that describe a common event, we hope the community can build applications that show news readers diverse sources covering similar events.
We note however that the annotations were performed in majority by crowd-workers and that even though inter-annotator agreement was high, it was not perfect. Bias of the annotators therefore remains in the dataset.
### Discussion of Biases
There are several sources of bias in the dataset:
- Annotator bias: 10 annotators participated in the creation of the dataset. Their opinions and perspectives influenced the creation of the dataset.
- Subject matter bias: HLGD consists of headlines from 10 news timelines from diverse topics (space, tech, politics, etc.). This choice has an impact on the types of positive and negative examples that appear in the dataset.
- Source selection bias: 33 English-language news sources are represented in the dataset. This selection of news sources has an effect on the content in the timeline, and the overall dataset.
- Time-range of the timelines: the timelines selected range from 2010 to 2020, which has an influence on the language and style of news headlines.
### Other Known Limitations
For the task of Headline Grouping, inter-annotator agreement is high (0.814) but not perfect. Some decisions for headline grouping are subjective and depend on interpretation of the reader.
## Additional Information
### Dataset Curators
The dataset was initially created by Philippe Laban, Lucas Bandarkar and Marti Hearst at UC Berkeley.
### Licensing Information
The licensing status of the dataset depends on the legal status of news headlines. It is commonly held that News Headlines fall under "fair-use" ([American Bar blog post](https://www.americanbar.org/groups/gpsolo/publications/gp_solo/2011/september/fair_use_news_reviews/))
The dataset only distributes headlines, a URL and a publication date. Users of the dataset can then retrieve additional information (such as the body content, author, etc.) directly by querying the URL.
### Citation Information
```
@inproceedings{Laban2021NewsHG,
title={News Headline Grouping as a Challenging NLU Task},
author={Laban, Philippe and Bandarkar, Lucas and Hearst, Marti A},
booktitle={NAACL 2021},
publisher = {Association for Computational Linguistics},
year={2021}
}
```
### Contributions
Thanks to [@tingofurro](https://github.com/<tingofurro>) for adding this dataset. | [
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kor_hate | null | 2023-01-25T14:33:47Z | 383 | 4 | korean-hatespeech-dataset | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"arxiv:2005.12503",
"region:us"
] | 2023-01-25T14:33:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: korean-hatespeech-dataset
pretty_name: Korean HateSpeech Dataset
dataset_info:
features:
- name: comments
dtype: string
- name: contain_gender_bias
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: bias
dtype:
class_label:
names:
'0': none
'1': gender
'2': others
- name: hate
dtype:
class_label:
names:
'0': hate
'1': offensive
'2': none
splits:
- name: train
num_bytes: 983608
num_examples: 7896
- name: test
num_bytes: 58913
num_examples: 471
download_size: 968449
dataset_size: 1042521
---
# Dataset Card for [Dataset Name]
## 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:** [Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech)
- **Repository:** [Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech)
- **Paper:** [BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection](https://arxiv.org/abs/2005.12503)
- **Point of Contact:** [Steven Liu](stevhliu@gmail.com)
### Dataset Summary
The Korean HateSpeech Dataset is a dataset of 8367 human-labeled entertainment news comments from a popular Korean news aggregation platform. Each comment was evaluated for either social bias (labels: `gender`, `others` `none`), hate speech (labels: `hate`, `offensive`, `none`) or gender bias (labels: `True`, `False`). The dataset was created to support the identification of toxic comments on online platforms where users can remain anonymous.
### Supported Tasks and Leaderboards
* `multi-label classification`: The dataset can be used to train a model for hate speech detection. A BERT model can be presented with a Korean entertainment news comment and be asked to label whether it contains social bias, gender bias and hate speech. Users can participate in a Kaggle leaderboard [here](https://www.kaggle.com/c/korean-hate-speech-detection/overview).
### Languages
The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
## Dataset Structure
### Data Instances
An example data instance contains a `comments` containing the text of the news comment and then labels for each of the following fields: `contain_gender_bias`, `bias` and `hate`.
```python
{'comments':'설마 ㅈ 현정 작가 아니지??'
'contain_gender_bias': 'True',
'bias': 'gender',
'hate': 'hate'
}
```
### Data Fields
* `comments`: text from the Korean news comment
* `contain_gender_bias`: a binary `True`/`False` label for the presence of gender bias
* `bias`: determines the type of social bias, which can be:
* `gender`: if the text includes bias for gender role, sexual orientation, sexual identity, and any thoughts on gender-related acts
* `others`: other kinds of factors that are considered not gender-related but social bias, including race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience
* `none`: a comment that does not incorporate the bias
* `hate`: determines how aggressive the comment is, which can be:
* `hate`: if the text is defined as an expression that display aggressive stances towards individuals/groups with certain characteristics (gender role, sexual orientation, sexual identity, any thoughts on gender-related acts, race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience, etc.)
* `offensive`: if the text contains rude or aggressive contents, can emit sarcasm through rhetorical question or irony, encompass an unethical expression or conveys unidentified rumors
* `none`: a comment that does not incorporate hate
### Data Splits
The data is split into a training and development (test) set. It contains 8371 annotated comments that are split into 7896 comments in the training set and 471 comments in the test set.
## Dataset Creation
### Curation Rationale
The dataset was created to provide the first human-labeled Korean corpus for toxic speech detection from a Korean online entertainment news aggregator. Recently, two young Korean celebrities suffered from a series of tragic incidents that led to two major Korean web portals to close the comments section on their platform. However, this only serves as a temporary solution, and the fundamental issue has not been solved yet. This dataset hopes to improve Korean hate speech detection.
### Source Data
#### Initial Data Collection and Normalization
A total of 10.4 million comments were collected from an online Korean entertainment news aggregator between Jan. 1, 2018 and Feb. 29, 2020. 1,580 articles were drawn using stratified sampling and the top 20 comments were extracted ranked in order of their Wilson score on the downvote for each article. Duplicate comments, single token comments and comments with more than 100 characters were removed (because they could convey various opinions). From here, 10K comments were randomly chosen for annotation.
#### Who are the source language producers?
The language producers are users of the Korean online news platform between 2018 and 2020.
### Annotations
#### Annotation process
Each comment was assigned to three random annotators to assign a majority decision. For more ambiguous comments, annotators were allowed to skip the comment. See Appendix A in the [paper](https://arxiv.org/pdf/2005.12503.pdf) for more detailed guidelines.
#### Who are the annotators?
Annotation was performed by 32 annotators, consisting of 29 annotators from the crowdsourcing platform DeepNatural AI and three NLP researchers.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to tackle the social issue of users creating toxic comments on online platforms. This dataset aims to improve detection of toxic comments online.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is curated by Jihyung Moon, Won Ik Cho and Junbum Lee.
### Licensing Information
[N/A]
### Citation Information
```
@inproceedings
{moon-et-al-2020-beep
title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection",
author = "Moon, Jihyung and
Cho, Won Ik and
Lee, Junbum",
booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.socialnlp-1.4",
pages = "25--31",
abstract = "Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity. Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released. In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea. The comments are annotated regarding social bias and hate speech since both aspects are correlated. The inter-annotator agreement Krippendorff{'}s alpha score is 0.492 and 0.496, respectively. We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks. The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue. Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined. We make our dataset publicly available and open competitions with the corpus and benchmarks.",
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/toxic_conversations | SetFit | 2022-02-11T13:45:54Z | 383 | 4 | null | [
"region:us"
] | 2022-02-11T13:45:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Toxic Conversation
This is a version of the [Jigsaw Unintended Bias in Toxicity Classification dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview). It contains comments from the Civil Comments platform together with annotations if the comment is toxic or not.
10 annotators annotated each example and, as recommended in the task page, set a comment as toxic when target >= 0.5
The dataset is inbalanced, with only about 8% of the comments marked as toxic. | [
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addy88/nq-question-answeronly | addy88 | 2021-12-14T13:59:58Z | 383 | 1 | null | [
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dennlinger/klexikon | dennlinger | 2022-10-25T15:03:56Z | 381 | 6 | klexikon | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:found",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"license:cc-by-sa-4.0",
"conditional-text-generation",
"simplification",
"document-level",
"arxiv:2201.07198",
"region:us"
] | 2022-10-25T15:03:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
- expert-generated
language_creators:
- found
- machine-generated
language:
- de
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
task_ids:
- text-simplification
paperswithcode_id: klexikon
pretty_name: Klexikon
tags:
- conditional-text-generation
- simplification
- document-level
---
# Dataset Card for the Klexikon Dataset
## Table of Contents
- [Version History](#version-history)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Version History
- **v0.3** (2022-09-01): Removing some five samples from the dataset due to duplication conflicts with other samples.
- **v0.2** (2022-02-28): Updated the files to no longer contain empty sections and removing otherwise empty lines at the end of files. Also removing lines with some sort of coordinate.
- **v0.1** (2022-01-19): Initial data release on Huggingface datasets.
## Dataset Description
- **Homepage:** [N/A]
- **Repository:** [Klexikon repository](https://github.com/dennlinger/klexikon)
- **Paper:** [Klexikon: A German Dataset for Joint Summarization and Simplification](https://arxiv.org/abs/2201.07198)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Dennis Aumiller](mailto:dennis.aumiller@gmail.com)
### Dataset Summary
The Klexikon dataset is a German resource of document-aligned texts between German Wikipedia and the children's lexicon "Klexikon". The dataset was created for the purpose of joint text simplification and summarization, and contains almost 2900 aligned article pairs.
Notably, the children's articles use a simpler language than the original Wikipedia articles; this is in addition to a clear length discrepancy between the source (Wikipedia) and target (Klexikon) domain.
### Supported Tasks and Leaderboards
- `summarization`: The dataset can be used to train a model for summarization. In particular, it poses a harder challenge than some of the commonly used datasets (CNN/DailyMail), which tend to suffer from positional biases in the source text. This makes it very easy to generate high (ROUGE) scoring solutions, by simply taking the leading 3 sentences. Our dataset provides a more challenging extraction task, combined with the additional difficulty of finding lexically appropriate simplifications.
- `simplification`: While not currently supported by the HF task board, text simplification is concerned with the appropriate representation of a text for disadvantaged readers (e.g., children, language learners, dyslexic,...).
For scoring, we ran preliminary experiments based on [ROUGE](https://huggingface.co/metrics/rouge), however, we want to cautiously point out that ROUGE is incapable of accurately depicting simplification appropriateness.
We combined this with looking at Flesch readability scores, as implemented by [textstat](https://github.com/shivam5992/textstat).
Note that simplification metrics such as [SARI](https://huggingface.co/metrics/sari) are not applicable here, since they require sentence alignments, which we do not provide.
### Languages
The associated BCP-47 code is `de-DE`.
The text of the articles is in German. Klexikon articles are further undergoing a simple form of peer-review before publication, and aim to simplify language for 8-13 year old children. This means that the general expected text difficulty for Klexikon articles is lower than Wikipedia's entries.
## Dataset Structure
### Data Instances
One datapoint represents the Wikipedia text (`wiki_text`), as well as the Klexikon text (`klexikon_text`).
Sentences are separated by newlines for both datasets, and section headings are indicated by leading `==` (or `===` for subheadings, `====` for sub-subheading, etc.).
Further, it includes the `wiki_url` and `klexikon_url`, pointing to the respective source texts. Note that the original articles were extracted in April 2021, so re-crawling the texts yourself will likely change some content.
Lastly, we include a unique identifier `u_id` as well as the page title `title` of the Klexikon page.
Sample (abridged texts for clarity):
```
{
"u_id": 0,
"title": "ABBA",
"wiki_url": "https://de.wikipedia.org/wiki/ABBA",
"klexikon_url": "https://klexikon.zum.de/wiki/ABBA",
"wiki_sentences": [
"ABBA ist eine schwedische Popgruppe, die aus den damaligen Paaren Agnetha Fältskog und Björn Ulvaeus sowie Benny Andersson und Anni-Frid Lyngstad besteht und sich 1972 in Stockholm formierte.",
"Sie gehört mit rund 400 Millionen verkauften Tonträgern zu den erfolgreichsten Bands der Musikgeschichte.",
"Bis in die 1970er Jahre hatte es keine andere Band aus Schweden oder Skandinavien gegeben, der vergleichbare Erfolge gelungen waren.",
"Trotz amerikanischer und britischer Dominanz im Musikgeschäft gelang der Band ein internationaler Durchbruch.",
"Sie hat die Geschichte der Popmusik mitgeprägt.",
"Zu ihren bekanntesten Songs zählen Mamma Mia, Dancing Queen und The Winner Takes It All.",
"1982 beendeten die Gruppenmitglieder aufgrund privater Differenzen ihre musikalische Zusammenarbeit.",
"Seit 2016 arbeiten die vier Musiker wieder zusammen an neuer Musik, die 2021 erscheinen soll.",
],
"klexikon_sentences": [
"ABBA war eine Musikgruppe aus Schweden.",
"Ihre Musikrichtung war die Popmusik.",
"Der Name entstand aus den Anfangsbuchstaben der Vornamen der Mitglieder, Agnetha, Björn, Benny und Anni-Frid.",
"Benny Andersson und Björn Ulvaeus, die beiden Männer, schrieben die Lieder und spielten Klavier und Gitarre.",
"Anni-Frid Lyngstad und Agnetha Fältskog sangen."
]
},
```
### Data Fields
* `u_id` (`int`): A unique identifier for each document pair in the dataset. 0-2349 are reserved for training data, 2350-2623 for testing, and 2364-2897 for validation.
* `title` (`str`): Title of the Klexikon page for this sample.
* `wiki_url` (`str`): URL of the associated Wikipedia article. Notably, this is non-trivial, since we potentially have disambiguated pages, where the Wikipedia title is not exactly the same as the Klexikon one.
* `klexikon_url` (`str`): URL of the Klexikon article.
* `wiki_text` (`List[str]`): List of sentences of the Wikipedia article. We prepare a pre-split document with spacy's sentence splitting (model: `de_core_news_md`). Additionally, please note that we do not include page contents outside of `<p>` tags, which excludes lists, captions and images.
* `klexikon_text` (`List[str]`): List of sentences of the Klexikon article. We apply the same processing as for the Wikipedia texts.
### Data Splits
We provide a stratified split of the dataset, based on the length of the respective Wiki article/Klexikon article pair (according to number of sentences).
The x-axis represents the length of the Wikipedia article, and the y-axis the length of the Klexikon article.
We segment the coordinate systems into rectangles of shape `(100, 10)`, and randomly sample a split of 80/10/10 for training/validation/test from each rectangle to ensure stratification. In case of rectangles with less than 10 entries, we put all samples into training.
The final splits have the following size:
* 2350 samples for training
* 274 samples for validation
* 274 samples for testing
## Dataset Creation
### Curation Rationale
As previously described, the Klexikon resource was created as an attempt to bridge the two fields of text summarization and text simplification. Previous datasets suffer from either one or more of the following shortcomings:
* They primarily focus on input/output pairs of similar lengths, which does not reflect longer-form texts.
* Data exists primarily for English, and other languages are notoriously understudied.
* Alignments exist for sentence-level, but not document-level.
This dataset serves as a starting point to investigate the feasibility of end-to-end simplification systems for longer input documents.
### Source Data
#### Initial Data Collection and Normalization
Data was collected from [Klexikon](klexikon.zum.de), and afterwards aligned with corresponding texts from [German Wikipedia](de.wikipedia.org).
Specifically, the collection process was performed in April 2021, and 3145 articles could be extracted from Klexikon back then. Afterwards, we semi-automatically align the articles with Wikipedia, by looking up articles with the same title.
For articles that do not exactly match, we manually review their content, and decide to match to an appropriate substitute if the content can be matched by at least 66% of the Klexikon paragraphs.
Similarly, we proceed to manually review disambiguation pages on Wikipedia.
We extract only full-text content, excluding figures, captions, and list elements from the final text corpus, and only retain articles for which the respective Wikipedia document consists of at least 15 paragraphs after pre-processing.
#### Who are the source language producers?
The language producers are contributors to Klexikon and Wikipedia. No demographic information was available from the data sources.
### Annotations
#### Annotation process
Annotations were performed by manually reviewing the URLs of the ambiguous article pairs. No annotation platforms or existing tools were used in the process.
Otherwise, articles were matched based on the exact title.
#### Who are the annotators?
The manually aligned articles were reviewed by the dataset author (Dennis Aumiller).
### Personal and Sensitive Information
Since Klexikon and Wikipedia are public encyclopedias, no further personal or sensitive information is included. We did not investigate to what extent information about public figures is included in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
Accessibility on the web is still a big issue, particularly for disadvantaged readers.
This dataset has the potential to strengthen text simplification systems, which can improve the situation.
In terms of language coverage, this dataset also has a beneficial impact on the availability of German data.
Potential negative biases include the problems of automatically aligned articles. The alignments may never be 100% perfect, and can therefore cause mis-aligned articles (or associations), despite the best of our intentions.
### Discussion of Biases
We have not tested whether any particular bias towards a specific article *type* (i.e., "person", "city", etc.) exists.
Similarly, we attempted to present an unbiased (stratified) split for validation and test set, but given that we only cover around 2900 articles, it is possible that these articles represent a particular focal lense on the overall distribution of lexical content.
### Other Known Limitations
Since the articles were written independently of each other, it is not guaranteed that there exists an exact coverage of each sentence in the simplified article, which could also stem from the fact that sometimes Wikipedia pages have separate article pages for aspects (e.g., the city of "Aarhus" has a separate page for its art museum (ARoS). However, Klexikon lists content and description for ARoS on the page of the city itself.
## Additional Information
### Dataset Curators
The dataset was curated only by the author of this dataset, Dennis Aumiller.
### Licensing Information
Klexikon and Wikipedia make their textual contents available under the CC BY-SA license, which will be inherited for this dataset.
