id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
medalpaca/medical_meadow_medical_flashcards | 2023-04-06T17:12:17.000Z | [
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] | medalpaca | null | null | 5 | 2,162 | 2023-04-06T17:09:17 | ---
license: cc
task_categories:
- question-answering
language:
- en
---
# Dataset Card for Medical Flashcards
## Dataset Description
- **Repository:** https://github.com/kbressem/medalpaca
- **Paper:** TBA
### Dataset Summary
Medicine as a whole encompasses a wide range of subjects that medical students and graduates must master
in order to practice effectively. This includes a deep understanding of basic medical sciences, clinical knowledge,
and clinical skills. The Anki Medical Curriculum flashcards are created and updated by medical students and cover the
entirety of this curriculum, addressing subjects such as anatomy, physiology, pathology, pharmacology, and more.
These flashcards frequently feature succinct summaries and mnemonics to aid in learning and retention of vital medical concepts.
In our study, we employed the flashcards as a resource for generating question-answer pairs for training purposes.
After removing cards that contained images, we utilized OpenAI's GPT-3.5-turbo to rephrase the cards into coherent,
contextually relevant question-answer pairs. In general the questions and answers are short and focused, as the flashcards
do not allow to add much information.
### Citation Information
TBA | 1,242 | [
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gia-project/gia-dataset-tokenized-1024 | 2023-09-29T15:51:41.000Z | [
"region:us"
] | gia-project | null | null | 0 | 2,147 | 2023-09-16T08:02:26 | ---
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- split: train
path: babyai-mini-boss-level/train-*
- split: test
path: babyai-mini-boss-level/test-*
- config_name: babyai-move-two-across-s8n9
data_files:
- split: train
path: babyai-move-two-across-s8n9/train-*
- split: test
path: babyai-move-two-across-s8n9/test-*
- config_name: babyai-one-room-s8
data_files:
- split: train
path: babyai-one-room-s8/train-*
- split: test
path: babyai-one-room-s8/test-*
- config_name: babyai-open
data_files:
- split: train
path: babyai-open/train-*
- split: test
path: babyai-open/test-*
- config_name: babyai-open-door
data_files:
- split: train
path: babyai-open-door/train-*
- split: test
path: babyai-open-door/test-*
- config_name: babyai-open-doors-order-n4
data_files:
- split: train
path: babyai-open-doors-order-n4/train-*
- split: test
path: babyai-open-doors-order-n4/test-*
- config_name: babyai-open-red-door
data_files:
- split: train
path: babyai-open-red-door/train-*
- split: test
path: babyai-open-red-door/test-*
- config_name: babyai-open-two-doors
data_files:
- split: train
path: babyai-open-two-doors/train-*
- split: test
path: babyai-open-two-doors/test-*
- config_name: babyai-pickup
data_files:
- split: train
path: babyai-pickup/train-*
- split: test
path: babyai-pickup/test-*
- config_name: babyai-pickup-above
data_files:
- split: train
path: babyai-pickup-above/train-*
- split: test
path: babyai-pickup-above/test-*
- config_name: babyai-pickup-loc
data_files:
- split: train
path: babyai-pickup-loc/train-*
- split: test
path: babyai-pickup-loc/test-*
- config_name: babyai-synth
data_files:
- split: train
path: babyai-synth/train-*
- split: test
path: babyai-synth/test-*
- config_name: babyai-synth-loc
data_files:
- split: train
path: babyai-synth-loc/train-*
- split: test
path: babyai-synth-loc/test-*
- config_name: babyai-synth-seq
data_files:
- split: train
path: babyai-synth-seq/train-*
- split: test
path: babyai-synth-seq/test-*
- config_name: babyai-unblock-pickup
data_files:
- split: train
path: babyai-unblock-pickup/train-*
- split: test
path: babyai-unblock-pickup/test-*
- config_name: babyai-unlock-local
data_files:
- split: train
path: babyai-unlock-local/train-*
- split: test
path: babyai-unlock-local/test-*
- config_name: babyai-unlock-pickup
data_files:
- split: train
path: babyai-unlock-pickup/train-*
- split: test
path: babyai-unlock-pickup/test-*
- config_name: metaworld-assembly
data_files:
- split: train
path: metaworld-assembly/train-*
- split: test
path: metaworld-assembly/test-*
- config_name: metaworld-basketball
data_files:
- split: train
path: metaworld-basketball/train-*
- split: test
path: metaworld-basketball/test-*
- config_name: metaworld-bin-picking
data_files:
- split: train
path: metaworld-bin-picking/train-*
- split: test
path: metaworld-bin-picking/test-*
- config_name: metaworld-box-close
data_files:
- split: train
path: metaworld-box-close/train-*
- split: test
path: metaworld-box-close/test-*
- config_name: metaworld-button-press
data_files:
- split: train
path: metaworld-button-press/train-*
- split: test
path: metaworld-button-press/test-*
- config_name: metaworld-button-press-topdown
data_files:
- split: train
path: metaworld-button-press-topdown/train-*
- split: test
path: metaworld-button-press-topdown/test-*
- config_name: metaworld-button-press-topdown-wall
data_files:
- split: train
path: metaworld-button-press-topdown-wall/train-*
- split: test
path: metaworld-button-press-topdown-wall/test-*
- config_name: metaworld-button-press-wall
data_files:
- split: train
path: metaworld-button-press-wall/train-*
- split: test
path: metaworld-button-press-wall/test-*
- config_name: metaworld-coffee-button
data_files:
- split: train
path: metaworld-coffee-button/train-*
- split: test
path: metaworld-coffee-button/test-*
- config_name: metaworld-coffee-pull
data_files:
- split: train
path: metaworld-coffee-pull/train-*
- split: test
path: metaworld-coffee-pull/test-*
- config_name: metaworld-coffee-push
data_files:
- split: train
path: metaworld-coffee-push/train-*
- split: test
path: metaworld-coffee-push/test-*
- config_name: metaworld-dial-turn
data_files:
- split: train
path: metaworld-dial-turn/train-*
- split: test
path: metaworld-dial-turn/test-*
- config_name: metaworld-disassemble
data_files:
- split: train
path: metaworld-disassemble/train-*
- split: test
path: metaworld-disassemble/test-*
- config_name: metaworld-door-close
data_files:
- split: train
path: metaworld-door-close/train-*
- split: test
path: metaworld-door-close/test-*
- config_name: metaworld-door-lock
data_files:
- split: train
path: metaworld-door-lock/train-*
- split: test
path: metaworld-door-lock/test-*
- config_name: metaworld-door-open
data_files:
- split: train
path: metaworld-door-open/train-*
- split: test
path: metaworld-door-open/test-*
- config_name: metaworld-door-unlock
data_files:
- split: train
path: metaworld-door-unlock/train-*
- split: test
path: metaworld-door-unlock/test-*
- config_name: metaworld-drawer-close
data_files:
- split: train
path: metaworld-drawer-close/train-*
- split: test
path: metaworld-drawer-close/test-*
- config_name: metaworld-drawer-open
data_files:
- split: train
path: metaworld-drawer-open/train-*
- split: test
path: metaworld-drawer-open/test-*
- config_name: metaworld-faucet-close
data_files:
- split: train
path: metaworld-faucet-close/train-*
- split: test
path: metaworld-faucet-close/test-*
- config_name: metaworld-faucet-open
data_files:
- split: train
path: metaworld-faucet-open/train-*
- split: test
path: metaworld-faucet-open/test-*
- config_name: metaworld-hammer
data_files:
- split: train
path: metaworld-hammer/train-*
- split: test
path: metaworld-hammer/test-*
- config_name: metaworld-hand-insert
data_files:
- split: train
path: metaworld-hand-insert/train-*
- split: test
path: metaworld-hand-insert/test-*
- config_name: metaworld-handle-press
data_files:
- split: train
path: metaworld-handle-press/train-*
- split: test
path: metaworld-handle-press/test-*
- config_name: metaworld-handle-press-side
data_files:
- split: train
path: metaworld-handle-press-side/train-*
- split: test
path: metaworld-handle-press-side/test-*
- config_name: metaworld-handle-pull
data_files:
- split: train
path: metaworld-handle-pull/train-*
- split: test
path: metaworld-handle-pull/test-*
- config_name: metaworld-handle-pull-side
data_files:
- split: train
path: metaworld-handle-pull-side/train-*
- split: test
path: metaworld-handle-pull-side/test-*
- config_name: metaworld-lever-pull
data_files:
- split: train
path: metaworld-lever-pull/train-*
- split: test
path: metaworld-lever-pull/test-*
- config_name: metaworld-peg-insert-side
data_files:
- split: train
path: metaworld-peg-insert-side/train-*
- split: test
path: metaworld-peg-insert-side/test-*
- config_name: metaworld-peg-unplug-side
data_files:
- split: train
path: metaworld-peg-unplug-side/train-*
- split: test
path: metaworld-peg-unplug-side/test-*
- config_name: metaworld-pick-out-of-hole
data_files:
- split: train
path: metaworld-pick-out-of-hole/train-*
- split: test
path: metaworld-pick-out-of-hole/test-*
- config_name: metaworld-pick-place
data_files:
- split: train
path: metaworld-pick-place/train-*
- split: test
path: metaworld-pick-place/test-*
- config_name: metaworld-pick-place-wall
data_files:
- split: train
path: metaworld-pick-place-wall/train-*
- split: test
path: metaworld-pick-place-wall/test-*
- config_name: metaworld-plate-slide
data_files:
- split: train
path: metaworld-plate-slide/train-*
- split: test
path: metaworld-plate-slide/test-*
- config_name: metaworld-plate-slide-back
data_files:
- split: train
path: metaworld-plate-slide-back/train-*
- split: test
path: metaworld-plate-slide-back/test-*
- config_name: metaworld-plate-slide-back-side
data_files:
- split: train
path: metaworld-plate-slide-back-side/train-*
- split: test
path: metaworld-plate-slide-back-side/test-*
- config_name: metaworld-plate-slide-side
data_files:
- split: train
path: metaworld-plate-slide-side/train-*
- split: test
path: metaworld-plate-slide-side/test-*
- config_name: metaworld-push
data_files:
- split: train
path: metaworld-push/train-*
- split: test
path: metaworld-push/test-*
- config_name: metaworld-push-back
data_files:
- split: train
path: metaworld-push-back/train-*
- split: test
path: metaworld-push-back/test-*
- config_name: metaworld-push-wall
data_files:
- split: train
path: metaworld-push-wall/train-*
- split: test
path: metaworld-push-wall/test-*
- config_name: metaworld-reach
data_files:
- split: train
path: metaworld-reach/train-*
- split: test
path: metaworld-reach/test-*
- config_name: metaworld-reach-wall
data_files:
- split: train
path: metaworld-reach-wall/train-*
- split: test
path: metaworld-reach-wall/test-*
- config_name: metaworld-shelf-place
data_files:
- split: train
path: metaworld-shelf-place/train-*
- split: test
path: metaworld-shelf-place/test-*
- config_name: metaworld-soccer
data_files:
- split: train
path: metaworld-soccer/train-*
- split: test
path: metaworld-soccer/test-*
- config_name: metaworld-stick-pull
data_files:
- split: train
path: metaworld-stick-pull/train-*
- split: test
path: metaworld-stick-pull/test-*
- config_name: metaworld-stick-push
data_files:
- split: train
path: metaworld-stick-push/train-*
- split: test
path: metaworld-stick-push/test-*
- config_name: metaworld-sweep
data_files:
- split: train
path: metaworld-sweep/train-*
- split: test
path: metaworld-sweep/test-*
- config_name: metaworld-sweep-into
data_files:
- split: train
path: metaworld-sweep-into/train-*
- split: test
path: metaworld-sweep-into/test-*
- config_name: metaworld-window-close
data_files:
- split: train
path: metaworld-window-close/train-*
- split: test
path: metaworld-window-close/test-*
- config_name: metaworld-window-open
data_files:
- split: train
path: metaworld-window-open/train-*
- split: test
path: metaworld-window-open/test-*
- config_name: mujoco-ant
data_files:
- split: train
path: mujoco-ant/train-*
- split: test
path: mujoco-ant/test-*
- config_name: mujoco-doublependulum
data_files:
- split: train
path: mujoco-doublependulum/train-*
- split: test
path: mujoco-doublependulum/test-*
- config_name: mujoco-halfcheetah
data_files:
- split: train
path: mujoco-halfcheetah/train-*
- split: test
path: mujoco-halfcheetah/test-*
- config_name: mujoco-hopper
data_files:
- split: train
path: mujoco-hopper/train-*
- split: test
path: mujoco-hopper/test-*
- config_name: mujoco-humanoid
data_files:
- split: train
path: mujoco-humanoid/train-*
- split: test
path: mujoco-humanoid/test-*
- config_name: mujoco-pendulum
data_files:
- split: train
path: mujoco-pendulum/train-*
- split: test
path: mujoco-pendulum/test-*
- config_name: mujoco-pusher
data_files:
- split: train
path: mujoco-pusher/train-*
- split: test
path: mujoco-pusher/test-*
- config_name: mujoco-reacher
data_files:
- split: train
path: mujoco-reacher/train-*
- split: test
path: mujoco-reacher/test-*
- config_name: mujoco-standup
data_files:
- split: train
path: mujoco-standup/train-*
- split: test
path: mujoco-standup/test-*
- config_name: mujoco-swimmer
data_files:
- split: train
path: mujoco-swimmer/train-*
- split: test
path: mujoco-swimmer/test-*
- config_name: mujoco-walker
data_files:
- split: train
path: mujoco-walker/train-*
- split: test
path: mujoco-walker/test-*
- config_name: ok-vqa
data_files:
- split: train
path: ok-vqa/train-*
- split: test
path: ok-vqa/test-*
---
# Dataset Card for "gia-dataset-tokenized-1024"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 102,143 | [
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bot-yaya/undl_text | 2023-10-07T00:31:07.000Z | [
"region:us"
] | bot-yaya | null | null | 0 | 2,141 | 2023-10-06T14:35:49 | ---
dataset_info:
features:
- name: ar
dtype: string
- name: zh
dtype: string
- name: en
dtype: string
- name: fr
dtype: string
- name: ru
dtype: string
- name: es
dtype: string
- name: de
dtype: string
- name: record
dtype: string
splits:
- name: train
num_bytes: 48667711040
num_examples: 165840
download_size: 18648916788
dataset_size: 48667711040
---
# Dataset Card for "undl_text"
pandoc转docx出的源文本，所用命令为：pandoc -i {filepath} -t plain -o {outpath} --strip-comments
这些文本可能仍需一定的步骤去噪，比如去掉全是横线的分隔符、去掉表格元素，才能用于后续的翻译及对齐步骤 | 587 | [
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] |
story_cloze | 2023-04-05T13:40:54.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Story Cloze Test' is a commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story. | @inproceedings{mostafazadeh2017lsdsem,
title={Lsdsem 2017 shared task: The story cloze test},
author={Mostafazadeh, Nasrin and Roth, Michael and Louis, Annie and Chambers, Nathanael and Allen, James},
booktitle={Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics},
pages={46--51},
year={2017}
} | 7 | 2,138 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: null
pretty_name: Story Cloze Test
dataset_info:
- config_name: '2016'
features:
- name: story_id
dtype: string
- name: input_sentence_1
dtype: string
- name: input_sentence_2
dtype: string
- name: input_sentence_3
dtype: string
- name: input_sentence_4
dtype: string
- name: sentence_quiz1
dtype: string
- name: sentence_quiz2
dtype: string
- name: answer_right_ending
dtype: int32
splits:
- name: validation
num_bytes: 614084
num_examples: 1871
- name: test
num_bytes: 613184
num_examples: 1871
download_size: 0
dataset_size: 1227268
- config_name: '2018'
features:
- name: story_id
dtype: string
- name: input_sentence_1
dtype: string
- name: input_sentence_2
dtype: string
- name: input_sentence_3
dtype: string
- name: input_sentence_4
dtype: string
- name: sentence_quiz1
dtype: string
- name: sentence_quiz2
dtype: string
- name: answer_right_ending
dtype: int32
splits:
- name: validation
num_bytes: 515439
num_examples: 1571
download_size: 0
dataset_size: 515439
---
# Dataset Card for "story_cloze"
## 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://cs.rochester.edu/nlp/rocstories/](https://cs.rochester.edu/nlp/rocstories/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Lsdsem 2017 shared task: The story cloze test](https://aclanthology.org/W17-0906.pdf)
- **Point of Contact:** [Nasrin Mostafazadeh](nasrinm@cs.rochester.edu)
- **Size of downloaded dataset files:** 2.13 MB
- **Size of the generated dataset:** 2.13 MB
- **Total amount of disk used:** 2.15 MB
### Dataset Summary
Story Cloze Test' is a new commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story.
### Supported Tasks and Leaderboards
commonsense reasoning
### Languages
English
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 2.13 MB
- **Size of the generated dataset:** 2.13 MB
- **Total amount of disk used:** 2.15 MB
An example of 'train' looks as follows.
```
{'answer_right_ending': 1,
'input_sentence_1': 'Rick grew up in a troubled household.',
'input_sentence_2': 'He never found good support in family, and turned to gangs.',
'input_sentence_3': "It wasn't long before Rick got shot in a robbery.",
'input_sentence_4': 'The incident caused him to turn a new leaf.',
'sentence_quiz1': 'He is happy now.',
'sentence_quiz2': 'He joined a gang.',
'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'}
```
### Data Fields
The data fields are the same among all splits.
- `input_sentence_1`: The first statement in the story.
- `input_sentence_2`: The second statement in the story.
- `input_sentence_3`: The third statement in the story.
- `input_sentence_4`: The forth statement in the story.
- `sentence_quiz1`: first possible continuation of the story.
- `sentence_quiz2`: second possible continuation of the story.
- `answer_right_ending`: correct possible ending; either 1 or 2.
- `story_id`: story id.