### Citation Information
If you use our dataset or associated code, please cite our paper:
```
@inproceedings{aumiller-gertz-2022-klexikon,
title = "Klexikon: A {G}erman Dataset for Joint Summarization and Simplification",
author = "Aumiller, Dennis and
Gertz, Michael",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.288",
pages = "2693--2701"
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sanchit-gandhi/gtzan | sanchit-gandhi | 2023-06-23T13:48:10Z | 381 | 0 | null | [
"region:us"
] | 2023-06-23T13:48:10Z | 2023-06-23T13:47:03.000Z | 2023-06-23T13:47:03 | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 32000
- name: genre
dtype:
class_label:
names:
'0': blues
'1': classical
'2': country
'3': disco
'4': hiphop
'5': jazz
'6': metal
'7': pop
'8': reggae
'9': rock
splits:
- name: train
num_bytes: 1322941192.0
num_examples: 999
download_size: 1305519226
dataset_size: 1322941192.0
---
# Dataset Card for "gtzan"
[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 | |
nuprl/MultiPL-T | nuprl | 2023-09-13T12:57:50Z | 381 | 1 | null | [
"license:bigcode-openrail-m",
"arxiv:2308.09895",
"region:us"
] | 2023-09-13T12:57:50Z | 2023-08-17T14:17:33.000Z | 2023-08-17T14:17:33 | ---
license: bigcode-openrail-m
dataset_info:
features:
- name: content
dtype: string
splits:
- name: racket
num_bytes: 14482516
num_examples: 40510
- name: ocaml
num_bytes: 19240207
num_examples: 43401
- name: lua
num_bytes: 25917278
num_examples: 48194
download_size: 7491686
dataset_size: 59640001
---
# MultiPL-T fine-tuning sets
This dataset contains the MultiPL-T fine-tuning sets described in the paper "Knowledge Transfer from High-Resource to Low-Resource
Programming Languages for Code LLMs": [Arxiv](https://arxiv.org/abs/2308.09895).
## MultiPL-T tuned models
StarCoderBase-1b: https://huggingface.co/nuprl/MultiPLCoder-1b
StarCoderBase-15b: https://huggingface.co/nuprl/MultiPLCoder-15b
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bing_coronavirus_query_set | null | 2022-11-03T16:30:54Z | 380 | 0 | null | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2022-11-03T16:30:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: null
pretty_name: BingCoronavirusQuerySet
dataset_info:
features:
- name: id
dtype: int32
- name: Date
dtype: string
- name: Query
dtype: string
- name: IsImplicitIntent
dtype: string
- name: Country
dtype: string
- name: PopularityScore
dtype: int32
config_name: country_2020-09-01_2020-09-30
splits:
- name: train
num_bytes: 22052706
num_examples: 317856
download_size: 16351450
dataset_size: 22052706
---
# Dataset Card for BingCoronavirusQuerySet
## 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:** None
- **Repository:** https://github.com/microsoft/BingCoronavirusQuerySet
- **Paper:** Nonewww
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Please note that you can specify the start and end date of the data. You can get start and end dates from here: https://github.com/microsoft/BingCoronavirusQuerySet/tree/master/data/2020
example:
```
load_dataset("bing_coronavirus_query_set", queries_by="state", start_date="2020-09-01", end_date="2020-09-30")
```
You can also load the data by country by using `queries_by="country"`.
### 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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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 [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
emozilla/pg19 | emozilla | 2023-10-09T15:06:39Z | 380 | 3 | null | [
"region:us"
] | 2023-10-09T15:06:39Z | 2023-08-08T13:11:36.000Z | 2023-08-08T13:11:36 | ---
dataset_info:
features:
- name: short_book_title
dtype: string
- name: publication_date
dtype: int32
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 11453688452
num_examples: 28602
- name: validation
num_bytes: 17402295
num_examples: 50
- name: test
num_bytes: 40482852
num_examples: 100
download_size: 2257437892
dataset_size: 11511573599
---
# Dataset Card for "pg19"
Paraquet version of [pg19](https://huggingface.co/datasets/pg19)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
maywell/ko_Ultrafeedback_binarized | maywell | 2023-11-09T03:25:29Z | 380 | 11 | null | [
"region:us"
] | 2023-11-09T03:25:29Z | 2023-11-04T18:41:52.000Z | 2023-11-04T18:41:52 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 226278590
num_examples: 61966
download_size: 110040382
dataset_size: 226278590
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# maywell/ko_Ultrafeedback_binarized
본 데이터는 Synatra-7B-Translation 모델을 통해 Ultrafeedback_binarized를 번역하고 정제한 데이터셋입니다.
해당 데이터를 직접적으로 상업적으로 사용하는 것은 허용되지 않으며, 데이터를 이용하여 훈련된 모델에 대한 상업적 사용은 허용됩니다.
아직 완벽히 정제되지는 않았으며, 오류나 수정사항에 대해서는 PR 부탁드립니다. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/f719c86e | result-kand2-sdxl-wuerst-karlo | 2023-11-08T23:40:52Z | 380 | 0 | null | [
"region:us"
] | 2023-11-08T23:40:52Z | 2023-11-08T23:40:51.000Z | 2023-11-08T23:40:51 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 224
num_examples: 10
download_size: 1403
dataset_size: 224
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f719c86e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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winglian/no_robots_rlhf | winglian | 2023-11-26T13:03:42Z | 380 | 1 | null | [
"region:us"
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eli5_category | null | 2022-11-18T20:00:33Z | 379 | 4 | null | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|eli5",
"language:en",
"license:unknown",
"region:us"
] | 2022-11-18T20:00:33Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: ELI5-Category
size_categories:
- 100K<n<1M
source_datasets:
- extended|eli5
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-abstractive-qa
dataset_info:
features:
- name: q_id
dtype: string
- name: title
dtype: string
- name: selftext
dtype: string
- name: category
dtype: string
- name: subreddit
dtype: string
- name: answers
struct:
- name: a_id
sequence: string
- name: text
sequence: string
- name: score
sequence: int32
- name: text_urls
sequence:
sequence: string
- name: title_urls
sequence: string
- name: selftext_urls
sequence: string
splits:
- name: train
num_bytes: 166409797
num_examples: 91772
- name: validation1
num_bytes: 13150585
num_examples: 5446
- name: validation2
num_bytes: 4737744
num_examples: 2375
- name: test
num_bytes: 10419098
num_examples: 5411
download_size: 72921829
dataset_size: 194717224
---
# Dataset Card for ELI5-Category
## 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:** [ELI5-Category homepage](https://celeritasml.netlify.app/posts/2021-12-01-eli5c/)
- **Repository:** [ELI5-Category repository](https://github.com/rexarski/ANLY580-final-project)
- **Point of Contact:** [Jingsong Gao](mailto:jg2109@georgetown.edu)
### Dataset Summary
The ELI5-Category dataset is a smaller but newer and categorized version of the original ELI5 dataset. It's an English-language dataset of questions and answers gathered from the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subreddit where users ask factual questions requiring paragraph-length or longer answers. After 2017, a tagging system was introduced to this subreddit so that the questions can be categorized into different topics according to their tags. Since the training and validation set is built by questions in different topics, the dataset is expected to alleviate the train/validation overlapping issue in the original [ELI5 dataset](https://huggingface.co/datasets/eli5).
### Supported Tasks and Leaderboards
- `abstractive-qa`, `open-domain-abstractive-qa`: The dataset can be used to train a model for Open Domain Long Form Question Answering. An LFQA model is presented with a non-factoid and asked to retrieve relevant information from a knowledge source (such as [Wikipedia](https://www.wikipedia.org/)), then use it to generate a multi-sentence answer.
### Languages
The text in the dataset is in English, as spoken by Reddit users on the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subreddit. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
The structure of this dataset is very similar to the original [ELI5 dataset](https://huggingface.co/datasets/eli5). A typical data point comprises a question, with a `title` containing the main question and a `selftext` which sometimes elaborates on it, and a list of answers from the forum sorted by scores they obtained. Additionally, the URLs in each of the text fields have been extracted to respective lists and replaced by generic tokens in the text.
In addition to the original ELI5 dataset, the data point also has a `category` field. There are 11 common values of `category` in this dataset: `Biology`,`Chemistry`,`Culture`,`Earth Science`,`Economics`,`Engineering`,`Mathematics`,`Other`,`Physics`,`Psychology`,`Technology`, and a special `category`: `Repost` indicates the same question has been asked before.
An example from the ELI5-Category set looks as follows:
```
{'q_id': '5lcm18',
'title': 'Why do old games running on new hardware still have technical issues ?',
'selftext': 'I am playing some mega man games on my Xbox One and experience slowdown when there are a lot of enemies on screen . but the Xbox One is significantly more powerful than the NES , so why is there still slowdown on this hardware ?',
'category': 'Engineering',
'subreddit': 'explainlikeimfive',
'answers': {'a_id': ['dbuo48e', 'dbusfve'],
'text': ["The XBox is emulating NES hardware and running the emulation at a set speed . If it ran it at as fast as possible , then it would be several times faster than the original NES game and would be unplayable . I ca n't speak for Mega Man exactly , but older games tended to run on a cycle locked to the screen refresh which was a fixed 60Hz or 50Hz . There was only one piece of hardware they ran on , so there was no need to adjust for different hardware speeds .",
"In that case , it 's probably on purpose - they want to emulate the experience as closely as possible , even including the slowdown and sprite flickering . Some emulators let you turn it off , but it 's usually turned on by default . In other cases , like if you 're trying to emulate PS2 games on your PC , the game might just run really slow in general . Even though your PC is way more powerful than a PS2 , it has to \" translate \" from PS2 language to PC language in realtime , which is much more difficult than running PS2 code on the PS2 itself ."],
'score': [13, 3],
'text_urls': [[],[]]},
'title_urls': {'url': []},
'selftext_urls': {'url': []}}
```
### Data Fields
- `q_id`: a string question identifier for each example, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/submissions/) Reddit submission dumps
- `subreddit`: always `explainlikeimfive`, indicating which subreddit the question came from
- `category`: tag of the question, the possible values are listed above.
- `title`: title of the question, with URLs extracted and replaced by `URL_n` tokens
- `title_urls`: list of the extracted URLs, the `n`th element of the list was replaced by `URL_n`
- `selftext`: either an empty string or an elaboration of the question
- `selftext_urls`: similar to `title_urls` but for `self_text`
- `answers`: a list of answers, each answer has:
- `a_id`: a string answer identifier for each answer, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/comments/) Reddit comments dumps.
- `text`: the answer text with the URLs normalized
- `score`: the number of upvotes - the number of downvotes the answer had received when the dumps were created
- `text_urls`: lists of the extracted URLs for every answer
### Data Splits
In order to avoid having duplicate questions across sets, three non-overlapping subsets of `category` are used in the training, validation and test set. Also, a special validation set contains all the questions in the `Repost` category. A valid retriever-generator model should have consistent performances on both validation sets.
The final split sizes are as follows:
| | Train | Valid | Valid2 |Test |
| ----- | ------ | ----- | ---- | ---- |
| `Biology` | 32769 | | | |
| `Chemistry` | 6633 | | | |
| `Culture` | | 5446 | | |
| `Earth Science` | 677 | | | |
| `Economics` | 5901 | | | |
| `Engineering` | | | | 5411 |
| `Mathematics` | 1912 | | | |
| `Other` | 19312 | | | |
| `Physics` | 10196 | | | |
| `Psychology` | 338 | | | |
| `Technology` | 14034 | | | |
| `Repost` | | | 2375 | |
| **Total** | 91772 | 5446 | 2375 | 5411 |
## Dataset Creation
### Curation Rationale
ELI5-Category was built to provide a testbed for machines to learn how to answer more complex questions, which requires them to find and combine the information in a coherent manner. The dataset was built by gathering questions that were asked by community members of three subreddits, including [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), along with the answers that were provided by other users. The [rules of the subreddit](https://www.reddit.com/r/explainlikeimfive/wiki/detailed_rules) make this data particularly well suited to training a model for abstractive question answering: the questions need to seek an objective explanation about well-established facts, and the answers provided need to be understandable to a layperson without any particular knowledge domain.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by filtering submissions and comments from the subreddits of interest from the XML dumps of the [Reddit forum](https://www.reddit.com/) hosted on [Pushshift.io](https://files.pushshift.io/reddit/).
In order to further improve the quality of the selected examples, only questions with a score of at least 2 and at least one answer with a score of at least 2 were selected for the dataset. The dataset questions and answers span a period from January 2017 to June 2021.
#### Who are the source language producers?
The language producers are users of the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subreddit between 2017 and 2021. No further demographic information was available from the data source.
### Annotations
The dataset contains the `category` as an additional annotation for the topics of questions.
#### Annotation process
The dataset is auto-annotated by the tags of posts in the [Reddit forum](https://www.reddit.com/).
#### Who are the annotators?
The annotators are users/administrators of the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subreddit between 2017 and 2021. No further demographic information was available from the data source.
### Personal and Sensitive Information
The authors removed the speaker IDs from the [Pushshift.io](https://files.pushshift.io/reddit/) dumps but did not otherwise anonymize the data. Some questions and answers are about contemporary public figures or individuals who appeared in the news.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset has a similar social impact to the original ELI5 dataset [Social Impact of Dataset](https://huggingface.co/datasets/eli5#social-impact-of-dataset).
### Discussion of Biases
The dataset has similar considerations of biases to the original ELI5 dataset [Discussion of Biases](https://huggingface.co/datasets/eli5#discussion-of-biases).
### Other Known Limitations
The dataset has similar limitations to the original ELI5 dataset [Other Known Limitations](https://huggingface.co/datasets/eli5#other-known-limitations).
## Additional Information
### Dataset Curators
The dataset was initially created by Jingsong Gao, Qinren Zhou, Rui Qiu, during a course project of `ANLY 580`: NLP for Data Analytics at Georgetown University.
### Licensing Information
The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear.
### Citation Information
```
@inproceedings{eli5-category,
author = {Jingsong Gao and
Qingren Zhou and
Rui Qiu},
title = {{ELI5-Category:} A categorized open-domain QA dataset},
year = {2021}
}
```
### Contributions
Thanks to [@jingshenSN2](https://github.com/jingshenSN2), [@QinrenZhou](https://github.com/QinrenZhou), [@rexarski](https://github.com/rexarski) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
domenicrosati/TruthfulQA | domenicrosati | 2022-07-01T15:41:54Z | 379 | 4 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2109.07958",
"region:us"
] | 2022-07-01T15:41:54Z | 2022-05-12T00:38:33.000Z | 2022-05-12T00:38:33 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: TruthfulQA
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
- closed-domain-qa
---
# Dataset Card for TruthfulQA
## 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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/sylinrl/TruthfulQA](https://github.com/sylinrl/TruthfulQA)
- **Repository:** [https://github.com/sylinrl/TruthfulQA](https://github.com/sylinrl/TruthfulQA)
- **Paper:** [https://arxiv.org/abs/2109.07958](https://arxiv.org/abs/2109.07958)
### Dataset Summary
TruthfulQA: Measuring How Models Mimic Human Falsehoods
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. We tested GPT-3, GPT-Neo/J, GPT-2 and a T5-based model. The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training distribution. We suggest that scaling up models alone is less promising for improving truthfulness than fine-tuning using training objectives other than imitation of text from the web.
### Supported Tasks and Leaderboards
See: [Tasks](https://github.com/sylinrl/TruthfulQA#tasks)
### Languages
English
## Dataset Structure
### Data Instances
The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics.
### Data Fields
1. **Type**: Adversarial v Non-Adversarial Questions
2. **Category**: Category of misleading question
3. **Question**: The question
4. **Best Answer**: The best correct answer
5. **Correct Answers**: A set of correct answers. Delimited by `;`.
6. **Incorrect Answers**: A set of incorrect answers. Delimited by `;`.
7. **Source**: A source that supports the correct answers.
### Data Splits
Due to constraints of huggingface the dataset is loaded into a "train" split.
### Contributions
Thanks to [@sylinrl](https://github.com/sylinrl) for adding this dataset. | [
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tomekkorbak/detoxify-pile-chunk3-4000000-4050000 | tomekkorbak | 2022-10-06T04:28:52Z | 379 | 0 | null | [
"region:us"
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nampdn-ai/tiny-textbooks | nampdn-ai | 2023-10-04T03:56:50Z | 378 | 65 | null | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.05463",
"arxiv:2306.01116",
"arxiv:2304.08442",
"arxiv:2305.07759",
"doi:10.57967/hf/1126",
"region:us"
] | 2023-10-04T03:56:50Z | 2023-08-10T09:21:07.000Z | 2023-08-10T09:21:07 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Textbooks
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# Textbook-like Dataset: A High-Quality Resource for Small Language Models
The idea is simply inspired by the [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463) paper. The source texts in this dataset have been gathered and carefully select the best of the [falcon-refinedweb](https://arxiv.org/abs/2306.01116) and [minipile](https://arxiv.org/abs/2304.08442) datasets to ensure the diversity, quality while tiny in size. The dataset was synthesized using 4x3090 Ti cards over a period of 500 hours, thanks to [Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b) finetuned model.
Why settle for low-quality text when you can train on a high-quality, textbook-like dataset? Training language models on subpar text can lead to several issues:
1. **Noise**: Such text often contains typos, grammatical errors, and poorly structured sentences, which can confuse models and degrade performance.
2. **Misinformation**: Low-quality web text may contain incorrect or misleading information, leading to models propagating these inaccuracies.
3. **Lack of Depth**: Subpar text often lacks the depth and detail found in high-quality content, limiting a model's understanding of complex topics.
Conversely, training on my clean and high-quality dataset offers numerous advantages:
1. **Accuracy**: The theoretical concepts in my dataset provide near accurate and detailed information, akin to a well-written textbook. (Need more contribute for facts check)
2. **Context**: Practical examples demonstrate how these concepts apply in real-world situations, offering valuable context.
3. **Performance**: Models trained on high-quality data can generate more accurate, insightful, and human-like text.
A standout feature of this dataset is its volume. It boasts a whopping **420,000 textbook documents**. This extensive collection ensures a wide coverage of topics and concepts, providing your models with a comprehensive and diverse learning resource.