### Data Splits
| name |validation |test|
|-------|-----:|---:|
|2016|1871|1871|
|2018|1571|-|
## 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{mostafazadeh2017lsdsem,
title={Lsdsem 2017 shared task: The story cloze test},
author={Mostafazadeh, Nasrin and Roth, Michael and Louis, Annie and Chambers, Nathanael and Allen, James},
booktitle={Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics},
pages={46--51},
year={2017}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai). | 7,056 | [
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danbider/codegen | 2023-07-21T01:53:30.000Z | [
"region:us"
] | danbider | null | null | 0 | 2,121 | 2023-07-20T23:14:53 | Entry not found | 15 | [
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GAIR/lima | 2023-06-08T02:40:19.000Z | [
"license:other",
"arxiv:2305.11206",
"region:us"
] | GAIR | A high-quality dataset for efficient instruction tuning. | null | 298 | 2,102 | 2023-06-07T05:16:04 | ---
license: other
---
Dataset for [LIMA: Less Is More for Alignment](https://arxiv.org/pdf/2305.11206.pdf)
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("GAIR/lima")
```
## License
If the source data of LIMA has a stricter license than CC BY-NC-SA, the LIMA dataset follows the same. Otherwise, it follows the CC BY-NC-SA license.
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EuropeanParliament/Eurovoc | 2023-10-26T12:28:18.000Z | [
"license:eupl-1.1",
"region:us"
] | EuropeanParliament | null | null | 0 | 2,100 | 2023-09-01T07:46:44 | ---
license: eupl-1.1
configs:
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---
# 🇪🇺 🏷️ EuroVoc dataset
This dataset contains more that 3,700,000 documents in 39 languages with associated EuroVoc labels.
## What's Cellar ?
Cellar is the common data repository of the Publications Office of the European Union. Digital publications and metadata are stored in and disseminated via Cellar, in order to be used by humans and machines. Aiming to transparently serve users, Cellar stores multilingual publications and metadata, it is open to all EU citizens and provides machine-readable data.
https://op.europa.eu/fr/web/cellar
## Why was this dataset created ?
"Extreme classification come with challenges of scalability due to large label spaces, data sparsity issues due to insufficient training samples."
https://medium.com/datapy-ai/extreme-multi-label-classification-for-eurovoc-b51d74623820
## How was dataset this created ?
The source code is available, check `cellar.py`
## When this dataset was created ?
14 July 2023
## What are the main characteristics of this dataset ?
There are a total of 39 different languages present in this dataset, of which some are EU languages and some are not. As the following graph illustrates, most of the documents of the dataset are written in EU languages (English being the most present language in the dataset), and the non-EU languages are very poorly represented (for example Arabic, Japanese,...). Note that since the Irish language (`gle`) was granted full official and working status in the EU in 2022, there are very few documents in that language. Additionally, Croatian (`hrv`) is also less represented in the dataset as Croatia is the latest country to have joined the EU in 2013.
The lengths of the documents also varies depending on the language it is written in. The document lengths are quite variable, especially in English. There is therefore a quite large disparity in document lengths in this dataset. Note that this boxplot does not present the outliers, since the length of certain documents can contain up to 86 million characters. The red lines in the boxplot indicates the median length of the documents for each language.
We notice that the documents in Irish have a very wide variability in document lengths, due to the fact it has very few documents. Therefore, we present the same boxplot without the Irish language in order to visualize with more detail the document length distribution in the other languages.
## How is the data structured ?
An example of a sample of this dataset is the following :
```json
{
"title": "Commission information notice...",
"date": "2023-09-29",
"eurovoc_concepts": ["air transport", "intra-EU transport"],
"url": "http://publications.europa.eu/resource/cellar/ec99987f-5e69-11ee-9220-01aa75ed71a1",
"lang": "eng",
"formats": ["fmx4", "pdfa2a", "xhtml"],
"text": "To ensure ownership by the relevant actors,..."
}
```
- `title` : title of the document
- `date` : publication date of the document
- `eurovoc_concepts` : list of the EuroVoc concepts related to this document
- `url` : URL to access the document
- `formats` : list of formats in which the original document is available
- `text` : text content of the document
## Bibliography
- Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2019. Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 78–87, Minneapolis, Minnesota. Association for Computational Linguistics.
- I. Chalkidis, M. Fergadiotis, P. Malakasiotis and I. Androutsopoulos, Large-Scale Multi-Label Text Classification on EU Legislation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, (short papers), 2019.
- Andrei-Marius Avram, Vasile Pais, and Dan Ioan Tufis. 2021. PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 92–101, Held Online. INCOMA Ltd..
- SHAHEEN, Zein, WOHLGENANNT, Gerhard, et FILTZ, Erwin. Large scale legal text classification using transformer models. arXiv preprint arXiv:2010.12871, 2020.
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RussianNLP/russian_super_glue | 2023-06-19T12:23:49.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text-generation",
"task_ids:natural-language-inference",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"size_categories:10M<n<100M",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:ru",
"license:mit",
"glue",
"qa",
"superGLUE",
"NLI",
"reasoning",
"arxiv:2202.07791",
"region:us"
] | RussianNLP | Recent advances in the field of universal language models and transformers require the development of a methodology for
their broad diagnostics and testing for general intellectual skills - detection of natural language inference,
commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon. For the first
time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology, was developed from
scratch for the Russian language. We provide baselines, human level evaluation, an open-source framework for evaluating
models and an overall leaderboard of transformer models for the Russian language. | @article{shavrina2020russiansuperglue,
title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark},
author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova,
Ekaterina and Malykh, Valentin and Mikhailov, Vladislav and Tikhonova, Maria and Chertok, Andrey and
Evlampiev, Andrey},
journal={arXiv preprint arXiv:2010.15925},
year={2020}
} | 15 | 2,099 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-classification
- question-answering
- zero-shot-classification
- text-generation
task_ids:
- natural-language-inference
- multi-class-classification
pretty_name: Russian SuperGLUE
language_bcp47:
- ru-RU
dataset_info:
- config_name: lidirus
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: knowledge
dtype: string
- name: lexical-semantics
dtype: string
- name: logic
dtype: string
- name: predicate-argument-structure
dtype: string
- name: idx
dtype: int32
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
splits:
- name: test
num_bytes: 470306
num_examples: 1104
download_size: 47118
dataset_size: 470306
- config_name: rcb
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: verb
dtype: string
- name: negation
dtype: string
- name: idx
dtype: int32
- name: label
dtype:
class_label:
names:
'0': entailment
'1': contradiction
'2': neutral
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num_bytes: 199712
num_examples: 438
- name: validation
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num_examples: 220
- name: test
num_bytes: 207031
num_examples: 438
download_size: 136700
dataset_size: 504736
- config_name: parus
features:
- name: premise
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: question
dtype: string
- name: idx
dtype: int32
- name: label
dtype:
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'1': choice2
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- name: validation
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num_examples: 100
- name: test
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num_examples: 500
download_size: 57585
dataset_size: 187056
- config_name: muserc
features:
- name: paragraph
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: idx
struct:
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dtype: int32
- name: question
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- name: answer
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- config_name: terra
features:
- name: premise
dtype: string
- name: hypothesis
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'1': not_entailment
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- name: test
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download_size: 907346
dataset_size: 3284227
- config_name: russe
features:
- name: word
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: start1
dtype: int32
- name: start2
dtype: int32
- name: end1
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- name: end2
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- name: gold_sense1
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- name: gold_sense2
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- config_name: rwsd
features:
- name: text
dtype: string
- name: span1_index
dtype: int32
- name: span2_index
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download_size: 40508
dataset_size: 279284
- config_name: danetqa
features:
- name: question
dtype: string
- name: passage
dtype: string
- name: idx
dtype: int32
- name: label
dtype:
class_label:
names:
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'1': 'True'
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- name: test
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num_examples: 805
download_size: 1293761
dataset_size: 4573523
- config_name: rucos
features:
- name: passage
dtype: string
- name: query
dtype: string
- name: entities
sequence: string
- name: answers
sequence: string
- name: idx
struct:
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dtype: int32
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dtype: int32
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num_examples: 7257
download_size: 56208297
dataset_size: 192611150
tags:
- glue
- qa
- superGLUE
- NLI
- reasoning
---
# Dataset Card for [Russian SuperGLUE]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://russiansuperglue.com/
- **Repository:** https://github.com/RussianNLP/RussianSuperGLUE
- **Paper:** https://russiansuperglue.com/download/main_article
- **Leaderboard:** https://russiansuperglue.com/leaderboard/2
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Modern universal language models and transformers such as BERT, ELMo, XLNet, RoBERTa and others need to be properly
compared and evaluated. In the last year, new models and methods for pretraining and transfer learning have driven
striking performance improvements across a range of language understanding tasks.
We offer testing methodology based on tasks, typically proposed for “strong AI” — logic, commonsense, reasoning.
Adhering to the GLUE and SuperGLUE methodology, we present a set of test tasks for general language understanding
and leaderboard models.
For the first time a complete test for Russian language was developed, which is similar to its English analog.
Many datasets were composed for the first time, and a leaderboard of models for the Russian language with comparable
results is also presented.
### Supported Tasks and Leaderboards
Supported tasks, barring a few additions, are equivalent to the original SuperGLUE tasks.
|Task Name|Equiv. to|
|----|---:|
|Linguistic Diagnostic for Russian|Broadcoverage Diagnostics (AX-b)|
|Russian Commitment Bank (RCB)|CommitmentBank (CB)|
|Choice of Plausible Alternatives for Russian language (PARus)|Choice of Plausible Alternatives (COPA)|
|Russian Multi-Sentence Reading Comprehension (MuSeRC)|Multi-Sentence Reading Comprehension (MultiRC)|
|Textual Entailment Recognition for Russian (TERRa)|Recognizing Textual Entailment (RTE)|
|Russian Words in Context (based on RUSSE)|Words in Context (WiC)|
|The Winograd Schema Challenge (Russian)|The Winograd Schema Challenge (WSC)|
|Yes/no Question Answering Dataset for the Russian (DaNetQA)|BoolQ|
|Russian Reading Comprehension with Commonsense Reasoning (RuCoS)|Reading Comprehension with Commonsense Reasoning (ReCoRD)|
### Languages
All tasks are in Russian.