Moreover, this dataset is generated using an open-source language model, ensuring the data is open for every researcher to process. I love the openness and that's why I want to contribute this dataset for the community to push over the limit.
Quality over quantity is a principle that holds true even in machine learning. Training on a large amount of low-quality tokens can lead to models learning and propagating the noise, inaccuracies, and poor structures present in the bad text. This can result in models that generate less accurate and less coherent outputs.
On the other hand, training on a smaller amount of high-quality tokens, like those in this dataset, can yield significantly better results. High-quality tokens provide accurate, well-structured, and meaningful information from which models can learn effectively. This leads to models that can generate more accurate, insightful, and human-like text.
In essence, it's about making every token count. Each high-quality token that a model learns from is a step towards better performance. So why waste computational resources and learning capacity on bad tokens when you can focus on high-quality ones? It's a more efficient and effective approach to training language models.
Choosing high-quality dataset over low-quality web text is akin to opting for a reliable textbook over scattered internet articles. This choice can significantly enhance the performance and reliability of your causal language models.
I'm excited to present this unique blend of theoretical concepts and practical examples designed to supercharge your causal language models. This isn't just another dataset; it's a high-quality resource that can help your models learn more effectively and with better common sense.
I hope this dataset is an useful resource for ML researchers working with small causal language models. I eagerly await your feedback and suggestions as I continue to refine and expand the dataset. Together, let's push the boundaries of what's possible with a **tiny language models**!
## Visualization
[Nomic Atlas](https://atlas.nomic.ai/map/0348f3f7-9280-404f-b6d3-d0b5993a6693/846bcd82-fcc5-474d-b24b-82d1b791f80b) 230k data points visualized thanks to Nomic AI platform.
### Disclaimer
While every effort has been made to ensure the accuracy of the information contained within this dataset, please note that it is provided 'as is' and without any warranties.
The use of the `textbook` field in this dataset is intended for research purposes only. You are advised to verify any information obtained from this dataset before acting upon it.
## Tiny Series
Explore the possibilities and limitations of building Small Language Models with these tiny gems of data!
- [TinyStories](https://arxiv.org/abs/2305.07759): The paper that sparked my interest in the journey of the tiny-* series.
- [tiny-codes](https://huggingface.co/datasets/nampdn-ai/tiny-codes): Collection of 1.6M short and clear code snippets that can help LLM models learn how to reason.
- [tiny-orca-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-orca-textbooks): Synthetic textbook to help model learn in-context on how it should perform task the right way.
- [tiny-webtext](https://huggingface.co/datasets/nampdn-ai/tiny-webtext): A 6GB (4.5M records) variety of diverse webtext enriched with critical thinking methods to make unbiased English dataset.
- [tiny-lessons](https://huggingface.co/datasets/nampdn-ai/tiny-lessons): Subset of this dataset, various lessons about "things of internet" augmented in a bite-sized textbook Markdown format.
- [tiny-bridgedict](https://huggingface.co/datasets/nampdn-ai/tiny-bridgedict): A dataset that links and transfers knowledge between English, Vietnamese, Chinese in a tiny multilingual models.
### Others small HQ datasets with textbook-like quality
- [devdocs.io](https://huggingface.co/datasets/nampdn-ai/devdocs.io): FreeCodeCamp has provided 189k comprehensive API documentation across a wide range of tech stacks and programming languages.
- [sciphi-python-textbook](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-python-textbook)
- [textbook_quality_programming](https://huggingface.co/datasets/vikp/textbook_quality_programming)
- [sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Villekom/oa_dolly_15k_fi | Villekom | 2023-11-28T10:52:22Z | 378 | 0 | null | [
"region:us"
] | 2023-11-28T10:52:22Z | 2023-08-23T14:15:04.000Z | 2023-08-23T14:15:04 | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
struct:
- name: CATEGORY
dtype: string
- name: CONTEXT
dtype: string
splits:
- name: train
num_bytes: 13654728
num_examples: 15015
download_size: 8698896
dataset_size: 13654728
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oa_dolly_15k_fi"
Taken from https://github.com/TurkuNLP/dolly-fi
[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 | |
thainer | null | 2023-01-25T14:45:41Z | 377 | 1 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-tirasaroj-aroonmanakun",
"language:th",
"license:cc-by-3.0",
"region:us"
] | 2023-01-25T14:45:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
- expert-generated
language:
- th
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-tirasaroj-aroonmanakun
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
pretty_name: thainer
dataset_info:
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': ADJ
'1': ADP
'2': ADV
'3': AUX
'4': CCONJ
'5': DET
'6': NOUN
'7': NUM
'8': PART
'9': PRON
'10': PROPN
'11': PUNCT
'12': SCONJ
'13': VERB
- name: ner_tags
sequence:
class_label:
names:
'0': B-DATE
'1': B-EMAIL
'2': B-LAW
'3': B-LEN
'4': B-LOCATION
'5': B-MONEY
'6': B-ORGANIZATION
'7': B-PERCENT
'8': B-PERSON
'9': B-PHONE
'10': B-TIME
'11': B-URL
'12': B-ZIP
'13': B-ไม่ยืนยัน
'14': I-DATE
'15': I-EMAIL
'16': I-LAW
'17': I-LEN
'18': I-LOCATION
'19': I-MONEY
'20': I-ORGANIZATION
'21': I-PERCENT
'22': I-PERSON
'23': I-PHONE
'24': I-TIME
'25': I-URL
'26': I-ไม่ยืนยัน
'27': O
config_name: thainer
splits:
- name: train
num_bytes: 8117902
num_examples: 6348
download_size: 5456461
dataset_size: 8117902
---
# Dataset Card for `thainer`
## 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/wannaphong/thai-ner
- **Repository:** https://github.com/wannaphong/thai-ner
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://github.com/wannaphong/
### Dataset Summary
ThaiNER (v1.3) is a 6,456-sentence named entity recognition dataset created from expanding the 2,258-sentence [unnamed dataset](http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) by [Tirasaroj and Aroonmanakun (2012)](http://pioneer.chula.ac.th/~awirote/publications/). It is used to train NER taggers in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp). The NER tags are annotated by [Tirasaroj and Aroonmanakun (2012)]((http://pioneer.chula.ac.th/~awirote/publications/)) for 2,258 sentences and the rest by [@wannaphong](https://github.com/wannaphong/). The POS tags are done by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)'s `perceptron` engine trained on `orchid_ud`. [@wannaphong](https://github.com/wannaphong/) is now the only maintainer of this dataset.
### Supported Tasks and Leaderboards
- named entity recognition
- pos tagging
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'id': 100, 'ner_tags': [27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27], 'pos_tags': [6, 12, 13, 1, 6, 5, 11, 7, 11, 6, 5, 13, 6, 6, 6, 11, 6, 6, 11, 6, 6, 11, 6, 6, 13, 6, 11, 11, 6, 11, 6, 11, 6, 11, 6, 11, 11, 6, 6, 11, 12, 6, 13, 5, 11, 7, 11, 6, 3, 11, 12, 3, 13, 6, 1, 6, 12, 13, 1, 6, 6, 5, 11, 3, 11, 5, 4, 6, 13, 6, 13, 6, 10, 3, 13, 13, 12, 13, 12, 0, 1, 10, 11, 6, 6, 11, 6, 11, 6, 12, 13, 5, 12, 3, 13, 13, 1, 6, 1, 6, 13], 'tokens': ['เชื้อโรค', 'ที่', 'ปรากฏ', 'ใน', 'สัตว์', 'ทั้ง', ' ', '4', ' ', 'ชนิด', 'นี้', 'เป็น', 'เชื้อ', 'โรคไข้หวัด', 'นก', ' ', 'เอช', 'พี', ' ', 'เอ', 'เวียน', ' ', 'อิน', 'ฟลู', 'เอน', 'ซา', ' ', '(', 'Hight', ' ', 'Polygenic', ' ', 'Avain', ' ', 'Influenza', ')', ' ', 'ชนิด', 'รุนแรง', ' ', 'ซึ่ง', 'การ', 'ตั้งชื่อ', 'ทั้ง', ' ', '4', ' ', 'ขึ้น', 'มา', ' ', 'เพื่อที่จะ', 'สามารถ', 'ระบุ', 'เชื้อ', 'ของ', 'ไวรัส', 'ที่', 'ทำอันตราย', 'ตาม', 'สิ่งมีชีวิต', 'ประเภท', 'ต่างๆ', ' ', 'ได้', ' ', 'อีก', 'ทั้ง', 'การ', 'ระบุ', 'สถานที่', 'คือ', 'ประเทศ', 'ไทย', 'จะ', 'ทำให้', 'รู้', 'ว่า', 'พบ', 'ที่', 'แรก', 'ใน', 'ไทย', ' ', 'ส่วน', 'วัน', ' ', 'เดือน', ' ', 'ปี', 'ที่', 'พบ', 'นั้น', 'ก็', 'จะ', 'ทำให้', 'ทราบ', 'ถึง', 'ครั้งแรก', 'ของ', 'การ', 'ค้นพบ']}
{'id': 107, 'ner_tags': [27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27], 'pos_tags': [0, 1, 6, 5, 11, 12, 3, 3, 13, 6, 13, 12, 0, 2, 12, 11, 6, 5, 13, 6, 5, 1, 6, 6, 1, 10, 11, 4, 13, 6, 11, 12, 6, 6, 10, 11, 13, 6, 1, 6, 4, 6, 1, 6, 6, 11, 4, 6, 1, 5, 6, 12, 2, 13, 6, 6, 5, 1, 11, 12, 13, 1, 6, 6, 11, 13, 11, 6, 6, 6, 11, 11, 6, 11, 11, 4, 10, 11, 11, 6, 11], 'tokens': ['ล่าสุด', 'ใน', 'เรื่อง', 'นี้', ' ', 'ทั้งนี้', 'คง', 'ต้อง', 'มี', 'การ', 'ตรวจสอบ', 'ให้', 'ชัดเจน', 'อีกครั้ง', 'ว่า', ' ', 'ไวรัส', 'นี้', 'เป็น', 'ชนิด', 'เดียว', 'กับ', 'ไข้หวัด', 'นก', 'ใน', 'ไทย', ' ', 'หรือ', 'เป็น', 'การกลายพันธุ์', ' ', 'โดยที่', 'คณะ', 'สัตวแพทย์', 'มหาวิทยาลัยเกษตรศาสตร์', ' ', 'จัด', 'ระดมสมอง', 'จาก', 'คณบดี', 'และ', 'ผู้เชี่ยวชาญ', 'จาก', 'คณะ', 'สัตวแพทย์', ' ', 'และ', 'ปศุสัตว์', 'ของ', 'หลาย', 'มหาวิทยาลัย', 'เพื่อ', 'ร่วมกัน', 'หา', 'ข้อมูล', 'เรื่อง', 'นี้', 'ด้วย', ' ', 'โดย', 'ประสาน', 'กับ', 'เจ้าหน้าที่', 'ระหว่างประเทศ', ' ', 'คือ', ' ', 'องค์การ', 'สุขภาพ', 'สัตว์โลก', ' ', '(', 'OIE', ')', ' ', 'และ', 'องค์การอนามัยโลก', ' ', '(', 'WHO', ')']}
```
### Data Fields
- `id`: sentence id
- `tokens`: word tokens by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)'s dictionary-based tokenizer `newmm`
- `pos_tags`: POS tags tagged by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)'s `perceptron` engine trained on `orchid_ud`
- `ner_tags`: NER tags tagged by humans
### Data Splits
No explicit split is given
## Dataset Creation
### Curation Rationale
ThaiNER (v1.3) is a 6,456-sentence named entity recognition dataset created from expanding the 2,258-sentence [unnamed dataset](http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) by [Tirasaroj and Aroonmanakun (2012)](http://pioneer.chula.ac.th/~awirote/publications/). It is used to train NER taggers in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp).
### Source Data
#### Initial Data Collection and Normalization
The earlier part of the dataset is all news articles, whereas the part added by [@wannaphong](https://github.com/wannaphong/) includes news articles, public announcements and [@wannaphong](https://github.com/wannaphong/)'s own chat messages with personal and sensitive information removed.
#### Who are the source language producers?
News articles and public announcements are created by their respective authors. Chat messages are created by [@wannaphong](https://github.com/wannaphong/).
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[Tirasaroj and Aroonmanakun (2012)](http://pioneer.chula.ac.th/~awirote/publications/) for the earlier 2,258 sentences and [@wannaphong](https://github.com/wannaphong/) for the rest
### Personal and Sensitive Information
News articles and public announcements are not expected to include personal and sensitive information. [@wannaphong](https://github.com/wannaphong/) has removed such information from his own chat messages.
## Considerations for Using the Data
### Social Impact of Dataset
- named entity recognition in Thai
### Discussion of Biases
Since almost all of collection and annotation is done by [@wannaphong](https://github.com/wannaphong/), his biases are expected to be reflected in the dataset.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[Tirasaroj and Aroonmanakun (2012)](http://pioneer.chula.ac.th/~awirote/publications/) for the earlier 2,258 sentences and [@wannaphong](https://github.com/wannaphong/) for the rest
### Licensing Information
CC-BY 3.0
### Citation Information
```
@misc{Wannaphong Phatthiyaphaibun_2019,
title={wannaphongcom/thai-ner: ThaiNER 1.3},
url={https://zenodo.org/record/3550546},
DOI={10.5281/ZENODO.3550546},
abstractNote={Thai Named Entity Recognition},
publisher={Zenodo},
author={Wannaphong Phatthiyaphaibun},
year={2019},
month={Nov}
}
```
Work extended from:
[Tirasaroj, N. and Aroonmanakun, W. 2012. Thai NER using CRF model based on surface features. In Proceedings of SNLP-AOS 2011, 9-10 February, 2012, Bangkok, pages 176-180.](http://pioneer.chula.ac.th/~awirote/publications/)
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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tmu_gfm_dataset | null | 2022-11-03T16:30:48Z | 377 | 2 | null | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"grammatical-error-correction",
"region:us"
] | 2022-11-03T16:30:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: null
pretty_name: TMU-GFM-Dataset
tags:
- grammatical-error-correction
dataset_info:
features:
- name: source
dtype: string
- name: output
dtype: string
- name: grammer
sequence: int32
- name: fluency
sequence: int32
- name: meaning
sequence: int32
- name: system
dtype: string
- name: ave_g
dtype: float32
- name: ave_f
dtype: float32
- name: ave_m
dtype: float32
splits:
- name: train
num_bytes: 1446144
num_examples: 4221
download_size: 1270197
dataset_size: 1446144
---
# Dataset Card for TMU-GFM-Dataset
## 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:** [N/A]
- **Repository:** https://github.com/tmu-nlp/TMU-GFM-Dataset
- **Paper:** [SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction](https://www.aclweb.org/anthology/2020.coling-main.573.pdf)
- **Leaderboard:** [N/A]
- **Point of Contact:** Check the paper.
### Dataset Summary
Authors collected manual evaluations for the grammaticality, fluency, and meaning preservation of the system outputs of 1,381 sentences from CoNLL 2013.
To collect the manual evaluations for various system outputs, each source sentence was corrected by the following five typical systems: statistical machine translation (SMT) (Grundkiewicz and Junczys-Dowmunt, 2018), recurrent neural network (RNN) (Luong et al., 2015), convolutional neural network (CNN) (Chollampatt and Ng, 2018), self-attention network (SAN) (Vaswani et al., 2017), and SAN with copy mechanism (SAN+Copy) (Zhao et al., 2019).
Manual evaluation for the grammaticality, fluency, and meaning preservation were assigned to a total of 4,223 sentences.
### Supported Tasks and Leaderboards
Grammatical Error Correction
### Languages
English
## Dataset Structure
### Data Instances
An example from the TMU-GFM-Dataset looks as follows:
```
{'ave_f': 3.4000000953674316,
'ave_g': 3.4000000953674316,
'ave_m': 3.5999999046325684,
'fluency': [3, 4, 3, 4, 3],
'grammer': [3, 4, 3, 4, 3],
'meaning': [3, 4, 4, 4, 3],
'output': 'After all, there will be an endless battle between the technology and human mentality.',
'source': 'Afterall there will be an endless battle between the technology and human mentality.',
'system': 'lstm,cnn'}
```
### Data Fields
The are 9 columns in the tmu-gfm-dataset.
- source: source sentence.
- output: system output sentence.
- grammer: Grammaticaliry annotations by 5 annotators.
- fluency: Fluency annotations by 5 annotators.
- meaning: Meaning Preservation annotations by 5 annotators.
- system: Which system the output sentence is from.
- ave_g: Average grammer score.
- ave_f: Average fluency score.
- ave_m: Average meaning score.
### Data Splits
Authors divided the dataset into train/dev/test with 3,376/422/423 sentences and used for fine-tuning BERT in thier paper.
## Dataset Creation
### Curation Rationale
The authors proposed a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC).
They said that previous studies have shown that reference-less metrics are promising; however, existing metrics are not optimized for manual evaluation of the system output because there is no dataset of system output with manual evaluation.
To achieve a better correlation with manual evaluation, they created a dataset to optimize each sub-metric to the manual evaluation of GEC systems. Their annotators evaluated the output of five typical GEC systems.
### Source Data
#### Initial Data Collection and Normalization
Authors collected manual evaluations for the grammaticality, fluency, and meaning preservation of the system outputs of 1,381 sentences from CoNLL 2013.
To collect the manual evaluations for various system outputs, each source sentence was corrected by the following five typical systems: statistical machine translation (SMT) (Grundkiewicz and Junczys-Dowmunt, 2018), recurrent neural network (RNN) (Luong et al., 2015), convolutional neural network (CNN) (Chollampatt and Ng, 2018), self-attention network (SAN) (Vaswani et al., 2017), and SAN with copy mechanism (SAN+Copy) (Zhao et al., 2019).
#### Who are the source language producers?
machine-generated
### Annotations
#### Annotation process
By excluding duplicate corrected sentences, manual evaluation for the grammaticality, fluency, and meaning preservation were assigned to a total of 4,223 sentences, as follows:
- Grammaticality: Annotators evaluated the grammatical correctness of the system output. The authors followed the five-point scale evaluation criteria (4: Perfect, 3: Comprehensible, 2: Somewhat comprehensible, 1: Incomprehensible, and 0: Other) proposed by Heilman et al. (2014).
- Fluency: Annotators evaluated how natural the sentence sounds for native speakers. The authors followed the criteria (4: Extremely natural, 3: Somewhat natural, 2: Somewhat unnatural, and 1: Extremely unnatural) proposed by Lau et al. (2015).
- Meaning preservation: Annotators evaluated the extent to which the meaning of source sentences is preserved in system output. The authors followed the criteria (4: Identical, 3: Minor differences, 2: Moderate differences, 1: Sub- stantially different, and 0: Other) proposed by Xu et al. (2016).
Finally, the authors created a dataset with manual evaluations for a total of 4,221 sentences, excluding sentences in which three or more annotators answered “0: Other.”
#### Who are the annotators?
Five native English annotators reqruited by using Amazon Mechaincal turk
### 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
@inproceedings{yoshimura-etal-2020-reference,
title = "{SOME}: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction",
author = "Yoshimura, Ryoma and
Kaneko, Masahiro and
Kajiwara, Tomoyuki and
Komachi, Mamoru",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.coling-main.573",
pages = "6516--6522",
abstract = "We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC). Previous studies have shown that reference-less metrics are promising; however, existing metrics are not optimized for manual evaluations of the system outputs because no dataset of the system output exists with manual evaluation. This study manually evaluates outputs of GEC systems to optimize the metrics. Experimental results show that the proposed metric improves correlation with the manual evaluation in both system- and sentence-level meta-evaluation. Our dataset and metric will be made publicly available.",
}
### Contributions
Thanks to [@forest1988](https://github.com/forest1988) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mponty/code_tutorials | mponty | 2023-11-01T02:22:58Z | 377 | 4 | null | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"language:ru",
"language:zh",
"language:es",
"code",
"region:us"
] | 2023-11-01T02:22:58Z | 2023-10-31T14:32:09.000Z | 2023-10-31T14:32:09 | ---
dataset_info:
features:
- name: text
dtype: string
- name: url
dtype: string
- name: dump
dtype: string
- name: lang
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3124929718.313386
num_examples: 518410
download_size: 2971113091
dataset_size: 3124929718.313386
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
language:
- en
- ru
- zh
- es
tags:
- code
pretty_name: k
size_categories:
- 100K<n<1M
---
# Coding Tutorials
This comprehensive dataset consists of **500,000** documents, summing up to around **1.5 billion** tokens.
Predominantly composed of coding tutorials, it has been meticulously compiled from various web crawl datasets like **RefinedWeb**, **OSCAR**, and **Escorpius**.
The selection process involved a stringent filtering of files using regular expressions to ensure the inclusion of content that contains programming code (most of them).
These tutorials offer more than mere code snippets.
They provide an extensive context, including the rationale behind the code, the problem being addressed, and detailed step-by-step instructions.
This layered context is helpful for training a code-LM model, enabling it to discern the user intent behind a piece of code and thus facilitating more contextually relevant assistance.
### Programming Language Distribution
```
cpp ▏ 39% █████████████████████████
python ▏ 25% ████████████████
java ▏ 16% ██████████
csharp ▏ 3% ██
javascript ▏ 1% ▋
kotlin ▏ 1% ▋
other ▏ 14% █████████
```
### Natural language distribution
```
en ▏ 80% █████████████████████████
ru ▏ 16% █████
zh ▏ 2% ▋
es ▏ 2% ▋
``` | [
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cail2018 | null | 2022-11-18T19:24:58Z | 376 | 8 | chinese-ai-and-law-cail-2018 | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:zh",
"license:unknown",
"judgement-prediction",
"arxiv:1807.02478",
"region:us"
] | 2022-11-18T19:24:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: chinese-ai-and-law-cail-2018
pretty_name: CAIL 2018
tags:
- judgement-prediction
dataset_info:
features:
- name: fact
dtype: string
- name: relevant_articles
sequence: int32
- name: accusation
sequence: string
- name: punish_of_money
dtype: float32
- name: criminals
sequence: string
- name: death_penalty
dtype: bool
- name: imprisonment
dtype: float32
- name: life_imprisonment
dtype: bool
splits:
- name: exercise_contest_train
num_bytes: 220112732
num_examples: 154592
- name: exercise_contest_valid
num_bytes: 21702157
num_examples: 17131
- name: exercise_contest_test
num_bytes: 41057634
num_examples: 32508
- name: first_stage_train
num_bytes: 1779657510
num_examples: 1710856
- name: first_stage_test
num_bytes: 244335194
num_examples: 217016
- name: final_test
num_bytes: 44194707
num_examples: 35922
download_size: 984551626
dataset_size: 2351059934
---
---
# Dataset Card for CAIL 2018
## 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/thunlp/CAIL/blob/master/README_en.md)
- **Repository:** [Github](https://github.com/thunlp/CAIL)
- **Paper:** [Arxiv](https://arxiv.org/abs/1807.02478)
- **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 [@JetRunner](https://github.com/JetRunner) for adding this dataset. | [
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covid_qa_castorini | null | 2022-11-03T16:30:54Z | 376 | 0 | covidqa | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2004.11339",
"region:us"
] | 2022-11-03T16:30:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: covidqa
pretty_name: CovidQaCastorini
dataset_info:
- config_name: covid_qa_deepset
features:
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dtype: int32
- name: context
dtype: string
- name: question
dtype: string
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sequence:
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dtype: string
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dtype: int32
splits:
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num_bytes: 65151262
num_examples: 2019
download_size: 4418117
dataset_size: 65151262
- config_name: covidqa
features:
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dtype: string
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splits:
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num_bytes: 33757
num_examples: 27
download_size: 51438
dataset_size: 33757
- config_name: covid_qa_castorini
features:
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dtype: string
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dtype: string
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splits:
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num_examples: 27
download_size: 51438
dataset_size: 33757
---
# Dataset Card for [covid_qa_castorini]
## 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:** http://covidqa.ai
- **Repository:** https://github.com/castorini/pygaggle
- **Paper:** https://arxiv.org/abs/2004.11339
- **Point of Contact:** [Castorini research group @UWaterloo](https://github.com/castorini/)
### Dataset Summary
CovidQA is a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered
from Kaggle’s COVID-19 Open Research Dataset Challenge.
The dataset comprises 156 question-article pairs with 27 questions (topics) and 85 unique articles.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
**What do the instances that comprise the dataset represent?**
Each represents a question, a context (document passage from the CORD19 dataset) and an answer.
**How many instances are there in total?**
**What data does each instance consist of?**
Each instance is a query (natural language question and keyword-based), a set of answers, and a document id with its title associated with each answer.
[More Information Needed]
### Data Fields
The data was annotated in SQuAD style fashion, where each row contains:
* **question_query**: Natural language question query
* **keyword_query**: Keyword-based query
* **category_name**: Category in which the queries are part of
* **answers**: List of answers
* **id**: The document ID the answer is found on
* **title**: Title of the document of the answer
* **exact_answer**: Text (string) of the exact answer
### Data Splits
**data/kaggle-lit-review-0.2.json**: 156 question-article pairs with 27 questions (topics) and 85 unique articles from
CORD-19.
[More Information Needed]
## Dataset Creation
The dataset aims to help for guiding research until more substantial evaluation resources become available. Being a smaller dataset,
it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
[More Information Needed]
### Annotations
Five of the co-authors participated in this annotation effort, applying the aforementioned approach, with one lead
annotator responsible for approving topics and answering technical questions from the other annotators. Two annotators are
undergraduate students majoring in computer science, one is a science alumna, another is a computer science professor,
and the lead annotator is a graduate student in computer science—all affiliated with the University of Waterloo.
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The dataset was intended as a stopgap measure for guiding research until more substantial evaluation resources become available.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
While this dataset, comprising 124 question–article pairs as of the present version 0.1 release, does not have sufficient
examples for supervised machine learning, it can be helpful for evaluating the zero-shot or transfer capabilities
of existing models on topics specifically related to COVID-19.
## Additional Information
The listed authors in the homepage are maintaining/supporting the dataset.
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is licensed under the [Apache License 2.0](https://github.com/castorini/pygaggle/blob/master/LICENSE).
### Citation Information
```
@article{tang2020rapidly,
title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
journal={arXiv preprint arXiv:2004.11339},
year={2020}
}
```
### Contributions
Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset. | [
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Biddls/Onion_News | Biddls | 2023-03-25T12:57:47Z | 376 | 2 | null | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"license:mit",
"region:us"
] | 2023-03-25T12:57:47Z | 2023-03-25T12:50:01.000Z | 2023-03-25T12:50:01 | ---
license: mit
task_categories:
- summarization
- text2text-generation
- text-generation
- text-classification
language:
- en
pretty_name: OnionNewsScrape
---
## This is a dataset of Onion news articles:
Note
- The headers and body of the news article is split by a ' #~# ' token
- Lines with just the token had no body or no header and can be skipped
- Feel free to use the script provided to scape the latest version, it takes about 30 mins on an i7-6850K | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
amttl | null | 2023-01-25T14:26:23Z | 375 | 1 | null | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:mit",
"region:us"
] | 2023-01-25T14:26:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
pretty_name: AMTTL
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: tags
sequence:
class_label:
names:
'0': B
'1': I
'2': E
'3': S
config_name: amttl
splits:
- name: train
num_bytes: 1132212
num_examples: 3063
- name: validation
num_bytes: 324374
num_examples: 822
- name: test
num_bytes: 328525
num_examples: 908
download_size: 685534
dataset_size: 1785111
---
# Dataset Card for AMTTL
## 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/adapt-sjtu/AMTTL/tree/master/medical_data)
- **Repository:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
- **Paper:** [Aclweb](http://aclweb.org/anthology/C18-1307)
- **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
```bibtex
@inproceedings{xing2018adaptive,
title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
pages={3619--3630},
year={2018}
}
```
### Contributions
Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset. | [
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Graphcore/wikipedia-bert-128 | Graphcore | 2022-09-07T14:42:32Z | 375 | 0 | null | [
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-09-07T14:42:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
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license:
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gamino/wiki_medical_terms | gamino | 2022-12-20T16:23:58Z | 375 | 26 | null | [
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"size_categories:1K<n<10K",
"language:en",
"license:gpl-3.0",
"medical",
"conditions",
"region:us"
] | 2022-12-20T16:23:58Z | 2022-12-20T15:25:02.000Z | 2022-12-20T15:25:02 | ---
annotations_creators:
- other
language:
- en
language_creators:
- other
license:
- gpl-3.0
multilinguality: []
pretty_name: Medical terms and their wikipedia text
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- medical
- conditions
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
### Dataset Summary
This data set contains over 6,000 medical terms and their wikipedia text. It is intended to be used on a downstream task that requires medical terms and their wikipedia explanation.
## 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
### Citation Information
[More Information Needed]
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
facat/sci-llm-part | facat | 2023-10-07T13:33:53Z | 375 | 1 | null | [
"region:us"
] | 2023-10-07T13:33:53Z | 2023-10-04T06:21:06.000Z | 2023-10-04T06:21:06 | ---
configs:
- config_name: default
data_files:
- split: gpt1
path: data/gpt1-*
- split: gpt2
path: data/gpt2-*
- split: gpt3
path: data/gpt3-*
- split: gpt4
path: data/gpt4-*
- split: gpt5
path: data/gpt5-*
- split: gpt6
path: data/gpt6-*
- split: han_40k
path: data/han_40k-*
- split: base_60k
path: data/base_60k-*
- split: test
path: data/test-*
- split: test2
path: data/test2-*
dataset_info:
features:
- name: prompt
dtype: string
- name: context
dtype: string
- name: chosen
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
splits:
- name: gpt1
num_bytes: 130420316
num_examples: 22113
- name: gpt2
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- name: gpt3
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- name: gpt4
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- name: gpt5
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- name: gpt6
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num_examples: 18325
- name: han_40k
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num_examples: 40807
- name: base_60k
num_bytes: 292172331
num_examples: 54209
- name: test
num_bytes: 2214599
num_examples: 500
- name: test2
num_bytes: 1111116
num_examples: 200
download_size: 311808265
dataset_size: 1543160982
---
# Dataset Card for "sci-llm-part"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jon-tow/okapi_mmlu | jon-tow | 2023-10-24T00:03:08Z | 375 | 2 | null | [
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] | 2023-10-24T00:03:08Z | 2023-10-23T22:18:44.000Z | 2023-10-23T22:18:44 | ---
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- mr
- ne
- nl
- pt
- ro
- ru
- sk
- sr
- sv
- ta
- te
- uk
- vi
license: cc-by-nc-4.0
---
# okapi_mmlu
<!-- Provide a quick summary of the dataset. -->
Multilingual translation of [Measuring Massive Multitask Language Understanding (MMLU)](https://arxiv.org/abs/2009.03300).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
MMLU is a benchmark that measured a text model’s multitask accuracy.
The test covers 57 tasks including elementary mathematics, US history, computer
science, law, and more. To attain high accuracy on this test, models must possess
extensive world knowledge and problem solving ability. By comprehensively evaluating the breadth and depth of a model’s academic and professional understanding, MMLU can be used to analyze models across many tasks and to identify important shortcomings.
- **Curated by:** Dac Lai, Viet and Van Nguyen, Chien and Ngo, Nghia Trung and Nguyen, Thuat and Dernoncourt, Franck and Rossi, Ryan A and Nguyen, Thien Huu
- **License:** The datasets are CC BY NC 4.0 (allowing only non-commercial use).
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** http://nlp.uoregon.edu/download/okapi-eval/datasets/
- **Paper:** Okapi ([Lai et al., 2023](https://arxiv.org/abs/2307.16039))
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```bibtex
@article{dac2023okapi,
title={Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback},
author={Dac Lai, Viet and Van Nguyen, Chien and Ngo, Nghia Trung and Nguyen, Thuat and Dernoncourt, Franck and Rossi, Ryan A and Nguyen, Thien Huu},
journal={arXiv e-prints},
pages={arXiv--2307},
year={2023}
}
```
```bibtex
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
danish_political_comments | null | 2023-11-24T16:12:39Z | 374 | 0 | null | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:da",
"license:unknown",
"region:us"
] | 2023-11-24T16:12:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- da
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: DanishPoliticalComments
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': '2'
'1': '1'
'2': '0'
'3': '-1'
'4': '-2'
splits:
- name: train
num_bytes: 829569
num_examples: 9008
download_size: 690873
dataset_size: 829569
---
# Dataset Card for DanishPoliticalComments
## 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/steffan267/Sentiment-Analysis-on-Danish-Social-Media
- **Repository:** https://github.com/steffan267/Sentiment-Analysis-on-Danish-Social-Media
- **Paper:** https://github.com/lucaspuvis/SAM/blob/master/Thesis.pdf
- **Point of Contact:** [More Information Needed]
### Dataset Summary
The dataset consists of 9008 sentences that are labeled with fine-grained polarity in the range from -2 to 2 (negative to positive). The quality of the fine-grained is not cross-validated and is therefore subject to uncertainties; however, the simple polarity has been cross-validated and therefore is considered to be more correct.
### 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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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 [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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Babelscape/rebel-dataset | Babelscape | 2023-06-15T12:12:59Z | 374 | 15 | null | [
"task_categories:text-retrieval",
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"relation-extraction",
"conditional-text-generation",
"arxiv:2005.00614",
"region:us"
] | 2023-06-15T12:12:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-retrieval
- text-generation
task_ids: []
pretty_name: rebel-dataset
tags:
- relation-extraction
- conditional-text-generation
---
cc-by-nc-sa-4.0---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-retrieval
- text-generation
task_ids: []
pretty_name: rebel-dataset
tags:
- relation-extraction
- conditional-text-generation
---
# Dataset Card for REBEL dataset
## Table of Contents
- [Dataset Card for REBEL dataset](#dataset-card-for-rebel)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/Babelscape/rebel](https://github.com/Babelscape/rebel)
- **Paper:** [https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf)
- **Point of Contact:** [huguetcabot@babelscape.com](huguetcabot@babelscape.com)
### Dataset Summary
Dataset created for [REBEL](https://huggingface.co/Babelscape/rebel-large) dataset from interlinking Wikidata and Wikipedia for Relation Extraction, filtered using NLI.
### Supported Tasks and Leaderboards
- `text-retrieval-other-relation-extraction`: The dataset can be used to train a model for Relation Extraction, which consists in extracting triplets from raw text, made of subject, object and relation type. Success on this task is typically measured by achieving a *high* [F1](https://huggingface.co/metrics/F1). The [BART](https://huggingface.co/transformers/model_doc/bart.html)) model currently achieves the following score: 74 Micro F1 and 51 Macro F1 for the 220 most frequent relation types.
### Languages
The dataset is in English, from the English Wikipedia.
## Dataset Structure
### Data Instances
REBEL
- `Size of downloaded dataset files`: 1490.02 MB
- `Size of the generated dataset`: 1199.27 MB
- `Total amount of disk used`: 2689.29 MB
```
{
'id': 'Q82442-1',
'title': 'Arsène Lupin, Gentleman Burglar',
'context': 'Arsène Lupin , Gentleman Burglar is the first collection of stories by Maurice Leblanc recounting the adventures of Arsène Lupin , released on 10 June 1907 .',
'triplets': '<triplet> Arsène Lupin, Gentleman Burglar <subj> Maurice Leblanc <obj> author <triplet> Arsène Lupin <subj> Maurice Leblanc <obj> creator'
}
```
The original data is in jsonl format and contains much more information. It is divided by Wikipedia articles instead of by sentence, and contains metadata about Wikidata entities, their boundaries in the text, how it was annotated, etc. For more information check the [paper repository](https://huggingface.co/Babelscape/rebel-large) and how it was generated using the Relation Extraction dataset pipeline, [cRocoDiLe](https://github.com/Babelscape/crocodile).