## Dataset Structure
### Data Instances
Note that there are no labels in the `test` splits. This is signified by the `-1` value.
#### LiDiRus
- **Size of downloaded dataset files:** 0.05 MB
- **Size of the generated dataset:** 0.49 MB
- **Total amount of disk used:** 0.54 MB
An example of 'test' looks as follows
```
{
"sentence1": "Новая игровая консоль доступна по цене.",
"sentence2": "Новая игровая консоль недоступна по цене.",
"knowledge": "",
"lexical-semantics": "Morphological negation",
"logic": "Negation",
"predicate-argument-structure": "",
"idx": 10,
"label": 1
}
```
#### RCB
- **Size of downloaded dataset files:** 0.14 MB
- **Size of the generated dataset:** 0.53 MB
- **Total amount of disk used:** 0.67 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "— Пойдём пообедаем. Я с утра ничего не ел. Отель, как видишь, весьма посредственный, но мне сказали,
что в здешнем ресторане отлично готовят.",
"hypothesis": "В здешнем ресторане отлично готовят.",
"verb": "сказать",
"negation": "no_negation",
"idx": 10,
"label": 2
}
```
An example of 'test' looks as follows
```
{
"premise": "Я уверен, что вместе мы победим. Да, парламентское большинство думает иначе.",
"hypothesis": "Вместе мы проиграем.",
"verb": "думать",
"negation": "no_negation",
"idx": 10,
"label": -1
}
```
#### PARus
- **Size of downloaded dataset files:** 0.06 MB
- **Size of the generated dataset:** 0.20 MB
- **Total amount of disk used:** 0.245 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "Женщина чинила кран.",
"choice1": "Кран подтекал.",
"choice2": "Кран был выключен.",
"question": "cause",
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"premise": "Ребятам было страшно.",
"choice1": "Их вожатый рассказал им историю про призрака.",
"choice2": "Они жарили маршмеллоу на костре.",
"question": "cause",
"idx": 10,
"label": -1
}
```
#### MuSeRC
- **Size of downloaded dataset files:** 1.26 MB
- **Size of the generated dataset:** 59.77 MB
- **Total amount of disk used:** 61.87 MB
An example of 'train'/'dev' looks as follows
```
{
"paragraph": "(1) Но люди не могут существовать без природы, поэтому в парке стояли железобетонные скамейки —
деревянные моментально ломали. (2) В парке бегали ребятишки, водилась шпана, которая развлекалась игрой в карты,
пьянкой, драками, «иногда насмерть». (3) «Имали они тут и девок...» (4) Верховодил шпаной Артемка-мыло, с
вспененной белой головой. (5) Людочка сколько ни пыталась усмирить лохмотья на буйной голове Артемки, ничего у
неё не получалось. (6) Его «кудри, издали напоминавшие мыльную пену, изблизя оказались что липкие рожки из
вокзальной столовой — сварили их, бросили комком в пустую тарелку, так они, слипшиеся, неподъёмно и лежали.
(7) Да и не ради причёски приходил парень к Людочке. (8) Как только её руки становились занятыми ножницами
и расчёской, Артемка начинал хватать её за разные места. (9) Людочка сначала увёртывалась от хватких рук Артемки,
а когда не помогло, стукнула его машинкой по голове и пробила до крови, пришлось лить йод на голову «ухажористого
человека». (10) Артемка заулюлюкал и со свистом стал ловить воздух. (11) С тех пор «домогания свои хулиганские
прекратил», более того, шпане повелел Людочку не трогать.",
"question": "Как развлекались в парке ребята?",
"answer": "Развлекались игрой в карты, пьянкой, драками, снимали они тут и девок.",
"idx":
{
"paragraph": 0,
"question": 2,
"answer": 10
},
"label": 1
}
```
An example of 'test' looks as follows
```
{
"paragraph": "\"(1) Издательство Viking Press совместно с компанией TradeMobile выпустят мобильное приложение,
посвященное Анне Франк, передает The Daily Telegraph. (2) Программа будет включать в себя фрагменты из дневника
Анны, озвученные британской актрисой Хеленой Бонэм Картер. (3) Помимо этого, в приложение войдут фотографии
и видеозаписи, документы из архива Фонда Анны Франк, план здания в Амстердаме, где Анна с семьей скрывались от
нацистов, и факсимильные копии страниц дневника. (4) Приложение, которое получит название Anne Frank App, выйдет
18 октября. (5) Интерфейс программы будет англоязычным. (6) На каких платформах будет доступно Anne Frank App,
не уточняется. Анна Франк родилась в Германии в 1929 году. (7) Когда в стране начались гонения на евреев, Анна с
семьей перебрались в Нидерланды. (8) С 1942 года члены семьи Франк и еще несколько человек скрывались от нацистов
в потайных комнатах дома в Амстердаме, который занимала компания отца Анны. (9) В 1944 году группу по доносу
обнаружили гестаповцы. (10) Обитатели \"Убежища\" (так Анна называла дом в дневнике) были отправлены в концлагеря;
выжить удалось только отцу девочки Отто Франку. (11) Находясь в \"Убежище\", Анна вела дневник, в котором описывала
свою жизнь и жизнь своих близких. (12) После ареста книгу с записями сохранила подруга семьи Франк и впоследствии
передала ее отцу Анны. (13) Дневник был впервые опубликован в 1947 году. (14) Сейчас он переведен более
чем на 60 языков.\"",
"question": "Какая информация войдет в новой мобильное приложение?",
"answer": "Видеозаписи Анны Франк.",
"idx":
{
"paragraph": 0,
"question": 2,
"answer": 10
},
"label": -1
}
```
#### TERRa
- **Size of downloaded dataset files:** 0.93 MB
- **Size of the generated dataset:** 3.44 MB
- **Total amount of disk used:** 4.39 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "Музей, расположенный в Королевских воротах, меняет экспозицию. На смену выставке, рассказывающей об
истории ворот и их реставрации, придет «Аптека трех королей». Как рассказали в музее, посетители попадут в
традиционный интерьер аптеки.",
"hypothesis": "Музей закроется навсегда.",
"idx": 10,
"label": 1
}
```
An example of 'test' looks as follows
```
{
"premise": "Маршрутка полыхала несколько минут. Свидетели утверждают, что приезду пожарных салон «Газели» выгорел полностью. К счастью, пассажиров внутри не было, а водитель успел выскочить из кабины.",
"hypothesis": "Маршрутка выгорела.",
"idx": 10,
"label": -1
}
```
#### RUSSE
- **Size of downloaded dataset files:** 3.88 MB
- **Size of the generated dataset:** 20.97 MB
- **Total amount of disk used:** 25.17 MB
An example of 'train'/'dev' looks as follows
```
{
"word": "дух",
"sentence1": "Завертелась в доме веселая коловерть: праздничный стол, праздничный дух, шумные разговоры",
"sentence2": "Вижу: духи собралися / Средь белеющих равнин. // Бесконечны, безобразны, / В мутной месяца игре / Закружились бесы разны, / Будто листья в ноябре",
"start1": 68,
"start2": 6,
"end1": 72,
"end2": 11,
"gold_sense1": 3,
"gold_sense2": 4,
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"word": "доска",
"sentence1": "На 40-й день после трагедии в переходе была установлена мемориальная доска, надпись на которой гласит: «В память о погибших и пострадавших от террористического акта 8 августа 2000 года».",
"sentence2": "Фото с 36-летним миллиардером привлекло сеть его необычной фигурой при стойке на доске и кремом на лице.",
"start1": 69,
"start2": 81,
"end1": 73,
"end2": 85,
"gold_sense1": -1,
"gold_sense2": -1,
"idx": 10,
"label": -1
}
```
#### RWSD
- **Size of downloaded dataset files:** 0.04 MB
- **Size of the generated dataset:** 0.29 MB
- **Total amount of disk used:** 0.320 MB
An example of 'train'/'dev' looks as follows
```
{
"text": "Женя поблагодарила Сашу за помощь, которую она оказала.",
"span1_index": 0,
"span2_index": 6,
"span1_text": "Женя",
"span2_text": "она оказала",
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"text": "Мод и Дора видели, как через прерию несутся поезда, из двигателей тянулись клубы черного дыма. Ревущие
звуки их моторов и дикие, яростные свистки можно было услышать издалека. Лошади убежали, когда они увидели
приближающийся поезд.",
"span1_index": 22,
"span2_index": 30,
"span1_text": "свистки",
"span2_text": "они увидели",
"idx": 10,
"label": -1
}
```
#### DaNetQA
- **Size of downloaded dataset files:** 1.36 MB
- **Size of the generated dataset:** 4.82 MB
- **Total amount of disk used:** 5.9 MB
An example of 'train'/'dev' looks as follows
```
{
"question": "Вреден ли алкоголь на первых неделях беременности?",
"passage": "А Бакингем-Хоуз и её коллеги суммировали последствия, найденные в обзорных статьях ранее. Частые случаи
задержки роста плода, результатом чего является укороченный средний срок беременности и сниженный вес при рождении.