### Data Fields
List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
- `id`: ID of the instance. It contains a unique id matching to a Wikipedia page and a number separated by a hyphen indicating which sentence of the Wikipedia article it is.
- `title`: Title of the Wikipedia page the sentence comes from.
- `context`: Text from Wikipedia articles that serves as context for the Relation Extraction task.
- `triplets`: Linearized version of the triplets present in the text, split by the use of special tokens. For more info on this linearization check the [paper](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf).
### Data Splits
Test and Validation splits are each 5% of the original data.
Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Sentences | 3,120,296 | 172,860 | 173,601 |
| Input Sentences (top 220 relation types as used in original paper) | 784,202 | 43,341 | 43,506 |
| Number of Triplets (top 220 relation types as used in original paper) | 878,555 | 48,514 | 48,852 |
## Dataset Creation
### Curation Rationale
This dataset was created to enable the training of a BART based model as pre-training phase for Relation Extraction as seen in the paper [REBEL: Relation Extraction By End-to-end Language generation](https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf).
### Source Data
Data comes from Wikipedia text before the table of contents, as well as Wikidata for the triplets annotation.
#### Initial Data Collection and Normalization
For the data collection, the dataset extraction pipeline [cRocoDiLe: Automati**c** **R**elati**o**n Extra**c**ti**o**n **D**ataset w**i**th N**L**I filt**e**ring](https://github.com/Babelscape/crocodile) insipired by [T-REx Pipeline](https://github.com/hadyelsahar/RE-NLG-Dataset) more details found at: [T-REx Website](https://hadyelsahar.github.io/t-rex/). The starting point is a Wikipedia dump as well as a Wikidata one.
After the triplets are extracted, an NLI system was used to filter out those not entailed by the text.
#### Who are the source language producers?
Any Wikipedia and Wikidata contributor.
### Annotations
#### Annotation process
The dataset extraction pipeline [cRocoDiLe: Automati**c** **R**elati**o**n Extra**c**ti**o**n **D**ataset w**i**th N**L**I filt**e**ring](https://github.com/Babelscape/crocodile).
#### Who are the annotators?
Automatic annottations
### Personal and Sensitive Information
All text is from Wikipedia, any Personal or Sensitive Information there may be present in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset serves as a pre-training step for Relation Extraction models. It is distantly annotated, hence it should only be used as such. A model trained solely on this dataset may produce allucinations coming from the silver nature of the dataset.
### Discussion of Biases
Since the dataset was automatically created from Wikipedia and Wikidata, it may reflect the biases withing those sources.
For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
For Wikidata, there are class imbalances, also resulting from Wikipedia.
### Other Known Limitations
Not for now
## Additional Information
### Dataset Curators
Pere-Lluis Huguet Cabot - Babelscape and Sapienza University of Rome, Italy
Roberto Navigli - Sapienza University of Rome, Italy
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{huguet-cabot-navigli-2021-rebel,
title = "REBEL: Relation Extraction By End-to-end Language generation",
author = "Huguet Cabot, Pere-Llu{\'\i}s and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf",
}
```
### Contributions
Thanks to [@littlepea13](https://github.com/LittlePea13) for adding this dataset. | [
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SetFit/amazon_polarity | SetFit | 2022-01-19T20:49:58Z | 374 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
howard-hou/OCR-VQA | howard-hou | 2023-04-24T01:29:24Z | 374 | 7 | null | [
"region:us"
] | 2023-04-24T01:29:24Z | 2023-04-23T17:43:27.000Z | 2023-04-23T17:43:27 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: string
- name: questions
sequence: string
- name: answers
sequence: string
- name: ocr_tokens
sequence: string
- name: ocr_info
list:
- name: word
dtype: string
- name: bounding_box
struct:
- name: width
dtype: float64
- name: height
dtype: float64
- name: top_left_x
dtype: float64
- name: top_left_y
dtype: float64
- name: title
dtype: string
- name: authorName
dtype: string
- name: genre
dtype: string
- name: image_width
dtype: int64
- name: image_height
dtype: int64
- name: image_url
dtype: string
- name: set_name
dtype: string
splits:
- name: train
num_bytes: 7503971854.0
num_examples: 166022
- name: test
num_bytes: 928616409.0
num_examples: 20796
- name: validation
num_bytes: 920236957.0
num_examples: 20731
download_size: 2329997099
dataset_size: 9352825220.0
---
# Dataset Card for "OCR-VQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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shibing624/medical | shibing624 | 2023-06-02T07:03:41Z | 374 | 164 | null | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"language:en",
"license:apache-2.0",
"text-generation",
"region:us"
] | 2023-06-02T07:03:41Z | 2023-05-22T14:45:06.000Z | 2023-05-22T14:45:06 | ---
license: apache-2.0
language:
- zh
- en
tags:
- text-generation
pretty_name: medical
task_categories:
- text-generation
size_categories:
- 1M<n<10M
---
# Dataset Card for medical
中文医疗数据集
- LLM Supervised Finetuning repository: https://github.com/shibing624/textgen
- MeidcalGPT repository: https://github.com/shibing624/MedicalGPT
## Dataset Description
medical is a Chinese Medical dataset. 医疗数据集,可用于医疗领域大模型训练。
```
tree medical
|-- finetune # 监督微调数据集,可用于SFT和RLHF
| |-- test_en_1.json
| |-- test_zh_0.json
| |-- train_en_1.json
| |-- train_zh_0.json
| |-- valid_en_1.json
| `-- valid_zh_0.json
|-- medical.py # hf dataset 数据展示用
|-- pretrain # 二次预训练数据集
| |-- medical_book_zh.json
| |-- test_encyclopedia.json
| |-- train_encyclopedia.json
| `-- valid_encyclopedia.json
|-- README.md
`-- reward # 奖励模型数据集
|-- test.json
|-- train.json
`-- valid.json
```
### Original Dataset Summary
#### pretrain
- train_encyclopedia.json: 共36万条,来自医疗百科数据[FreedomIntelligence/huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa) , 拼接 questions 和 answers,形成 text 文本字段,语句通顺,用于预训练注入医疗知识。
- medical_book_zh.json: 共8475条,来自医疗教材的文本数据,来源:https://github.com/jind11/MedQA, 原始数据集:[google drive](https://drive.google.com/u/0/uc?export=download&confirm=t&id=1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw) ,只对长段落切分为2048字的小段落了。
#### finetune
- train_zh_0.json: 共195万条,来自1)中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的六个科室医疗问诊数据,
有79万条;2)在线医疗百科 huatuo_encyclopedia_qa ,有36万条;3)医疗知识图谱 huatuo_knowledge_graph_qa,有79万条。三部分合并,共195万条。
- train_en_1.json:共11万条,来自英文医疗问诊对话数据[Kent0n-Li/ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor),合并了HealthCareMagic-100k、GenMedGPT-5k 数据集,共11万条。
#### reward
- train.json 共4000条,问题来自中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的随机4000条提问,`response_chosen`来自该数据集的医生答复,
`response_rejected`来自本草模型[SCIR-HI/Huatuo-Llama-Med-Chinese](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese)的答复。
### Supported Tasks and Leaderboards
中文医疗对话模型
The dataset designed for medical task training pretrained language models.
### Languages
The data are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
head pretrain/train_encyclopedia.json
```json
{"text": "怀孕后嘴巴很淡怎么办?有孕妇在怀孕之后,发现自己嘴巴比较淡,出现这种情况的原因其实也非常的复杂,首先和妊娠反应有直接的关系,这是一种正常的情况,另外有些孕妇平常不注意口腔卫生,舌苔比较厚或者自己有了一些消化系统方面的疾病,这就要求人们必须要及时的进行处理。女性在怀孕之后,身体就会出现一些明显的变化,首先人们月经会停止,另外也会有恶心、呕吐等一些妊娠反应,不过这些都是正常的。有些孕妇发现自己在怀孕之后,口味发生了很大的变化,嘴巴变得非常的淡。其实这也和激素变化有直接的关系,可能是妊娠反应所致,在怀孕期间,因为受到体内激素水平的变化,所以就会有肠胃系统的改变,人们可能会出现食欲不振,消化不良等症状表现,也有一些孕妇会发现自己嘴巴没有味道,会有口苦的症状,而这也是正常的孕期反应,人们在平常要多喝一些水,多吃一些清淡营养的食物慢慢就会改善。也有可能是舌苔过厚所致,孕妇嘴巴里面没有味道,很有可能是舌苔比较重、舌苔过厚导致的,这样就会影响到味蕾对味道的敏感度,不仅嘴巴里面没有味道,甚至有些孕妇在说话的时候也会有口臭,这就要求人们在每天早上漱口的时候,必须要用牙刷刷舌苔开始,不要清理的特别深,以免人们会有呕吐,慢慢习惯之后再往深一点的清洗,一般2到3天就会得到改善。嘴巴感到比较淡,其实也和脾胃虚寒有直接的关系,消化系统疾病,内分泌疾病,营养不良等,但有可能导致舌头部位因为味蕾的敏感度下降,产生口淡之感,患者会有食欲不振的表现,发现病症及时就诊治疗。"}
```
head finetune/train_zh_0.json
```json
{"instruction": "曲匹地尔片的用法用量", "input": "", "output": "注意:同种药品可由于不同的包装规格有不同的用法或用量。本文只供参考。如果不确定,请参看药品随带的说明书或向医生询问。口服。一次50~100mg(1-2片),3次/日,或遵医嘱。"}
```
head finetune/train_en_1.json
```json
{"instruction": "If you are a doctor, please answer the medical questions based on the patient's description.", "input": "Doctor, I have been experiencing sudden and frequent panic attacks. I don't know what to do.", "output": "Well, based on what you're telling me, it sounds like you may be suffering from panic disorder. The best course of action is to start with psychotherapy and mental health counseling. Additionally, we should conduct an electrocardiogram to make sure that there are no physical issues causing your panic attacks. We will also need to perform a depression screen and a toxicology screen to rule out any other underlying causes. Finally, I would recommend a comprehensive psychological and psychiatric evaluation and therapy to help manage your symptoms."}
```
head reward/train.json
```json
{"question": "孩子感统失调,学习不行怎么办?,孩子说话晚,走路晚,现在小学二年级,学习跟不上,理解力差,运动协调性差,家里很着急,不知怎么办。", "response_chosen": "病情分析:你好!孩子说话晚,走路也晚,很可能是大脑本身发育不好引起的发育迟缓。而五岁时所致的智力检查为临界范围,那就是说孩子的智商是有问题的,也应考虑与大脑发育不好有关。指导意见:人的大脑在头一年发育最快,可塑性最强,在头三年可塑性还是可以的,超过三岁再进行训练,效果就不怎么好了。建议再给孩子做一做智力测试,如果孩子的智商还是在临界范围,那就要考虑让孩子去特殊学校进行康复训练,而不是继续在普通小学就读,否则对孩子来说,就是强人所难了。希望自己的孩子能聪明,这是每个家长都会有的心愿,但如果孩子自身的条件就是不能跟上同龄孩子,那家长也要面对这个事实的,对吗?医生询问:", "response_rejected": "建议家长先带孩子去正规医院做全面检查以确定病因和病情严重程度;同时可以进行物理治疗、康复训练等辅助治疗方法。"}
```
### Data Fields
#### 预训练数据集 pretrain
字段解释:
- text: 文本
#### 指令微调数据集 finetune
字段解释:
- instruction: 指令
- input:问题(可为空)
- output:答复
#### 奖励模型数据集 reward
字段解释:
- question: 问题
- response_chosen: 优质回答
- response_rejected: 低质回答
### Data Splits
```
> wc -l medical/*/*
500 medical/finetune/test_en_1.json
500 medical/finetune/test_zh_0.json
116617 medical/finetune/train_en_1.json
1949972 medical/finetune/train_zh_0.json
500 medical/finetune/valid_en_1.json
500 medical/finetune/valid_zh_0.json
8475 medical/pretrain/medical_book_zh.json
500 medical/pretrain/test_encyclopedia.json
361420 medical/pretrain/train_encyclopedia.json
500 medical/pretrain/valid_encyclopedia.json
100 medical/reward/test.json
3800 medical/reward/train.json
100 medical/reward/valid.json
2443484 total
```
### Licensing Information
The dataset is available under the Apache 2.0.
### Citation Information
- https://github.com/Toyhom/Chinese-medical-dialogue-data
- https://github.com/FreedomIntelligence/Huatuo-26M/blob/main/README_zh-CN.md
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa
- https://github.com/Kent0n-Li/ChatDoctor
附上几个优质的reward model dataset:
- https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise
- https://huggingface.co/datasets/sunzeyeah/chinese_chatgpt_corpus
- https://huggingface.co/datasets/Cohere/miracl-zh-queries-22-12
- https://huggingface.co/datasets/Dahoas/rm-static
### Contributions
[shibing624](https://github.com/shibing624) 整理并上传
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
KShivendu/dbpedia-entities-openai-1M | KShivendu | 2023-11-16T04:34:14Z | 374 | 9 | null | [
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"region:us"
] | 2023-11-16T04:34:14Z | 2023-06-20T22:29:43.000Z | 2023-06-20T22:29:43 | ---
license: mit
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: openai
sequence: float32
splits:
- name: train
num_bytes: 12383152
num_examples: 1000000
download_size: 12383152
dataset_size: 1000000
language:
- en
pretty_name: OpenAI 1M with DBPedia Entities
size_categories:
- 1M<n<10M
---
1M OpenAI Embeddings -- 1536 dimensions
Created: June 2023.
Text used for Embedding: title (string) + text (string)
Embedding Model: text-embedding-ada-002
First used for the pgvector vs VectorDB (Qdrant) benchmark: https://nirantk.com/writing/pgvector-vs-qdrant/
### Future work
We are planning to take this up to 10M (and possibly 100M) vectors. Contact [@KShivendu_](https://twitter.com/KShivendu_) on Twitter or mail to hello@nirantk.com if you want to help :)
### Credits:
This dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
edarchimbaud/perimeter-stocks | edarchimbaud | 2023-11-21T15:00:09Z | 374 | 1 | null | [
"region:us"
] | 2023-11-21T15:00:09Z | 2023-08-12T20:21:35.000Z | 2023-08-12T20:21:35 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: security
dtype: string
- name: gics_sector
dtype: string
- name: gics_sub_industry
dtype: string
splits:
- name: train
num_bytes: 111992
num_examples: 1500
download_size: 44216
dataset_size: 111992
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "perimeter-stocks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ai4privacy/pii-masking-200k | ai4privacy | 2023-11-17T07:07:12Z | 374 | 14 | null | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:fr",
"language:de",
"language:it",
"legal",
"business",
"psychology",
"privacy",
"region:us"
] | 2023-11-17T07:07:12Z | 2023-11-06T03:34:07.000Z | 2023-11-06T03:34:07 | ---
language:
- en
- fr
- de
- it
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
multilinguality:
- multilingual
tags:
- legal
- business
- psychology
- privacy
size_categories:
- 100K<n<1M
pretty_name: Ai4Privacy PII200k Dataset
source_datasets:
- original
---
# Purpose and Features
World's largest open source privacy dataset.
The purpose of the dataset is to train models to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
The example texts have **54 PII classes** (types of sensitive data), targeting **229 discussion subjects / use cases** split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...).
Key facts:
- Size: 13.6m text tokens in ~209k examples with 649k PII tokens (see [summary.json](summary.json))
- 4 languages, more to come!
- English
- French
- German
- Italian
- Synthetic data generated using proprietary algorithms
- No privacy violations!
- Human-in-the-loop validated high quality dataset
# Getting started
Option 1: Python
```terminal
pip install datasets
```
```python
from datasets import load_dataset
dataset = load_dataset("ai4privacy/pii-masking-200k", data_files=["*.jsonl"])
```
# Token distribution across PII classes
We have taken steps to balance the token distribution across PII classes covered by the dataset.
This graph shows the distribution of observations across the different PII classes in this release:

There is 1 class that is still overrepresented in the dataset: firstname.
We will further improve the balance with future dataset releases.
This is the token distribution excluding the FIRSTNAME class:

# Compatible Machine Learning Tasks:
- Tokenclassification. Check out a HuggingFace's [guide on token classification](https://huggingface.co/docs/transformers/tasks/token_classification).
- [ALBERT](https://huggingface.co/docs/transformers/model_doc/albert), [BERT](https://huggingface.co/docs/transformers/model_doc/bert), [BigBird](https://huggingface.co/docs/transformers/model_doc/big_bird), [BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt), [BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom), [BROS](https://huggingface.co/docs/transformers/model_doc/bros), [CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert), [CANINE](https://huggingface.co/docs/transformers/model_doc/canine), [ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert), [Data2VecText](https://huggingface.co/docs/transformers/model_doc/data2vec-text), [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta), [DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2), [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert), [ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra), [ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie), [ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m), [ESM](https://huggingface.co/docs/transformers/model_doc/esm), [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon), [FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert), [FNet](https://huggingface.co/docs/transformers/model_doc/fnet), [Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel), [GPT-Sw3](https://huggingface.co/docs/transformers/model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo), [GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox), [I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert), [LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm), [LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2), [LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3), [LiLT](https://huggingface.co/docs/transformers/model_doc/lilt), [Longformer](https://huggingface.co/docs/transformers/model_doc/longformer), [LUKE](https://huggingface.co/docs/transformers/model_doc/luke), [MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm), [MEGA](https://huggingface.co/docs/transformers/model_doc/mega), [Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert), [MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert), [MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet), [MPT](https://huggingface.co/docs/transformers/model_doc/mpt), [MRA](https://huggingface.co/docs/transformers/model_doc/mra), [Nezha](https://huggingface.co/docs/transformers/model_doc/nezha), [Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer), [QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert), [RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert), [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co/docs/transformers/model_doc/roc_bert), [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer), [SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm), [XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet), [X-MOD](https://huggingface.co/docs/transformers/model_doc/xmod), [YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)
- Text Generation: Mapping the unmasked_text to to the masked_text or privacy_mask attributes. Check out HuggingFace's [guide to fine-tunning](https://huggingface.co/docs/transformers/v4.15.0/training)
- [T5 Family](https://huggingface.co/docs/transformers/model_doc/t5), [Llama2](https://huggingface.co/docs/transformers/main/model_doc/llama2)
# Information regarding the rows:
- Each row represents a json object with a natural language text that includes placeholders for PII (and could plausibly be written by a human to an AI assistant).