По сравнению с нормальными детьми, дети 3-4-недельного возраста демонстрируют «менее оптимальную» двигательную
активность, рефлексы, и ориентацию в пространстве, а дети 4-6 лет показывают низкий уровень работы
нейроповеденческих функций, внимания, эмоциональной экспрессии, и развития речи и языка. Величина этих влияний
часто небольшая, частично в связи с независимыми переменными: включая употребление во время беременности
алкоголя/табака, а также факторы среды . У детей школьного возраста проблемы с устойчивым вниманием и контролем
своего поведения, а также незначительные с ростом, познавательными и языковыми способностями.",
"idx": 10,
"label": 1
}
```
An example of 'test' looks as follows
```
{
"question": "Вредна ли жесткая вода?",
"passage": "Различают временную жёсткость, обусловленную гидрокарбонатами кальция и магния Са2; Mg2, и постоянную
жёсткость, вызванную присутствием других солей, не выделяющихся при кипячении воды: в основном, сульфатов и
хлоридов Са и Mg. Жёсткая вода при умывании сушит кожу, в ней плохо образуется пена при использовании мыла.
Использование жёсткой воды вызывает появление осадка на стенках котлов, в трубах и т. п. В то же время,
использование слишком мягкой воды может приводить к коррозии труб, так как, в этом случае отсутствует
кислотно-щелочная буферность, которую обеспечивает гидрокарбонатная жёсткость. Потребление жёсткой или мягкой
воды обычно не является опасным для здоровья, однако есть данные о том, что высокая жёсткость способствует
образованию мочевых камней, а низкая — незначительно увеличивает риск сердечно-сосудистых заболеваний. Вкус
природной питьевой воды, например, воды родников, обусловлен именно присутствием солей жёсткости.",
"idx": 100,
"label": -1
}
```
#### RuCoS
- **Size of downloaded dataset files:** 56.62 MB
- **Size of the generated dataset:** 202.38 MB
- **Total amount of disk used:** 261.10 MB
An example of 'train'/'dev' looks as follows
```
{
"passage": "В Абхазии 24 августа на досрочных выборах выбирают нового президента. Кто бы ни стал победителем,
возможности его будут ограничены, говорят эксперты, опрошенные DW. В Абхазии 24 августа проходят досрочные выборы
президента не признанной международным сообществом республики. Толчком к их проведению стали массовые протесты в
конце мая 2014 года, в результате которых со своего поста был вынужден уйти действующий президент Абхазии Александр
Анкваб. Эксперты называют среди наиболее перспективных кандидатов находящегося в оппозиции политика Рауля Хаджимбу,
экс-главу службы безопасности Аслана Бжанию и генерала Мираба Кишмарию, исполняющего обязанности министра обороны.
У кого больше шансов\n\"Ставки делаются на победу Хаджимбы.\n@highlight\nВ Швеции задержаны двое граждан РФ в связи
с нападением на чеченского блогера\n@highlight\nТуризм в эпоху коронавируса: куда поехать? И ехать ли
вообще?\n@highlight\nКомментарий: Россия накануне эпидемии - виноватые назначены заранее",
"query": "Несмотря на то, что Кремль вложил много денег как в @placeholder, так и в Южную Осетию, об экономическом
восстановлении данных регионов говорить не приходится, считает Хальбах: \"Многие по-прежнему живут в
полуразрушенных домах и временных жилищах\".",
"entities":
[
"DW.",
"Абхазии ",
"Александр Анкваб.",
"Аслана Бжанию ",
"Мираба Кишмарию,",
"РФ ",
"Рауля Хаджимбу,",
"Россия ",
"Хаджимбы.",
"Швеции "
],
"answers":
[
"Абхазии"
],
"idx":
{
"passage": 500,
"query": 500
}
}
```
An example of 'test' looks as follows
```
{
"passage": "Почему и как изменится курс белорусского рубля? Какие инструменты следует предпочесть населению, чтобы
сохранить сбережения, DW рассказали финансовые аналитики Беларуси. На последних валютных торгах БВФБ 2015 года в
среду, 30 декабря, курс белорусского рубля к доллару - 18569, к евро - 20300, к российскому рублю - 255. В 2016
году белорусскому рублю пророчат падение как минимум на 12 процентов к корзине валют, к которой привязан его курс.
А чтобы избежать потерь, белорусам советуют диверсифицировать инвестиционные портфели. Чем обусловлены прогнозные
изменения котировок белорусского рубля, и какие финансовые инструменты стоит предпочесть, чтобы минимизировать риск
потерь?\n@highlight\nВ Германии за сутки выявлено более 100 новых заражений коронавирусом\n@highlight\nРыночные цены
на нефть рухнули из-за провала переговоров ОПЕК+\n@highlight\nВ Италии за сутки произошел резкий скачок смертей от
COVID-19",
"query": "Последнее, убежден аналитик, инструмент для узкого круга профессиональных инвесторов, культуры следить за
финансовым состоянием предприятий - такой, чтобы играть на рынке корпоративных облигаций, - в @placeholder пока нет.",
"entities":
[
"DW ",
"Беларуси.",
"Германии ",
"Италии ",
"ОПЕК+"
],
"answers": [],
"idx":
{
"passage": 500,
"query": 500
}
}
```
### Data Fields
#### LiDiRus
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `not_entailment` (1)
- `sentence1`: a `string` feature
- `sentence2`: a `string` feature
- `knowledge`: a `string` feature with possible values `''`, `'World knowledge'`, `'Common sense'`
- `lexical-semantics`: a `string` feature
- `logic`: a `string` feature
- `predicate-argument-structure`: a `string` feature
#### RCB
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `contradiction` (1), `neutral` (2)
- `premise`: a `string` feature
- `hypothesis`: a `string` feature
- `verb`: a `string` feature
- `negation`: a `string` feature with possible values `'no_negation'`, `'negation'`, `''`, `'double_negation'`
#### PARus
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `choice1` (0), `choice2` (1)
- `premise`: a `string` feature
- `choice1`: a `string` feature
- `choice2`: a `string` feature
- `question`: a `string` feature with possible values `'cause'`, `'effect'`
#### MuSeRC
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0) , `true` (1) (does the provided `answer` contain
a factual response to the `question`)
- `paragraph`: a `string` feature
- `question`: a `string` feature
- `answer`: a `string` feature
#### TERRa
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `not_entailment` (1)
- `premise`: a `string` feature
- `hypothesis`: a `string` feature
#### RUSSE
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (whether the given `word` used in the
same sense in both sentences)
- `word`: a `string` feature
- `sentence1`: a `string` feature
- `sentence2`: a `string` feature
- `gold_sense1`: an `int32` feature
- `gold_sense2`: an `int32` feature
- `start1`: an `int32` feature
- `start2`: an `int32` feature
- `end1`: an `int32` feature
- `end2`: an `int32` feature
#### RWSD
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (whether the given spans are
coreferential)
- `text`: a `string` feature
- `span1_index`: an `int32` feature
- `span2_index`: an `int32` feature
- `span1_text`: a `string` feature
- `span2_text`: a `string` feature
#### DaNetQA
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (yes/no answer to the `question` found
in the `passage`)
- `question`: a `string` feature
- `passage`: a `string` feature
#### RuCoS
- `idx`: an `int32` feature
- `passage`: a `string` feature
- `query`: a `string` feature
- `entities`: a `list of strings` feature
- `answers`: a `list of strings` feature
[More Information Needed]
### Data Splits
#### LiDiRus
| |test|
|---|---:|
|LiDiRus|1104|
#### RCB
| |train|validation|test|
|----|---:|----:|---:|
|RCB|438|220|438|
#### PARus
| |train|validation|test|
|----|---:|----:|---:|
|PARus|400|100|500|
#### MuSeRC
| |train|validation|test|
|----|---:|----:|---:|
|MuSeRC|500|100|322|
#### TERRa
| |train|validation|test|
|----|---:|----:|---:|
|TERRa|2616|307|3198|
#### RUSSE
| |train|validation|test|
|----|---:|----:|---:|
|RUSSE|19845|8508|18892|
#### RWSD
| |train|validation|test|
|----|---:|----:|---:|
|RWSD|606|204|154|
#### DaNetQA
| |train|validation|test|
|----|---:|----:|---:|
|DaNetQA|1749|821|805|
#### RuCoS
| |train|validation|test|
|----|---:|----:|---:|
|RuCoS|72193|7577|7257|
## 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
All our datasets are published by MIT License.