- Sample row:
- "masked_text" contains a PII free natural text
- "Product officially launching in [COUNTY_1]. Estimate profit of [CURRENCYSYMBOL_1][AMOUNT_1]. Expenses by [ACCOUNTNAME_1].",
- "unmasked_text" shows a natural sentence containing PII
- "Product officially launching in Washington County. Estimate profit of $488293.16. Expenses by Checking Account."
- "privacy_mask" indicates the mapping between the privacy token instances and the string within the natural text.*
- "{'[COUNTY_1]': 'Washington County', '[CURRENCYSYMBOL_1]': '$', '[AMOUNT_1]': '488293.16', '[ACCOUNTNAME_1]': 'Checking Account'}"
- "span_labels" is an array of arrays formatted in the following way [start, end, pii token instance].*
- "[[0, 32, 'O'], [32, 49, 'COUNTY_1'], [49, 70, 'O'], [70, 71, 'CURRENCYSYMBOL_1'], [71, 80, 'AMOUNT_1'], [80, 94, 'O'], [94, 110, 'ACCOUNTNAME_1'], [110, 111, 'O']]",
- "bio_labels" follows the common place notation for "beginning", "inside" and "outside" of where each private tokens starts.[original paper](https://arxiv.org/abs/cmp-lg/9505040)
-["O", "O", "O", "O", "B-COUNTY", "I-COUNTY", "O", "O", "O", "O", "B-CURRENCYSYMBOL", "O", "O", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "O", "O", "O", "B-ACCOUNTNAME", "I-ACCOUNTNAME", "O"],
- "tokenised_text" breaks down the unmasked sentence into tokens using Bert Family tokeniser to help fine-tune large language models.
- ["product", "officially", "launching", "in", "washington", "county", ".", "estimate", "profit", "of", "$", "48", "##8", "##29", "##3", ".", "16", ".", "expenses", "by", "checking", "account", "."]
*note for the nested objects, we store them as string to maximise compability between various software.
# About Us:
At Ai4Privacy, we are commited to building the global seatbelt of the 21st century for Artificial Intelligence to help fight against potential risks of personal information being integrated into data pipelines.
Newsletter & updates: [www.Ai4Privacy.com](www.Ai4Privacy.com)
- Looking for ML engineers, developers, beta-testers, human in the loop validators (all languages)
- Integrations with already existing open source solutions
- Ask us a question on discord: [https://discord.gg/kxSbJrUQZF](https://discord.gg/kxSbJrUQZF)
# Roadmap and Future Development
- Carbon Neutral
- Benchmarking
- Better multilingual and especially localisation
- Extended integrations
- Continuously increase the training set
- Further optimisation to the model to reduce size and increase generalisability
- Next released major update is planned for the 14th of December 2023 (subscribe to newsletter for updates)
# Use Cases and Applications
**Chatbots**: Incorporating a PII masking model into chatbot systems can ensure the privacy and security of user conversations by automatically redacting sensitive information such as names, addresses, phone numbers, and email addresses.
**Customer Support Systems**: When interacting with customers through support tickets or live chats, masking PII can help protect sensitive customer data, enabling support agents to handle inquiries without the risk of exposing personal information.
**Email Filtering**: Email providers can utilize a PII masking model to automatically detect and redact PII from incoming and outgoing emails, reducing the chances of accidental disclosure of sensitive information.
**Data Anonymization**: Organizations dealing with large datasets containing PII, such as medical or financial records, can leverage a PII masking model to anonymize the data before sharing it for research, analysis, or collaboration purposes.
**Social Media Platforms**: Integrating PII masking capabilities into social media platforms can help users protect their personal information from unauthorized access, ensuring a safer online environment.
**Content Moderation**: PII masking can assist content moderation systems in automatically detecting and blurring or redacting sensitive information in user-generated content, preventing the accidental sharing of personal details.
**Online Forms**: Web applications that collect user data through online forms, such as registration forms or surveys, can employ a PII masking model to anonymize or mask the collected information in real-time, enhancing privacy and data protection.
**Collaborative Document Editing**: Collaboration platforms and document editing tools can use a PII masking model to automatically mask or redact sensitive information when multiple users are working on shared documents.
**Research and Data Sharing**: Researchers and institutions can leverage a PII masking model to ensure privacy and confidentiality when sharing datasets for collaboration, analysis, or publication purposes, reducing the risk of data breaches or identity theft.
**Content Generation**: Content generation systems, such as article generators or language models, can benefit from PII masking to automatically mask or generate fictional PII when creating sample texts or examples, safeguarding the privacy of individuals.
(...and whatever else your creative mind can think of)
# Support and Maintenance
AI4Privacy is a project affiliated with [AISuisse SA](https://www.aisuisse.com/). | [
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rahular/itihasa | rahular | 2022-10-24T18:06:01Z | 373 | 4 | null | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:sa",
"language:en",
"license:apache-2.0",
"conditional-text-generation",
"region:us"
] | 2022-10-24T18:06:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- sa
- en
license:
- apache-2.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: Itihasa
metrics:
- bleu
- sacrebleu
- rouge
- ter
- chrF
tags:
- conditional-text-generation
---
# Itihāsa
Itihāsa is a Sanskrit-English translation corpus containing 93,000 Sanskrit shlokas and their English translations extracted from M. N. Dutt's seminal works on The Rāmāyana and The Mahābhārata. The paper which introduced this dataset can be found [here](https://aclanthology.org/2021.wat-1.22/).
This repository contains the randomized train, development, and test sets. The original extracted data can be found [here](https://github.com/rahular/itihasa/tree/gh-pages/res) in JSON format. If you just want to browse the data, you can go [here](http://rahular.com/itihasa/).
## Usage
```
>> from datasets import load_dataset
>> dataset = load_dataset("rahular/itihasa")
>> dataset
DatasetDict({
train: Dataset({
features: ['translation'],
num_rows: 75162
})
validation: Dataset({
features: ['translation'],
num_rows: 6149
})
test: Dataset({
features: ['translation'],
num_rows: 11722
})
})
>> dataset['train'][0]
{'translation': {'en': 'The ascetic Vālmīki asked Nārada, the best of sages and foremost of those conversant with words, ever engaged in austerities and Vedic studies.',
'sn': 'ॐ तपः स्वाध्यायनिरतं तपस्वी वाग्विदां वरम्। नारदं परिपप्रच्छ वाल्मीकिर्मुनिपुङ्गवम्॥'}}
```
## Citation
If you found this dataset to be useful, please consider citing the paper as follows:
```
@inproceedings{aralikatte-etal-2021-itihasa,
title = "Itihasa: A large-scale corpus for {S}anskrit to {E}nglish translation",
author = "Aralikatte, Rahul and
de Lhoneux, Miryam and
Kunchukuttan, Anoop and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wat-1.22",
pages = "191--197",
abstract = "This work introduces Itihasa, a large-scale translation dataset containing 93,000 pairs of Sanskrit shlokas and their English translations. The shlokas are extracted from two Indian epics viz., The Ramayana and The Mahabharata. We first describe the motivation behind the curation of such a dataset and follow up with empirical analysis to bring out its nuances. We then benchmark the performance of standard translation models on this corpus and show that even state-of-the-art transformer architectures perform poorly, emphasizing the complexity of the dataset.",
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DataProvenanceInitiative/flan2021_submix_original | DataProvenanceInitiative | 2023-10-16T17:30:45Z | 373 | 0 | null | [
"region:us"
] | 2023-10-16T17:30:45Z | 2023-10-16T17:28:22.000Z | 2023-10-16T17:28:22 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 8988026240
num_examples: 5362361
download_size: 5486287486
dataset_size: 8988026240
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "flan2021_submix_original"
[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 | |
sentence-transformers/reddit-title-body | sentence-transformers | 2021-10-19T09:20:35Z | 372 | 8 | null | [
"region:us"
] | 2021-10-19T09:20:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Reddit (Title, Body)-Pairs
This dataset contains jsonl-Files about (title, body) pairs from Reddit. Each line is a JSON object of the following format:
```
{'title': 'The title of a thread', 'body': 'The longer body of the thread', 'subreddit': 'subreddit_name'}
```
The 2021 file contains submissions up until including 2021-06. Entries in the respective files are shuffled on a monthly basis.
The data has been filtered for:
- Remove threads with an upvote_ratio < 0.5
- Only include threads with a title more than 25 characters and bodies with `len(title)+25 < len(body) < 4096`
- Only keep threads with at least 3 comments or at least 3 upvotes.
## Overview
| File | Lines |
| --- | :---: |
| reddit_title_text_2010.jsonl.gz | 431,782
| reddit_title_text_2011.jsonl.gz | 1,673,264
| reddit_title_text_2012.jsonl.gz | 3,727,526
| reddit_title_text_2013.jsonl.gz | 5,713,956
| reddit_title_text_2014.jsonl.gz | 8,538,976
| reddit_title_text_2015.jsonl.gz | 11,064,453
| reddit_title_text_2016.jsonl.gz | 12,224,789
| reddit_title_text_2017.jsonl.gz | 13,558,139
| reddit_title_text_2018.jsonl.gz | 15,552,110
| reddit_title_text_2019.jsonl.gz | 19,224,970
| reddit_title_text_2020.jsonl.gz | 23,030,988
| reddit_title_text_2021.jsonl.gz | 12,704,958
Note: The data comes from [Pushshift](https://files.pushshift.io/reddit/). Please have a look at the respective license of Reddit and Pushshift before using the data.
Be aware that this dataset is not filtered for biases, hate-speech, spam, racial slurm etc. It depicts the content as it is posted on Reddit. | [
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lmqg/qag_zhquad | lmqg | 2023-11-07T19:15:25Z | 372 | 0 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10k<n<100K",
"source_datasets:lmqg/qag_zhquad",
"language:ja",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2023-11-07T19:15:25Z | 2023-11-07T11:24:53.000Z | 2023-11-07T11:24:53 | ---
license: cc-by-sa-4.0
pretty_name: Chinese SQuAD for question generation
language: ja
multilinguality: monolingual
size_categories: 10k<n<100K
source_datasets: lmqg/qag_zhquad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qag_zhquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the [Chinese SQuAD](https://github.com/junzeng-pluto/ChineseSquad).
Please see the original repository ([https://github.com/junzeng-pluto/ChineseSquad](https://github.com/junzeng-pluto/ChineseSquad)) for more details.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Japanese (ja)
## Dataset Structure
An example of 'train' looks as follows.
```
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
| train | validation | test |
|--------:|-----------:|-----:|
| 19097 | 2475 | 6664 |
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
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OxAISH-AL-LLM/wiki_toxic | OxAISH-AL-LLM | 2022-09-19T15:53:19Z | 371 | 13 | null | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:en",
"license:cc0-1.0",
"wikipedia",
"toxicity",
"toxic comments",
"region:us"
] | 2022-09-19T15:53:19Z | 2022-08-25T12:59:12.000Z | 2022-08-25T12:59:12 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Toxic Wikipedia Comments
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
tags:
- wikipedia
- toxicity
- toxic comments
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Wiki Toxic
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Wiki Toxic dataset is a modified, cleaned version of the dataset used in the [Kaggle Toxic Comment Classification challenge](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/overview) from 2017/18. The dataset contains comments collected from Wikipedia forums and classifies them into two categories, `toxic` and `non-toxic`.
The Kaggle dataset was cleaned using the included `clean.py` file.
### Supported Tasks and Leaderboards
- Text Classification: the dataset can be used for training a model to recognise toxicity in sentences and classify them accordingly.
### Languages
The sole language used in the dataset is English.
## Dataset Structure
### Data Instances
For each data point, there is an id, the comment_text itself, and a label (0 for non-toxic, 1 for toxic).
```
{'id': 'a123a58f610cffbc',
'comment_text': '"This article SUCKS. It may be poorly written, poorly formatted, or full of pointless crap that no one cares about, and probably all of the above. If it can be rewritten into something less horrible, please, for the love of God, do so, before the vacuum caused by its utter lack of quality drags the rest of Wikipedia down into a bottomless pit of mediocrity."',
'label': 1}
```
### Data Fields
- `id`: A unique identifier string for each comment
- `comment_text`: A string containing the text of the comment
- `label`: An integer, either 0 if the comment is non-toxic, or 1 if the comment is toxic
### Data Splits
The Wiki Toxic dataset has three splits: *train*, *validation*, and *test*. The statistics for each split are below:
| Dataset Split | Number of data points in split |
| ----------- | ----------- |
| Train | 127,656 |
| Validation | 31,915 |
| Test | 63,978 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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philschmid/emotion | philschmid | 2023-01-20T14:56:20Z | 371 | 1 | emotion | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"emotion-classification",
"region:us"
] | 2023-01-20T14:56:20Z | 2023-01-20T14:56:20.000Z | 2023-01-20T14:56:20 | ---
pretty_name: Emotion
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: emotion
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
tags:
- emotion-classification
dataset_info:
- config_name: split
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 1741597
num_examples: 16000
- name: validation
num_bytes: 214703
num_examples: 2000
- name: test
num_bytes: 217181
num_examples: 2000
download_size: 740883
dataset_size: 2173481
- config_name: unsplit
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 45445685
num_examples: 416809
download_size: 15388281
dataset_size: 45445685
duplicated_from: emotion
---
# Dataset Card for "emotion"
## 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/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.95 MB
- **Size of the generated dataset:** 4.16 MB
- **Total amount of disk used:** 8.11 MB
### Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the 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
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
An example looks as follows.
```
{
"text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
"label": 0
}
```
### Data Fields
The data fields are:
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
### Data Splits
The dataset has 2 configurations:
- split: with a total of 20_000 examples split into train, validation and split
- unsplit: with a total of 416_809 examples in a single train split
| name | train | validation | test |
|---------|-------:|-----------:|-----:|
| split | 16000 | 2000 | 2000 |
| unsplit | 416809 | n/a | n/a |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset should be used for educational and research purposes only.
### Citation Information
If you use this dataset, please cite:
```
@inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1404",
doi = "10.18653/v1/D18-1404",
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
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cdt | null | 2023-01-25T14:27:46Z | 370 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:bsd-3-clause",
"region:us"
] | 2023-01-25T14:27:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: cdt
dataset_info:
features:
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 1104322
num_examples: 10041
- name: test
num_bytes: 109681
num_examples: 1000
download_size: 375476
dataset_size: 1214003
---
# Dataset Card for [Dataset Name]
## 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:**
http://2019.poleval.pl/index.php/tasks/
- **Repository:**
https://github.com/ptaszynski/cyberbullying-Polish
- **Paper:**
- **Leaderboard:**
https://klejbenchmark.com/leaderboard/
- **Point of Contact:**
### Dataset Summary
The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sentence: an anonymized tweet in polish
- target: 1 if tweet is described as bullying, 0 otherwise. The test set doesn't have labels so -1 is used instead.
### 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
BSD 3-Clause
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. | [
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few_rel | null | 2023-06-01T14:59:47Z | 370 | 2 | fewrel | [
"task_categories:other",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:mit",
"relation-extraction",
"arxiv:1810.10147",
"arxiv:1910.07124",
"region:us"
] | 2023-06-01T14:59:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- n<1K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: fewrel
pretty_name: Few-Shot Relation Classification Dataset
tags:
- relation-extraction
dataset_info:
- config_name: default
features:
- name: relation
dtype: string
- name: tokens
sequence: string
- name: head
struct:
- name: text
dtype: string
- name: type
dtype: string
- name: indices
sequence:
sequence: int64
- name: tail
struct:
- name: text
dtype: string
- name: type
dtype: string
- name: indices
sequence:
sequence: int64
- name: names
sequence: string
splits:
- name: train_wiki
num_bytes: 19923155
num_examples: 44800
- name: val_nyt
num_bytes: 1385642
num_examples: 2500
- name: val_pubmed
num_bytes: 488502
num_examples: 1000
- name: val_semeval
num_bytes: 2646249
num_examples: 8851
- name: val_wiki
num_bytes: 5147348
num_examples: 11200
- name: pubmed_unsupervised
num_bytes: 1117703
num_examples: 2500
download_size: 22674323
dataset_size: 30708599
- config_name: pid2name
features:
- name: relation
dtype: string
- name: names
sequence: string
splits:
- name: pid2name
num_bytes: 81607
num_examples: 744
download_size: 22674323
dataset_size: 81607
config_names:
- default
- pid2name
---
# Dataset Card for few_rel
## 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 Page](https://thunlp.github.io/)
- **Repository:** [GitHub](https://github.com/thunlp/FewRel)
- **Paper:** [FewRel](https://arxiv.org/abs/1810.10147), [FewRel 2.0](https://arxiv.org/abs/1910.07124)
- **Leaderboard:** [GitHub Leaderboard](https://thunlp.github.io/fewrel.html)
- **Point of Contact:** [Needs More Information]
### Dataset Summary
FewRel is a large-scale few-shot relation extraction dataset, which contains more than one hundred relations and tens of thousands of annotated instances cross different domains.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The dataset contaings English text, as used by writers on Wikipedia, and crowdsourced English annotations.
## Dataset Structure
### Data Instances
An instance from `train_wiki` split:
```
{'head': {'indices': [[16]], 'text': 'tjq', 'type': 'Q1331049'}, 'names': ['place served by transport hub', 'territorial entity or entities served by this transport hub (airport, train station, etc.)'], 'relation': 'P931', 'tail': {'indices': [[13, 14]], 'text': 'tanjung pandan', 'type': 'Q3056359'}, 'tokens': ['Merpati', 'flight', '106', 'departed', 'Jakarta', '(', 'CGK', ')', 'on', 'a', 'domestic', 'flight', 'to', 'Tanjung', 'Pandan', '(', 'TJQ', ')', '.']}
```
### Data Fields
For `default`:
- `relation`: a `string` feature containing PID of the relation.
- `tokens`: a `list` of `string` features containing tokens for the text.
- `head`: a dictionary containing:
- `text`: a `string` feature representing the head entity.
- `type`: a `string` feature representing the type of the head entity.
- `indices`: a `list` containing `list` of token indices.
- `tail`: a dictionary containing:
- `text`: a `string` feature representing the tail entity.
- `type`: a `string` feature representing the type of the tail entity.
- `indices`: a `list` containing `list` of token indices.
- `names`: a `list` of `string` features containing relation names. For `pubmed_unsupervised` split, this is set to a `list` with an empty `string`. For `val_semeval` and `val_pubmed` split, this is set to a `list` with the `string` from the `relation` field.
### Data Splits
`train_wiki`: 44800
`val_nyt`: 2500
`val_pubmed`: 1000
`val_semeval`: 8851
`val_wiki`: 11200
`pubmed_unsupervised`: 2500
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
For FewRel:
Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong
For FewRel 2.0:
Gao, Tianyu and Han, Xu and Zhu, Hao and Liu, Zhiyuan and Li, Peng and Sun, Maosong and Zhou, Jie
### Licensing Information
```
MIT License
Copyright (c) 2018 THUNLP
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```
@inproceedings{han-etal-2018-fewrel,
title = "{F}ew{R}el: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation",
author = "Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1514",
doi = "10.18653/v1/D18-1514",
pages = "4803--4809"
}
```
```
@inproceedings{gao-etal-2019-fewrel,
title = "{F}ew{R}el 2.0: Towards More Challenging Few-Shot Relation Classification",
author = "Gao, Tianyu and Han, Xu and Zhu, Hao and Liu, Zhiyuan and Li, Peng and Sun, Maosong and Zhou, Jie",
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)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1649",
doi = "10.18653/v1/D19-1649",
pages = "6251--6256"
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bazyl/GTSRB | bazyl | 2022-10-25T10:39:19Z | 370 | 0 | null | [
"task_categories:image-classification",
"task_ids:multi-label-image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10K<n<100K",
"source_datasets:original",
"license:gpl-3.0",
"region:us"
] | 2022-10-25T10:39:19Z | 2022-06-25T00:30:19.000Z | 2022-06-25T00:30:19 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language: []
license:
- gpl-3.0
multilinguality: []
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
pretty_name: GTSRB
---
# Dataset Card for GTSRB
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** http://www.sciencedirect.com/science/article/pii/S0893608012000457
- **Repository:** https://github.com/bazylhorsey/gtsrb/
- **Paper:** Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
- **Leaderboard:** https://benchmark.ini.rub.de/gtsrb_results.html
- **Point of Contact:** bhorsey16@gmail.com
### Dataset Summary
The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties:
- Single-image, multi-class classification problem
- More than 40 classes
- More than 50,000 images in total
- Large, lifelike database
### Supported Tasks and Leaderboards
[Kaggle](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign) \
[Original](https://benchmark.ini.rub.de/gtsrb_results.html)
## Dataset Structure
### Data Instances
```
{
"Width": 31,
"Height": 31,
"Roi.X1": 6,
"Roi.Y1": 6,
"Roi.X2": 26,
"Roi.Y2": 26,
"ClassId": 20,
"Path": "Train/20/00020_00004_00002.png",
}
```
### Data Fields
- Width: width of image
- Height: Height of image
- Roi.X1: Upper left X coordinate
- Roi.Y1: Upper left Y coordinate
- Roi.X2: Lower right t X coordinate
- Roi.Y2: Lower right Y coordinate
- ClassId: Class of image
- Path: Path of image
### Data Splits
Categories: 42
Train: 39209
Test: 12630
## Dataset Creation
### Curation Rationale
Recognition of traffic signs is a challenging real-world problem of high industrial relevance. Although commercial systems have reached the market and several studies on this topic have been published, systematic unbiased comparisons of different approaches are missing and comprehensive benchmark datasets are not freely available.
Traffic sign recognition is a multi-class classification problem with unbalanced class frequencies. Traffic signs can provide a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. However, there exist subsets of classes (e. g., speed limit signs) that are very similar to each other.
The classifier has to cope with large variations in visual appearances due to illumination changes, partial occlusions, rotations, weather conditions, etc.
Humans are capable of recognizing the large variety of existing road signs with close to 100% correctness. This does not only apply to real-world driving, which provides both context and multiple views of a single traffic sign, but also to the recognition from single images.
<!-- ### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] -->
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HuggingFaceH4/test-dataset-all-splits | HuggingFaceH4 | 2023-04-25T22:09:49Z | 370 | 0 | null | [
"region:us"
] | 2023-04-25T22:09:49Z | 2023-04-25T22:09:40.000Z | 2023-04-25T22:09:40 | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: messages
list:
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dtype: string
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splits:
- name: train_ift
num_bytes: 230850
num_examples: 100
- name: train_rl
num_bytes: 369068
num_examples: 100
- name: train_rm
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num_examples: 100
- name: test_rm
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num_examples: 100
- name: test_rl
num_bytes: 312141
num_examples: 100
- name: test_ift
num_bytes: 218856
num_examples: 100
download_size: 1071322
dataset_size: 1812124
---
# Dataset Card for "test-dataset-all-splits"
[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 | |
THUDM/ImageRewardDB | THUDM | 2023-06-21T06:36:29Z | 370 | 21 | null | [
"task_categories:text-to-image",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"arxiv:2304.05977",
"region:us"
] | 2023-06-21T06:36:29Z | 2023-05-21T15:39:22.000Z | 2023-05-21T15:39:22 | ---
license: apache-2.0
task_categories:
- text-to-image
language:
- en
pretty_name: ImageReward Dataset
size_categories:
- 100K<n<1M
---
# ImageRewardDB
## Dataset Description
- **Homepage: https://huggingface.co/datasets/wuyuchen/ImageRewardDB**
- **Repository: https://github.com/THUDM/ImageReward**
- **Paper: https://arxiv.org/abs/2304.05977**
### Dataset Summary
ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference.
It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB.
To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria for quantitative assessment and
annotator training, optimizing labeling experience, and ensuring quality validation. And ImageRewardDB is now publicly available at
[🤗 Hugging Face Dataset](https://huggingface.co/datasets/wuyuchen/ImageRewardDB).
Notice: All images in ImageRewardDB are collected from DiffusionDB, and in addition, we gathered together images corresponding to the same prompt.
### Languages
The text in the dataset is all in English.
### Four Subsets
Considering that the ImageRewardDB contains a large number of images, we provide four subsets in different scales to support different needs.
For all subsets, the validation and test splits remain the same. The validation split(1.10GB) contains 412 prompts and 2.6K images(7.32K pairs) and
the test(1.16GB) split contains 466 prompts and 2.7K images(7.23K pairs). The information on the train split in different scales is as follows:
|Subset|Num of Pairs|Num of Images|Num of Prompts|Size|
|:--|--:|--:|--:|--:|
|ImageRewardDB 1K|17.6K|6.2K|1K|2.7GB|
|ImageRewardDB 2K|35.5K|12.5K|2K|5.5GB|
|ImageRewardDB 4K|71.0K|25.1K|4K|10.8GB|
|ImageRewardDB 8K|141.1K|49.9K|8K|20.9GB|
## Dataset Structure
All the data in this repository is stored in a well-organized way. The 62.6K images in ImageRewardDB are split into several folders,
stored in corresponding directories under "./images" according to its split. Each folder contains around 500 prompts, their corresponding
images, and a JSON file. The JSON file links the image with its corresponding prompt and annotation.
The file structure is as follows:
```
# ImageRewardDB
./
├── images
│ ├── train
│ │ ├── train_1
│ │ │ ├── 0a1ed3a5-04f6-4a1b-aee6-d584e7c8ed9c.webp
│ │ │ ├── 0a58cfa8-ff61-4d31-9757-27322aec3aaf.webp
│ │ │ ├── [...]
│ │ │ └── train_1.json
│ │ ├── train_2
│ │ ├── train_3
│ │ ├── [...]
│ │ └── train_32
│ ├── validation
│ │ └── [...]
│ └── test
│ └── [...]
├── metadata-train.parquet
├── metadata-validation.parquet
└── metadata-test.parquet
```
The sub-folders have the name of {split_name}_{part_id}, and the JSON file has the same name as the sub-folder.
Each image is a lossless WebP file and has a unique name generated by [UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier).
### Data Instances
For instance, below is the image of `1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp` and its information in train_1.json.
```json
{
"image_path": "images/train/train_1/0280642d-f69f-41d1-8598-5a44e296aa8b.webp",
"prompt_id": "000864-0061",
"prompt": "painting of a holy woman, decorated, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8 k ",
"classification": "People",
"image_amount_in_total": 9,
"rank": 5,
"overall_rating": 4,
"image_text_alignment_rating": 3,
"fidelity_rating": 4
}
```
### Data Fields
* image: The image object
* prompt_id: The id of the corresponding prompt
* prompt: The text of the corresponding prompt
* classification: The classification of the corresponding prompt
* image_amount_in_total: Total amount of images related to the prompt
* rank: The relative rank of the image in all related images
* overall_rating: The overall score of this image
* image_text_alignment_rating: The score of how well the generated image matches the given text
* fidelity_rating: The score of whether the output image is true to the shape and characteristics that the object should have
### Data Splits
As we mentioned above, all scales of the subsets we provided have three splits of "train", "validation", and "test".
And all the subsets share the same validation and test splits.
### Dataset Metadata
We also include three metadata tables `metadata-train.parquet`, `metadata-validation.parquet`, and `metadata-test.parquet` to
help you access and comprehend ImageRewardDB without downloading the Zip files.
All the tables share the same schema, and each row refers to an image. The schema is shown below,
and actually, the JSON files we mentioned above share the same schema:
|Column|Type|Description|
|:---|:---|:---|
|`image_path`|`string`|The relative path of the image in the repository.|
|`prompt_id`|`string`|The id of the corresponding prompt.|
|`prompt`|`string`|The text of the corresponding prompt.|
|`classification`|`string`| The classification of the corresponding prompt.|
|`image_amount_in_total`|`int`| Total amount of images related to the prompt.|
|`rank`|`int`| The relative rank of the image in all related images.|
|`overall_rating`|`int`| The overall score of this image.
|`image_text_alignment_rating`|`int`|The score of how well the generated image matches the given text.|
|`fidelity_rating`|`int`|The score of whether the output image is true to the shape and characteristics that the object should have.|
Below is an example row from metadata-train.parquet.
|image_path|prompt_id|prompt|classification|image_amount_in_total|rank|overall_rating|image_text_alignment_rating|fidelity_rating|
|:---|:---|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---|:---|:---|:---|:---|:---|
|images/train/train_1/1b4b2d61-89c2-4091-a1c0-f547ad5065cb.webp|001324-0093|a magical forest that separates the good world from the dark world, ...|Outdoor Scenes|8|3|6|6|6|
## Loading ImageRewardDB
You can use the Hugging Face [Datasets](https://huggingface.co/docs/datasets/quickstart) library to easily load the ImageRewardDB.
As we mentioned before, we provide four subsets in the scales of 1k, 2k, 4k, and 8k. You can load them using as following:
```python
from datasets import load_dataset
# Load the 1K-scale dataset
dataset = load_dataset("THUDM/ImageRewardDB", "1k")
# Load the 2K-scale dataset
dataset = load_dataset("THUDM/ImageRewardDB", "2k")
# Load the 4K-scale dataset
dataset = load_dataset("THUDM/ImageRewardDB", "4K")
# Load the 8K-scale dataset
dataset = load_dataset("THUDM/ImageRewardDB", "8k")
```
## Additional Information
### Licensing Information
The ImageRewardDB dataset is available under the [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE).
### Citation Information
```
@misc{xu2023imagereward,
title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation},
author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong},
year={2023},
eprint={2304.05977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mia-project/child_handpicked_sentences | mia-project | 2023-11-14T05:17:36Z | 370 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-14T05:17:36Z | 2023-11-14T00:20:46.000Z | 2023-11-14T00:20:46 | ---
license: unknown
dataset_info:
features:
- name: audio_quality
dtype: string
- name: age
dtype: int64
- name: sentence_transcription
dtype: string
- name: audio
dtype: audio
- name: sentence_number
dtype: string
- name: language_collection
dtype: string
- name: kid_name
dtype: string
- name: url_origin
dtype: string
splits:
- name: train
num_bytes: 2583290.0
num_examples: 189
download_size: 2447605
dataset_size: 2583290.0
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 | |
turkish_product_reviews | null | 2023-01-25T14:54:42Z | 369 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:tr",
"license:unknown",
"region:us"
] | 2023-01-25T14:54:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- tr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Turkish Product Reviews
dataset_info:
features:
- name: sentence
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 43369710
num_examples: 235165
download_size: 13184332
dataset_size: 43369710
---
# Dataset Card for Turkish Product Reviews
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [turkish-text-data](https://github.com/fthbrmnby/turkish-text-data)
- **Point of Contact:** [Fatih Barmanbay](https://github.com/fthbrmnby)
### Dataset Summary
This Turkish Product Reviews Dataset contains 235.165 product reviews collected online. There are 220.284 positive, 14881 negative reviews.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Turkish.
## Dataset Structure
### Data Instances
**Example 1:**
**sentence:** beklentimin altında bir ürün kaliteli değil
**sentiment:** 0 (negative)
**Example 2:**
**sentence:** fiyat ve performans olarak gayet iyi
**sentiment:** 1 (positive)
### Data Fields
- **sentence**(string) : Contatins turkish product review
- **sentiment**(int) : 0 (negative) or 1 (positive)
### Data Splits
It is not divided into Train set and Test set.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
The dataset does not contain any additional 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
The dataset was created by [Fatih Barmanbay](https://github.com/fthbrmnby).
### Licensing Information
The data is under the [CC-BY-SA-4.0 License](https://github.com/fthbrmnby/turkish-text-data/blob/master/LICENCE)
### Citation Information
No citation available for this dataset.
### Contributions
Thanks to [@basakbuluz](https://github.com/basakbuluz) for adding this dataset. | [
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rubrix/gutenberg_spacy-ner | rubrix | 2022-02-24T21:48:13Z | 369 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
clickbait_news_bg | null | 2023-01-25T14:28:03Z | 368 | 0 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bg",
"license:unknown",
"region:us"
] | 2023-01-25T14:28:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- bg
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: Clickbait/Fake News in Bulgarian
dataset_info:
features:
- name: fake_news_score
dtype:
class_label:
names:
'0': legitimate
'1': fake
- name: click_bait_score
dtype:
class_label:
names:
'0': normal
'1': clickbait
- name: content_title
dtype: string
- name: content_url
dtype: string
- name: content_published_time
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 24480402
num_examples: 2815
- name: validation
num_bytes: 6752242
num_examples: 761
download_size: 8569575
dataset_size: 31232644
---
# Dataset Card for Clickbait/Fake News in Bulgarian
## 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:** [Data Science Society / Case Fake News](https://gitlab.com/datasciencesociety/case_fake_news)
- **Repository:** [Data Science Society / Case Fake News / Data](https://gitlab.com/datasciencesociety/case_fake_news/-/tree/master/data)
- **Paper:** [This paper uses the dataset.](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a corpus of Bulgarian news over a fixed period of time, whose factuality had been questioned.
The news come from 377 different sources from various domains, including politics, interesting facts and tips&tricks.
The dataset was prepared for the Hack the
Fake News hackathon. It was provided by the
[Bulgarian Association of PR Agencies](http://www.bapra.bg/) and is
available in [Gitlab](https://gitlab.com/datasciencesociety/).
The corpus was automatically collected, and then annotated by students of journalism.
The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news
and 1,968 (i.e., 70%) are click-baits; There are 761 testing examples.
There is 98% correlation between fake news and clickbaits.
One important aspect about the training dataset is that it contains many repetitions.
This should not be surprising as it attempts to represent a natural distribution of factual
vs. fake news on-line over a period of time. As publishers of fake news often have a group of
websites that feature the same deceiving content, we should expect some repetition.
In particular, the training dataset contains
434 unique articles with duplicates. These articles have three reposts each on average, with
the most reposted article appearing 45 times.
If we take into account the labels of the reposted articles, we can see that if an article
is reposted, it is more likely to be fake news.
The number of fake news that have a duplicate in the training dataset are 1018 whereas,
the number of articles with genuine content
that have a duplicate article in the training set is 322.
(The dataset description is from the following [paper](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf).)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Bulgarian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Each entry in the dataset consists of the following elements:
* `fake_news_score` - a label indicating whether the article is fake or not
* `click_bait_score` - another label indicating whether it is a click-bait
* `content_title` - article heading
* `content_url` - URL of the original article
* `content_published_time` - date of publication
* `content` - article content
### Data Splits
The **training dataset** contains 2,815 examples, where 1,940 (i.e., 69%) are fake news
and 1,968 (i.e., 70%) are click-baits;
The **validation dataset** contains 761 testing examples.
## 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 [@tsvm](https://github.com/tsvm), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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interpress_news_category_tr_lite | null | 2023-01-25T14:33:07Z | 368 | 10 | null | [
"task_categories:text-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|interpress_news_category_tr",
"language:tr",
"license:unknown",
"news-category-classification",
"region:us"
] | 2023-01-25T14:33:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- tr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|interpress_news_category_tr
task_categories:
- text-classification
task_ids: []
pretty_name: Interpress Turkish News Category Dataset (270K - Lite Version)
tags:
- news-category-classification
dataset_info:
features:
- name: content
dtype: string
- name: category
dtype:
class_label:
names:
'0': kültürsanat
'1': ekonomi
'2': siyaset
'3': eğitim
'4': dünya
'5': spor
'6': teknoloji
'7': magazin
'8': sağlık
'9': gündem
config_name: 270k_10class
splits:
- name: train
num_bytes: 721110711
num_examples: 218880
- name: test
num_bytes: 179348267
num_examples: 54721
download_size: 342920336
dataset_size: 900458978
---
# Dataset Card for Interpress Turkish News Category Dataset (270K - Lite Version)
## 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:** [Interpress](https://www.interpress.com/)
- **Point of Contact:** [Yavuz Komecoglu](mailto:yavuz.komecoglu@kodiks.com)
### Dataset Summary
Turkish News Category Dataset (270K - Lite Version) is a Turkish news data set consisting of 273601 news in 10 categories ("kültürsanat", "ekonomi", "siyaset", "eğitim", "dünya", "spor", "teknoloji", "magazin", "sağlık", "gündem"), compiled from printed media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. **It has been rearranged as easily separable and with fewer classes.**
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Turkish.