### Citation Information
```
@article{shavrina2020russiansuperglue,
title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark},
author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova, Ekaterina and Malykh, Valentin and Mikhailov, Vladislav and Tikhonova, Maria and Chertok, Andrey and Evlampiev, Andrey},
journal={arXiv preprint arXiv:2010.15925},
year={2020}
}
@misc{fenogenova2022russian,
title={Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models},
author={Alena Fenogenova and Maria Tikhonova and Vladislav Mikhailov and Tatiana Shavrina and Anton Emelyanov and Denis Shevelev and Alexandr Kukushkin and Valentin Malykh and Ekaterina Artemova},
year={2022},
eprint={2202.07791},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@slowwavesleep](https://github.com/slowwavesleep) for adding this dataset. | 28,658 | [
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codeparrot/github-code-clean | 2022-07-05T09:35:14.000Z | [
"license:apache-2.0",
"region:us"
] | codeparrot | The GitHub Code clean dataset in a more filtered version of codeparrot/github-code dataset, it consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in almost 1TB of text data. | null | 55 | 2,099 | 2022-06-29T23:08:17 | ---
license: apache-2.0
---
This is a cleaner version of [Github-code dataset](https://huggingface.co/datasets/codeparrot/github-code), we add the following filters:
* Average line length < 100
* Alpha numeric characters fraction > 0.25
* Remove auto-generated files (keyword search)
3.39M files are removed making up 2.94% of the dataset. | 340 | [
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] |
BeIR/nfcorpus | 2022-10-23T06:01:44.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 0 | 2,090 | 2022-06-05T16:27:38 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
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hf-internal-testing/example-documents | 2022-08-04T12:42:46.000Z | [
"region:us"
] | hf-internal-testing | null | null | 1 | 2,084 | 2022-07-28T14:03:22 | Entry not found | 15 | [
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] | null | This is a collection of translated sentences from Tatoeba
359 languages, 3,403 bitexts
total number of files: 750
total number of tokens: 65.54M
total number of sentence fragments: 8.96M | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
} | 20 | 2,070 | 2022-03-02T23:29:22 | ---
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license:
- cc-by-2.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: tatoeba
pretty_name: Tatoeba
dataset_info:
- config_name: en-mr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- mr
splits:
- name: train
num_bytes: 6190484
num_examples: 53462
download_size: 1436200
dataset_size: 6190484
- config_name: eo-nl
features:
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dtype: string
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dtype:
translation:
languages:
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splits:
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num_examples: 93650
download_size: 3020382
dataset_size: 8150048
- config_name: es-pt
features:
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dtype: string
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dtype:
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languages:
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splits:
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num_bytes: 6180464
num_examples: 67782
download_size: 2340361
dataset_size: 6180464
- config_name: fr-ru
features:
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dtype: string
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dtype:
translation:
languages:
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splits:
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num_bytes: 19775390
num_examples: 195161
download_size: 5509784
dataset_size: 19775390
- config_name: es-gl
features:
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dtype: string
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dtype:
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languages:
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- gl
splits:
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num_bytes: 287683
num_examples: 3135
download_size: 128506
dataset_size: 287683
---
# Dataset Card for Tatoeba
## 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://opus.nlpl.eu/Tatoeba.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Tatoeba is a collection of sentences and translations.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Tatoeba.php
E.g.
`dataset = load_dataset("tatoeba", lang1="en", lang2="he")`
The default date is v2021-07-22, but you can also change the date with
`dataset = load_dataset("tatoeba", lang1="en", lang2="he", date="v2020-11-09")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- ab
- acm
- ady
- af
- afb
- afh
- aii
- ain
- ajp
- akl
- aln
- am
- an
- ang
- aoz
- apc
- ar
- arq
- ary
- arz
- as
- ast
- avk
- awa
- ayl
- az
- ba
- bal
- bar
- be
- ber
- bg
- bho
- bjn
- bm
- bn
- bo
- br
- brx
- bs
- bua
- bvy
- bzt
- ca
- cay
- cbk
- ce
- ceb
- ch
- chg
- chn
- cho
- chr
- cjy
- ckb
- ckt
- cmn
- co
- code
- cpi
- crh
- crk
- cs
- csb
- cv
- cy
- da
- de
- dng
- drt
- dsb
- dtp
- dv
- dws
- ee
- egl
- el
- emx
- en
- enm
- eo
- es
- et
- eu
- ext
- fi
- fj
- fkv
- fo
- fr
- frm
- fro
- frr
- fuc
- fur
- fuv
- fy
- ga
- gag
- gan
- gbm
- gcf
- gd
- gil
- gl
- gn
- gom
- gos
- got
- grc
- gsw
- gu
- gv
- ha
- hak
- haw
- hbo
- he
- hi
- hif
- hil
- hnj
- hoc
- hr
- hrx
- hsb
- hsn
- ht
- hu
- hy
- ia
- iba
- id
- ie
- ig
- ii
- ike
- ilo
- io
- is
- it
- izh
- ja
- jam
- jbo
- jdt
- jpa
- jv
- ka
- kaa
- kab
- kam
- kek
- kha
- kjh
- kk
- kl
- km
- kmr
- kn
- ko
- koi
- kpv
- krc
- krl
- ksh
- ku
- kum
- kw
- kxi
- ky
- kzj: Coastal Kadazan (deprecated tag; preferred value: Kadazan Dusun; Central Dusun (`dtp`))
- la
- laa
- lad
- lb
- ldn
- lfn
- lg
- lij
- liv
- lkt
- lld
- lmo
- ln
- lo
- lt
- ltg
- lut
- lv
- lzh
- lzz
- mad
- mai
- max
- mdf
- mfe
- mg
- mgm
- mh
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- mi
- mic
- min
- mk
- ml
- mn
- mni
- mnw
- moh
- mr
- mt
- mvv
- mwl
- mww
- my
- myv
- na
- nah
- nan
- nb
- nch
- nds
- ngt
- ngu
- niu
- nl
- nlv
- nn
- nog
- non
- nov
- npi
- nst
- nus
- nv
- ny
- nys
- oar
- oc
- ofs
- ood
- or
- orv
- os
- osp
- ota
- otk
- pa
- pag
- pal
- pam
- pap
- pau
- pcd
- pdc
- pes
- phn
- pi
- pl
- pms
- pnb
- ppl
- prg
- ps
- pt
- qu
- quc
- qya
- rap
- rif
- rm
- rn
- ro
- rom
- ru
- rue
- rw
- sa
- sah
- sc
- scn
- sco
- sd
- sdh
- se
- sg
- sgs
- shs
- shy
- si
- sjn
- sl
- sm
- sma
- sn
- so
- sq
- sr
- stq
- su
- sux
- sv
- swg
- swh
- syc
- ta
- te
- tet
- tg
- th
- thv
- ti
- tig
- tk
- tl
- tlh
- tly
- tmr
- tmw
- tn
- to
- toi
- tok
- tpi
- tpw
- tr
- ts
- tt
- tts
- tvl
- ty
- tyv
- tzl
- udm
- ug
- uk
- umb
- ur
- uz
- vec
- vep
- vi
- vo
- vro
- wa
- war
- wo
- wuu
- xal
- xh
- xqa
- yi
- yo
- yue
- zlm
- zsm
- zu
- zza
## 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. | 8,928 | [
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sberquad | 2023-08-29T12:35:15.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"license:unknown",
"arxiv:1912.09723",
"region:us"
] | null | Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Russian original analogue presented in Sberbank Data Science Journey 2017. | @article{Efimov_2020,
title={SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis},
ISBN={9783030582197},
ISSN={1611-3349},
url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},
DOI={10.1007/978-3-030-58219-7_1},
journal={Experimental IR Meets Multilinguality, Multimodality, and Interaction},
publisher={Springer International Publishing},
author={Efimov, Pavel and Chertok, Andrey and Boytsov, Leonid and Braslavski, Pavel},
year={2020},
pages={3–15}
} | 10 | 2,054 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
- crowdsourced
language:
- ru
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: sberquad
pretty_name: SberQuAD
dataset_info:
config_name: sberquad
features:
- name: id
dtype: int32
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 71631541
num_examples: 45328
- name: validation
num_bytes: 7972953
num_examples: 5036
- name: test
num_bytes: 36397776
num_examples: 23936
download_size: 10491714
dataset_size: 116002270
---
# Dataset Card for sberquad
## 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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/sberbank-ai/data-science-journey-2017
- **Paper:** https://arxiv.org/abs/1912.09723
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
Russian original analogue presented in Sberbank Data Science Journey 2017.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Russian
## Dataset Structure
### Data Instances
```
{
"context": "Первые упоминания о строении человеческого тела встречаются в Древнем Египте...",
"id": 14754,
"qas": [
{
"id": 60544,
"question": "Где встречаются первые упоминания о строении человеческого тела?",
"answers": [{"answer_start": 60, "text": "в Древнем Египте"}],
}
]
}
```
### Data Fields
- id: a int32 feature
- title: a string feature
- context: a string feature
- question: a string feature
- answers: a dictionary feature containing:
- text: a string feature
- answer_start: a int32 feature
### Data Splits
| name |train |validation|test |
|----------|-----:|---------:|-----|
|plain_text|45328 | 5036 |23936|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@InProceedings{sberquad,
doi = {10.1007/978-3-030-58219-7_1},
author = {Pavel Efimov and
Andrey Chertok and
Leonid Boytsov and
Pavel Braslavski},
title = {SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis},
booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction},
year = {2020},
publisher = {Springer International Publishing},
pages = {3--15}
}
```
### Contributions
Thanks to [@alenusch](https://github.com/Alenush) for adding this dataset. | 4,819 | [
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facebook/voxpopuli | 2022-10-14T13:43:12.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"language:en",
"language:de",
"language:fr",
"language:es",
"language:pl",
"language:it",
"language:ro",
"language:hu",
"language:cs",
"language:nl",
"language:fi",
"language:hr",
"language:sk",
"language:sl",
"language:et",
"language:lt",
"license:cc0-1.0",
"license:other",
"arxiv:2101.00390",
"region:us"
] | facebook | A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. | @inproceedings{wang-etal-2021-voxpopuli,
title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning,
Semi-Supervised Learning and Interpretation",
author = "Wang, Changhan and
Riviere, Morgane and
Lee, Ann and
Wu, Anne and
Talnikar, Chaitanya and
Haziza, Daniel and
Williamson, Mary and
Pino, Juan and
Dupoux, Emmanuel",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics
and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.80",
doi = "10.18653/v1/2021.acl-long.80",
pages = "993--1003",
} | 26 | 2,045 | 2022-05-10T14:42:49 | ---
annotations_creators: []
language:
- en
- de
- fr
- es
- pl
- it
- ro
- hu
- cs
- nl
- fi
- hr
- sk
- sl
- et
- lt
language_creators: []
license:
- cc0-1.0
- other
multilinguality:
- multilingual
pretty_name: VoxPopuli
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for Voxpopuli
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/facebookresearch/voxpopuli
- **Repository:** https://github.com/facebookresearch/voxpopuli
- **Paper:** https://arxiv.org/abs/2101.00390
- **Point of Contact:** [changhan@fb.com](mailto:changhan@fb.com), [mriviere@fb.com](mailto:mriviere@fb.com), [annl@fb.com](mailto:annl@fb.com)
### Dataset Summary
VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home). We acknowledge the European Parliament for creating and sharing these materials.