## Dataset Structure
### Data Instances
A text classification dataset with 10 different news category.
Here is an example from the dataset:
```
{
'category': 0,
'content': 'Tarihten Sınıfta Kaldık Bugün tarihe damgasını vuran Osmanlı İmparatorluğu nun kuruluş yıldönümü. Adına dizilerin çekildiği tarihimizi ne kadar biliyoruz? Gerekçeler faklı; ama sonuç aynı çıktı. Tarihten sınıfta kaldık. Sayfa 5r 1 Bugün tarihe damgasını vuran Osmanlı İmparatorluğumun kuruluş yıldönümü. Adına dizilerin çekildiği tarihimizi ne kadar biliyoruz? Gerekçeler faklı; ama sonuç aynı çıktı. Tarihten sınıfta kaldık 7 Ocak 1299... Kıtalara dağılan ücüyle, ülkeler arasında gördüğü aygıyla tarihe damgasını vuran anlı devletin kuruluş tarihi. Peki, anlı tarihimizi ne kadar biliyoruz? on zamanlarda tarihimizi anlatan izilere ilgi nasıl? Bu dizilerde anlatanlar ne kadar sağlıklı? İşte sokaın değerlendirmesi; levlüdiye Karaman (42-Ev lamım): Bir bilgim yok. Tarihle izla ilgilenmiyorum. Eşim daha ilgilidir bu konuda. Evde anlatır, ndan duyduklarımla yetiniyorum esem yalan olmaz. Osmanlı döeminde yaşamak isterdim. Tarih izileri izlerim Muhteşem Yüzyıl izisini çok izledim; hatta hiç kaırmazdım. Ama tarihimiz bu değil. Sunuün bilincindeyim. Muhteşem üzyıl dizisi genelde haremiyle ön landaydı. Onun için tarihi diziden ğrenmeyi de doğru bulmuyorum. )kullarda verilen tarih dersleri yeisiz. Daha çok tanıtabilirler. Görel anlatım yapılsın çocuklarımız aten okumak istemiyor. En azman eğlenceli hale getirip bu şekilde ilgilendirebilirler. erdi Üstün (22-Saatçi): Bu gün Osmanlı Devleti nin kuruluş yıldönümü olduğunu bilmiyordum. O dönemde yaşamak isterdim. Tarih yazılmış neden yaşamak istemeyim ki. Tarihime yeterince hakim olduğumu düşünüyorum. Araştırmalar yapıyorum. Merak ediyorum. Okullarda verilen tarih dersleri yeterli. Tarih dizisi izlemem, televizyondan tarihimi öğrenmek bana mantıklı gelmiyor. Yeterli olabilir; ama hikayeleştiriliyor. Sonuçta olduğu gibi anlatılsa daha iyi olur. Songül Karabacak (40-Ev Hanımı): Kuruluş yıldönümü olduğunu bilmiyordum. Tarih bilgim çok azdır. Zaten biz yaşadığımız dönemde tarih yazıyoruz. Osmanlı Dönemi nde yaşamak istemezdim. Sebebini bilmiyorum; ama hayatımdan memnunum, dönemden de memnunum. Dizileri takip etmiyorum. Ama mutlaka dizilerde tarihimiz doğru yansıtılıyor ki insanlar sürekli takip ediyor. Benim televizyonla pek aram yoktur. Ertuğrul Şahin (47-Çalışmıyor): Kuruluş yıldönümü olduğunu bilmiyordum. Sizden öğrendim. O dönemde yaşamak isterdim. Tarih sonuçta merak ederim. Tarihle ilgili çok bilgim yok. Okumadım, zaten şartlar el vermedi. Okullarda verilen eğitim yeterli değil. Örnek vermek gerekirse; 20 yaşında oğlum var Atatürk ün doğum yılını soruyorum yüzüme bakıyor. Verilen eğitim belli. Konu belirliyorlar onun dışına çıkmıyorlar. Daha fazla bilgi verilebilir. Tabi gençlerimizde de suç var bize baksınlar tarihimizi bilmiyoruz. Onlar araştırma yapsınlar her gün internette geziyorlar faydasız bir şeye bakacaklarına ecdatlarını okusunlar. Tarih dizlerini izlerim. Ama doğru yansıtılıyor mu orasını bilmiyorum sadece izleyiciyim. Ama önceden Süleyman Şah ı duyardım. Büyüklerimiz anlatırdı bunu diziden teyit ettim mesela. Ahmet Efe (22-Muhasebeci): Kuruluş yıldönümü olduğuyla ilgili bir bilgim yok. O dönemde yaşamak isterdim. Aldığımız bilgiler sonucunda illa ki bir özenme oluyor. Tam anlamıyla tarih bilgisine sahip olduğumu düşünmüyorum. Tarihe merakım var aslında; ama çok kısıtlı araştırma yapıyorum. Okullarda verilen tarih dersi yeterli değil. Çünkü şuradan birkaç çocuğu çevirip sorsanız size yeterli bilgi vermez. Veremez onun da bilgisi yok sonuçta. Zaten kısıtlı bilgiler veriliyor. Tarih dizilerini kılıç kalkan kuşanıp izliyorum. Doğru yansıtılıyor bundan dolayı da biraz insanlar tarihini öğrenmeye başladı desek yalan olmaz. Bu ne kadar doğru derseniz de bilgiyi doğru verdikten sonra tabi diziden de tarih öğrenilebilir. Mehmet Ak (28-Satış Danışmanı): Kuruluşunun bugün olduğunu bilmiyordum. O dönemde yaşamak isterdim. Yeterli bilgim yok bence kim tarihi tam anlamıyla öğrenebilir ki zaten. Ama tabi tarih kitapları okuyorum, araştırıyorum. Okullarda verilen tarih derslerini yeterli bulmuyorum; ama daha fazla neler yapılabilir, tarih küçüklere nasıl anlatılır bilmiyorum tek bildiğim yeterli olmadığı. Tarih dizileri gerçeği yüzde 75 yansıtıyor. Bu konuda araştırma yaptım yüzeysel anlatılıyor; fakat yine de bilgi edinilebilecek diziler. En azından rutinleşmiş dizi konularından uzak. Aile ile rahat rahat izleyebilirsin. Hasan Çalık (65-Emekli): Kuruluş yıldönümü olduğunu biliyorum. Araştırma yaparım. O dönemde yaşamak istemezdim Cumhuriyet döneminde yaşamayı daha çok isterdim. Okullarda verilen dersler yeterli. Film ya da dizi okumak yerine kitap okumayı tercih ederim. Bir insan ancak kitap okuyarak aydınlanabilir. Bu şekilde kendini geliştirebilir. Bir ömre ne kadar kitap sığdırırsan o kadar aydın bir insan olursun. Konusu fark etmez ister tarih olsun, ister roman okumak her zaman kazanç sağlar. Bir diziden tarihi ne kadar yeterli öğrenebilirsin ki ya da ne kadar doğru anlatılabilir. Bence diziyi bırakıp kitaplara yönelsinler. Nuray Çelik'
}
```
### Data Fields
- **category** : Indicates to which category the news text belongs.
(Such as "kültürsanat" (0), "ekonomi" (1), "siyaset" (2), "eğitim" (3), "dünya" (4), "spor" (5), "teknoloji" (6), "magazin" (7), "sağlık" (8), "gündem" (9))
- **content** : Contains the text of the news.
### Data Splits
The data is split into a training and testing. The split is organized as the following
| | train | test |
|------------|--------:|-------:|
| data split | 218,880 | 54,721 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
Downloaded over 270,000 news from the printed media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. This data collection compiled from print media and internet news is presented in its raw form. For this reason, it is appropriate to use it with careful pre-processing steps regarding various OCR errors and typos.
#### Who are the source language producers?
Turkish printed news sources and online news sites.
### Annotations
The dataset does not contain any additional 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
https://www.interpress.com/
### Contributions
Thanks to [@basakbuluz](https://github.com/basakbuluz) & [@yavuzkomecoglu](https://github.com/yavuzkomecoglu) & [@serdarakyol](https://github.com/serdarakyol/) for adding this dataset. | [
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Zaid/quac_expanded | Zaid | 2021-10-04T19:41:30Z | 368 | 0 | null | [
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iohadrubin/wikitext-103-raw-v1 | iohadrubin | 2022-08-14T13:41:10Z | 368 | 2 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/ebm_pico | bigbio | 2022-12-22T15:44:33Z | 368 | 0 | null | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | 2022-12-22T15:44:33Z | 2022-11-13T22:08:15.000Z | 2022-11-13T22:08:15 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: EBM NLP
homepage: https://github.com/bepnye/EBM-NLP
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for EBM NLP
## Dataset Description
- **Homepage:** https://github.com/bepnye/EBM-NLP
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
This corpus release contains 4,993 abstracts annotated with (P)articipants,
(I)nterventions, and (O)utcomes. Training labels are sourced from AMT workers and
aggregated to reduce noise. Test labels are collected from medical professionals.
## Citation Information
```
@inproceedings{nye-etal-2018-corpus,
title = "A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature",
author = "Nye, Benjamin and
Li, Junyi Jessy and
Patel, Roma and
Yang, Yinfei and
Marshall, Iain and
Nenkova, Ani and
Wallace, Byron",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1019",
doi = "10.18653/v1/P18-1019",
pages = "197--207",
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
distil-whisper/librispeech_asr-prompted | distil-whisper | 2023-09-19T09:31:43Z | 368 | 0 | null | [
"region:us"
] | 2023-09-19T09:31:43Z | 2023-09-19T08:45:04.000Z | 2023-09-19T08:45:04 | ---
dataset_info:
config_name: all
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
- name: whisper_transcript_unprompted
dtype: string
- name: whisper_transcript
dtype: string
splits:
- name: train.clean.100
num_bytes: 6641615051.062
num_examples: 28539
- name: train.clean.360
num_bytes: 23977966959.828
num_examples: 104014
- name: train.other.500
num_bytes: 31918849882.584
num_examples: 148688
- name: validation.clean
num_bytes: 361026354.966
num_examples: 2703
- name: validation.other
num_bytes: 338707588.648
num_examples: 2864
- name: test.clean
num_bytes: 369123744.42
num_examples: 2620
- name: test.other
num_bytes: 353861942.154
num_examples: 2939
download_size: 61926395211
dataset_size: 63961151523.662
configs:
- config_name: all
data_files:
- split: train.clean.100
path: all/train.clean.100-*
- split: train.clean.360
path: all/train.clean.360-*
- split: train.other.500
path: all/train.other.500-*
- split: validation.clean
path: all/validation.clean-*
- split: validation.other
path: all/validation.other-*
- split: test.clean
path: all/test.clean-*
- split: test.other
path: all/test.other-*
---
# Dataset Card for "librispeech_asr-prompted"
[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 | |
newsroom | null | 2023-04-05T13:35:54Z | 367 | 7 | newsroom | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2023-04-05T13:35:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: CORNELL NEWSROOM
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: newsroom
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: date
dtype: string
- name: density_bin
dtype: string
- name: coverage_bin
dtype: string
- name: compression_bin
dtype: string
- name: density
dtype: float32
- name: coverage
dtype: float32
- name: compression
dtype: float32
splits:
- name: test
num_bytes: 472446866
num_examples: 108862
- name: train
num_bytes: 4357506078
num_examples: 995041
- name: validation
num_bytes: 473206951
num_examples: 108837
download_size: 0
dataset_size: 5303159895
---
# Dataset Card for "newsroom"
## 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://lil.nlp.cornell.edu/newsroom/index.html](https://lil.nlp.cornell.edu/newsroom/index.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 5.30 GB
- **Total amount of disk used:** 5.30 GB
### Dataset Summary
NEWSROOM is a large dataset for training and evaluating summarization systems.
It contains 1.3 million articles and summaries written by authors and
editors in the newsrooms of 38 major publications.
Dataset features includes:
- text: Input news text.
- summary: Summary for the news.
And additional features:
- title: news title.
- url: url of the news.
- date: date of the article.
- density: extractive density.
- coverage: extractive coverage.
- compression: compression ratio.
- density_bin: low, medium, high.
- coverage_bin: extractive, abstractive.
- compression_bin: low, medium, high.
This dataset can be downloaded upon requests. Unzip all the contents
"train.jsonl, dev.josnl, test.jsonl" to the `tfds` folder.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
English (`en`).
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 5.30 GB
- **Total amount of disk used:** 5.30 GB
An example of 'train' looks as follows.
```
{
"compression": 33.880001068115234,
"compression_bin": "medium",
"coverage": 1.0,
"coverage_bin": "high",
"date": "200600000",
"density": 11.720000267028809,
"density_bin": "extractive",
"summary": "some summary 1",
"text": "some text 1",
"title": "news title 1",
"url": "url.html"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `text`: a `string` feature.
- `summary`: a `string` feature.
- `title`: a `string` feature.
- `url`: a `string` feature.
- `date`: a `string` feature.
- `density_bin`: a `string` feature.
- `coverage_bin`: a `string` feature.
- `compression_bin`: a `string` feature.
- `density`: a `float32` feature.
- `coverage`: a `float32` feature.
- `compression`: a `float32` feature.
### Data Splits
| name |train |validation| test |
|-------|-----:|---------:|-----:|
|default|995041| 108837|108862|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
https://cornell.qualtrics.com/jfe/form/SV_6YA3HQ2p75XH4IR
This Dataset Usage Agreement ("Agreement") is a legal agreement with the Cornell Newsroom Summaries Team ("Newsroom") for the Dataset made available to the individual or entity ("Researcher") exercising rights under this Agreement. "Dataset" includes all text, data, information, source code, and any related materials, documentation, files, media, updates or revisions.
The Dataset is intended for non-commercial research and educational purposes only, and is made available free of charge without extending any license or other intellectual property rights. By downloading or using the Dataset, the Researcher acknowledges that they agree to the terms in this Agreement, and represent and warrant that they have authority to do so on behalf of any entity exercising rights under this Agreement. The Researcher accepts and agrees to be bound by the terms and conditions of this Agreement. If the Researcher does not agree to this Agreement, they may not download or use the Dataset.
By sharing content with Newsroom, such as by submitting content to this site or by corresponding with Newsroom contributors, the Researcher grants Newsroom the right to use, reproduce, display, perform, adapt, modify, distribute, have distributed, and promote the content in any form, anywhere and for any purpose, such as for evaluating and comparing summarization systems. Nothing in this Agreement shall obligate Newsroom to provide any support for the Dataset. Any feedback, suggestions, ideas, comments, improvements given by the Researcher related to the Dataset is voluntarily given, and may be used by Newsroom without obligation or restriction of any kind.
The Researcher accepts full responsibility for their use of the Dataset and shall defend indemnify, and hold harmless Newsroom, including their employees, trustees, officers, and agents, against any and all claims arising from the Researcher's use of the Dataset. The Researcher agrees to comply with all laws and regulations as they relate to access to and use of the Dataset and Service including U.S. export jurisdiction and other U.S. and international regulations.
THE DATASET IS PROVIDED "AS IS." NEWSROOM DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WITHOUT LIMITATION OF THE ABOVE, NEWSROOM DISCLAIMS ANY WARRANTY THAT DATASET IS BUG OR ERROR-FREE, AND GRANTS NO WARRANTY REGARDING ITS USE OR THE RESULTS THEREFROM INCLUDING, WITHOUT LIMITATION, ITS CORRECTNESS, ACCURACY, OR RELIABILITY. THE DATASET IS NOT WARRANTIED TO FULFILL ANY PARTICULAR PURPOSES OR NEEDS.
TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT SHALL NEWSROOM BE LIABLE FOR ANY LOSS, DAMAGE OR INJURY, DIRECT AND INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER FOR BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY LIMITED REMEDY.
This Agreement is effective until terminated. Newsroom reserves the right to terminate the Researcher's access to the Dataset at any time. If the Researcher breaches this Agreement, the Researcher's rights to use the Dataset shall terminate automatically. The Researcher will immediately cease all use and distribution of the Dataset and destroy any copies or portions of the Dataset in their possession.
This Agreement is governed by the laws of the State of New York, without regard to conflict of law principles. All terms and provisions of this Agreement shall, if possible, be construed in a manner which makes them valid, but in the event any term or provision of this Agreement is found by a court of competent jurisdiction to be illegal or unenforceable, the validity or enforceability of the remainder of this Agreement shall not be affected.
This Agreement is the complete and exclusive agreement between the parties with respect to its subject matter and supersedes all prior or contemporaneous oral or written agreements or understandings relating to the subject matter.
### Citation Information
```
@inproceedings{N18-1065,
author = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
title = {NEWSROOM: A Dataset of 1.3 Million Summaries
with Diverse Extractive Strategies},
booktitle = {Proceedings of the 2018 Conference of the
North American Chapter of the Association for
Computational Linguistics: Human Language Technologies},
year = {2018},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@yoavartzi](https://github.com/yoavartzi), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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0.20978547632694244,
0.5947548151016235,
0.30455490946769714,
0.16014432907104492,
0.8859511017799377,
-0.421328604221344,
0.1814548522233963,
0.0034617665223777294,
0.2416592538356781,
-0.3407817780971527,
-0.09921789169311523,
-0.26094850897789,
-0.08528678119182587,
-0.39635008573532104,
-0.380114883184433
] | null | null | null | null | null | null | null | null | null | null | null | null | null |
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