This implementation contains transcribed speech data for 18 languages.
It also contains 29 hours of transcribed speech data of non-native English intended for research in ASR for accented speech (15 L2 accents)
### Example usage
VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name:
```python
from datasets import load_dataset
voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr")
```
To load all the languages in a single dataset use "multilang" config name:
```python
voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang")
```
To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter:
```python
voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"])
```
To load accented English data, use "en_accented" config name:
```python
voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented")
```
**Note that L2 English subset contains only `test` split.**
### Supported Tasks and Leaderboards
* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
Accented English subset can also be used for research in ASR for accented speech (15 L2 accents)
### Languages
VoxPopuli contains labelled (transcribed) data for 18 languages:
| Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
|:---:|:---:|:---:|:---:|:---:|
| English | En | 543 | 1313 | 4.8M |
| German | De | 282 | 531 | 2.3M |
| French | Fr | 211 | 534 | 2.1M |
| Spanish | Es | 166 | 305 | 1.6M |
| Polish | Pl | 111 | 282 | 802K |
| Italian | It | 91 | 306 | 757K |
| Romanian | Ro | 89 | 164 | 739K |
| Hungarian | Hu | 63 | 143 | 431K |
| Czech | Cs | 62 | 138 | 461K |
| Dutch | Nl | 53 | 221 | 488K |
| Finnish | Fi | 27 | 84 | 160K |
| Croatian | Hr | 43 | 83 | 337K |
| Slovak | Sk | 35 | 96 | 270K |
| Slovene | Sl | 10 | 45 | 76K |
| Estonian | Et | 3 | 29 | 18K |
| Lithuanian | Lt | 2 | 21 | 10K |
| Total | | 1791 | 4295 | 15M |
Accented speech transcribed data has 15 various L2 accents:
| Accent | Code | Transcribed Hours | Transcribed Speakers |
|:---:|:---:|:---:|:---:|
| Dutch | en_nl | 3.52 | 45 |
| German | en_de | 3.52 | 84 |
| Czech | en_cs | 3.30 | 26 |
| Polish | en_pl | 3.23 | 33 |
| French | en_fr | 2.56 | 27 |
| Hungarian | en_hu | 2.33 | 23 |
| Finnish | en_fi | 2.18 | 20 |
| Romanian | en_ro | 1.85 | 27 |
| Slovak | en_sk | 1.46 | 17 |
| Spanish | en_es | 1.42 | 18 |
| Italian | en_it | 1.11 | 15 |
| Estonian | en_et | 1.08 | 6 |
| Lithuanian | en_lt | 0.65 | 7 |
| Croatian | en_hr | 0.42 | 9 |
| Slovene | en_sl | 0.25 | 7 |
## Dataset Structure
### Data Instances
```python
{
'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5',
'language': 11, # "hr"
'audio': {
'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav',
'array': array([-0.01434326, -0.01055908, 0.00106812, ..., 0.00646973], dtype=float32),
'sampling_rate': 16000
},
'raw_text': '',
'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.',
'gender': 'female',
'speaker_id': '119431',
'is_gold_transcript': True,
'accent': 'None'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `language` (datasets.ClassLabel) - numerical id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `raw_text` (string) - original (orthographic) audio segment text
* `normalized_text` (string) - normalized audio segment transcription
* `gender` (string) - gender of speaker
* `speaker_id` (string) - id of speaker
* `is_gold_transcript` (bool) - ?
* `accent` (string) - type of accent, for example "en_lt", if applicable, else "None".
### Data Splits
All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home)
#### Initial Data Collection and Normalization
The VoxPopuli transcribed set comes from aligning the full-event source speech audio with the transcripts for plenary sessions. Official timestamps
are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture
of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps,
we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation.
Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available.
The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a
maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts.
The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data.
The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment.
We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER).
#### Who are the source language producers?
Speakers are participants of the European Parliament events, many of them are EU officials.
### 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
Gender speakers distribution is imbalanced, percentage of female speakers is mostly lower than 50% across languages, with the minimum of 15% for the Lithuanian language data.
VoxPopuli includes all available speeches from the 2009-2020 EP events without any selections on the topics or speakers.
The speech contents represent the standpoints of the speakers in the EP events, many of which are EU officials.
### Other Known Limitations
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is distributet under CC0 license, see also [European Parliament's legal notice](https://www.europarl.europa.eu/legal-notice/en/) for the raw data.
### Citation Information
Please cite this paper:
```bibtex
@inproceedings{wang-etal-2021-voxpopuli,
title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation",
author = "Wang, Changhan and
Riviere, Morgane and
Lee, Ann and
Wu, Anne and
Talnikar, Chaitanya and
Haziza, Daniel and
Williamson, Mary and
Pino, Juan and
Dupoux, Emmanuel",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.80",
pages = "993--1003",
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
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silicone | 2023-06-01T14:59:53.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"emotion-classification",
"dialogue-act-classification",
"arxiv:2009.11152",
"region:us"
] | null | The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection
of resources for training, evaluating, and analyzing natural language understanding systems
specifically designed for spoken language. All datasets are in the English language and cover a
variety of domains including daily life, scripted scenarios, joint task completion, phone call
conversations, and televsion dialogue. Some datasets additionally include emotion and/or sentimant
labels. | @inproceedings{chapuis-etal-2020-hierarchical,
title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
author = "Chapuis, Emile and
Colombo, Pierre and
Manica, Matteo and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
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.239",
doi = "10.18653/v1/2020.findings-emnlp.239",
pages = "2636--2648",
abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a
key component of spoken dialog systems. In this work, we propose a new approach to learn
generic representations adapted to spoken dialog, which we evaluate on a new benchmark we
call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE).
SILICONE is model-agnostic and contains 10 different datasets of various sizes.
We obtain our representations with a hierarchical encoder based on transformer architectures,
for which we extend two well-known pre-training objectives. Pre-training is performed on
OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We
demonstrate how hierarchical encoders achieve competitive results with consistently fewer
parameters compared to state-of-the-art models and we show their importance for both
pre-training and fine-tuning.",
} | 8 | 2,036 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
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size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
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task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- dialogue-modeling
- language-modeling
- masked-language-modeling
- sentiment-classification
- text-scoring
pretty_name: SILICONE Benchmark
tags:
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- dialogue-act-classification
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num_examples: 1478
download_size: 802002
dataset_size: 1118457
- config_name: sem
features:
- name: Utterance
dtype: string
- name: NbPairInSession
dtype: string
- name: Dialogue_ID
dtype: string
- name: SpeechTurn
dtype: string
- name: Speaker
dtype: string
- name: Sentiment
dtype: string
- name: Label
dtype:
class_label:
names:
'0': Negative
'1': Neutral
'2': Positive
- name: Idx
dtype: int32
splits:
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num_bytes: 496168
num_examples: 4264
- name: validation
num_bytes: 57896
num_examples: 485
- name: test
num_bytes: 100072
num_examples: 878
download_size: 513689
dataset_size: 654136
- config_name: swda
features:
- name: Utterance
dtype: string
- name: Dialogue_Act
dtype: string
- name: From_Caller
dtype: string
- name: To_Caller
dtype: string
- name: Topic
dtype: string
- name: Dialogue_ID
dtype: string
- name: Conv_ID
dtype: string
- name: Label
dtype:
class_label:
names:
'0': sd
'1': b
'2': sv
'3': '%'
'4': aa
'5': ba
'6': fc
'7': qw
'8': nn
'9': bk
'10': h
'11': qy^d
'12': bh
'13': ^q
'14': bf
'15': fo_o_fw_"_by_bc
'16': fo_o_fw_by_bc_"
'17': na
'18': ad
'19': ^2
'20': b^m
'21': qo
'22': qh
'23': ^h
'24': ar
'25': ng
'26': br
'27': 'no'
'28': fp
'29': qrr
'30': arp_nd
'31': t3
'32': oo_co_cc
'33': aap_am
'34': t1
'35': bd
'36': ^g
'37': qw^d
'38': fa
'39': ft
'40': +
'41': x
'42': ny
'43': sv_fx
'44': qy_qr
'45': ba_fe
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dtype: int32
splits:
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num_examples: 190709
- name: validation
num_bytes: 2265898
num_examples: 21203
- name: test
num_bytes: 291471
num_examples: 2714
download_size: 16227500
dataset_size: 23057157
config_names:
- dyda_da
- dyda_e
- iemocap
- maptask
- meld_e
- meld_s
- mrda
- oasis
- sem
- swda
---
# Dataset Card for SILICONE Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [N/A]
- **Repository:** https://github.com/eusip/SILICONE-benchmark
- **Paper:** https://arxiv.org/abs/2009.11152
- **Leaderboard:** [N/A]
- **Point of Contact:** [Ebenge Usip](ebenge.usip@telecom-paris.fr)
### Dataset Summary
The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. All datasets are in the English language and covers a variety of domains including daily life, scripted scenarios, joint task completion, phone call conversations, and televsion dialogue. Some datasets additionally include emotion and/or sentimant labels.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
#### DailyDialog Act Corpus (Dialogue Act)
For the `dyda_da` configuration one example from the dataset is:
```
{
'Utterance': "the taxi drivers are on strike again .",
'Dialogue_Act': 2, # "inform"
'Dialogue_ID': "2"
}
```
#### DailyDialog Act Corpus (Emotion)
For the `dyda_e` configuration one example from the dataset is:
```
{
'Utterance': "'oh , breaktime flies .'",
'Emotion': 5, # "sadness"
'Dialogue_ID': "997"
}
```
#### Interactive Emotional Dyadic Motion Capture (IEMOCAP) database
For the `iemocap` configuration one example from the dataset is:
```
{
'Dialogue_ID': "Ses04F_script03_2",
'Utterance_ID': "Ses04F_script03_2_F025",
'Utterance': "You're quite insufferable. I expect it's because you're drunk.",
'Emotion': 0, # "ang"
}
```
#### HCRC MapTask Corpus
For the `maptask` configuration one example from the dataset is:
```
{
'Speaker': "f",
'Utterance': "i think that would bring me over the crevasse",
'Dialogue_Act': 4, # "explain"
}
```
#### Multimodal EmotionLines Dataset (Emotion)
For the `meld_e` configuration one example from the dataset is:
```
{
'Utterance': "'Push 'em out , push 'em out , harder , harder .'",
'Speaker': "Joey",
'Emotion': 3, # "joy"
'Dialogue_ID': "1",
'Utterance_ID': "2"
}
```
#### Multimodal EmotionLines Dataset (Sentiment)
For the `meld_s` configuration one example from the dataset is:
```
{
'Utterance': "'Okay , y'know what ? There is no more left , left !'",
'Speaker': "Rachel",
'Sentiment': 0, # "negative"
'Dialogue_ID': "2",
'Utterance_ID': "4"
}
```
#### ICSI MRDA Corpus
For the `mrda` configuration one example from the dataset is:
```
{
'Utterance_ID': "Bed006-c2_0073656_0076706",
'Dialogue_Act': 0, # "s"
'Channel_ID': "Bed006-c2",
'Speaker': "mn015",
'Dialogue_ID': "Bed006",
'Utterance': "keith is not technically one of us yet ."
}
```
#### BT OASIS Corpus
For the `oasis` configuration one example from the dataset is:
```
{
'Speaker': "b",
'Utterance': "when i rang up um when i rang to find out why she said oh well your card's been declined",
'Dialogue_Act': 21, # "inform"
}
```
#### SEMAINE database
For the `sem` configuration one example from the dataset is:
```
{
'Utterance': "can you think of somebody who is like that ?",
'NbPairInSession': "11",
'Dialogue_ID': "59",
'SpeechTurn': "674",
'Speaker': "Agent",
'Sentiment': 1, # "Neutral"
}
```
#### Switchboard Dialog Act (SwDA) Corpus
For the `swda` configuration one example from the dataset is:
```
{
'Utterance': "but i 'd probably say that 's roughly right .",
'Dialogue_Act': 33, # "aap_am"
'From_Caller': "1255",
'To_Caller': "1087",
'Topic': "CRIME",
'Dialogue_ID': "818",
'Conv_ID': "sw2836",
}
```
### Data Fields
For the `dyda_da` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "commissive" (0), "directive" (1), "inform" (2) or "question" (3).
- `Dialogue_ID`: identifier of the dialogue as a string.
For the `dyda_e` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "happiness" (3), "no emotion" (4), "sadness" (5) or "surprise" (6).
- `Dialogue_ID`: identifier of the dialogue as a string.
For the `iemocap` configuration, the different fields are:
- `Dialogue_ID`: identifier of the dialogue as a string.
- `Utterance_ID`: identifier of the utterance as a string.
- `Utterance`: Utterance as a string.
- `Emotion`: Emotion label of the utterance. It can be one of "Anger" (0), "Disgust" (1), "Excitement" (2), "Fear" (3), "Frustration" (4), "Happiness" (5), "Neutral" (6), "Other" (7), "Sadness" (8), "Surprise" (9) or "Unknown" (10).
For the `maptask` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "acknowledge" (0), "align" (1), "check" (2), "clarify" (3), "explain" (4), "instruct" (5), "query_w" (6), "query_yn" (7), "ready" (8), "reply_n" (9), "reply_w" (10) or "reply_y" (11).
For the `meld_e` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Speaker`: Speaker as a string.
- `Emotion`: Emotion label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "joy" (3), "neutral" (4), "sadness" (5) or "surprise" (6).
- `Dialogue_ID`: identifier of the dialogue as a string.
- `Utterance_ID`: identifier of the utterance as a string.
For the `meld_s` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Speaker`: Speaker as a string.
- `Sentiment`: Sentiment label of the utterance. It can be one of "negative" (0), "neutral" (1) or "positive" (2).
- `Dialogue_ID`: identifier of the dialogue as a string.
- `Utterance_ID`: identifier of the utterance as a string.
For the `mrda` configuration, the different fields are:
- `Utterance_ID`: identifier of the utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "s" (0) [Statement/Subjective Statement], "d" (1) [Declarative Question], "b" (2) [Backchannel], "f" (3) [Follow-me] or "q" (4) [Question].
- `Channel_ID`: identifier of the channel as a string.
- `Speaker`: identifier of the speaker as a string.
- `Dialogue_ID`: identifier of the channel as a string.
- `Utterance`: Utterance as a string.
For the `oasis` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "accept" (0), "ackn" (1), "answ" (2), "answElab" (3), "appreciate" (4), "backch" (5), "bye" (6), "complete" (7), "confirm" (8), "correct" (9), "direct" (10), "directElab" (11), "echo" (12), "exclaim" (13), "expressOpinion"(14), "expressPossibility" (15), "expressRegret" (16), "expressWish" (17), "greet" (18), "hold" (19),
"identifySelf" (20), "inform" (21), "informCont" (22), "informDisc" (23), "informIntent" (24), "init" (25), "negate" (26), "offer" (27), "pardon" (28), "raiseIssue" (29), "refer" (30), "refuse" (31), "reqDirect" (32), "reqInfo" (33), "reqModal" (34), "selfTalk" (35), "suggest" (36), "thank" (37), "informIntent-hold" (38), "correctSelf" (39), "expressRegret-inform" (40) or "thank-identifySelf" (41).
For the `sem` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `NbPairInSession`: number of utterance pairs in a dialogue.
- `Dialogue_ID`: identifier of the dialogue as a string.
- `SpeechTurn`: SpeakerTurn as a string.
- `Speaker`: Speaker as a string.
- `Sentiment`: Sentiment label of the utterance. It can be "Negative", "Neutral" or "Positive".
For the `swda` configuration, the different fields are:
`Utterance`: Utterance as a string.
`Dialogue_Act`: Dialogue act label of the utterance. It can be "sd" (0) [Statement-non-opinion], "b" (1) [Acknowledge (Backchannel)], "sv" (2) [Statement-opinion], "%" (3) [Uninterpretable], "aa" (4) [Agree/Accept], "ba" (5) [Appreciation], "fc" (6) [Conventional-closing], "qw" (7) [Wh-Question], "nn" (8) [No Answers], "bk" (9) [Response Acknowledgement], "h" (10) [Hedge], "qy^d" (11) [Declarative Yes-No-Question], "bh" (12) [Backchannel in Question Form], "^q" (13) [Quotation], "bf" (14) [Summarize/Reformulate], 'fo_o_fw_"_by_bc' (15) [Other], 'fo_o_fw_by_bc_"' (16) [Other], "na" (17) [Affirmative Non-yes Answers], "ad" (18) [Action-directive], "^2" (19) [Collaborative Completion], "b^m" (20) [Repeat-phrase], "qo" (21) [Open-Question], "qh" (22) [Rhetorical-Question], "^h" (23) [Hold Before Answer/Agreement], "ar" (24) [Reject], "ng" (25) [Negative Non-no Answers], "br" (26) [Signal-non-understanding], "no" (27) [Other Answers], "fp" (28) [Conventional-opening], "qrr" (29) [Or-Clause], "arp_nd" (30) [Dispreferred Answers], "t3" (31) [3rd-party-talk], "oo_co_cc" (32) [Offers, Options Commits], "aap_am" (33) [Maybe/Accept-part], "t1" (34) [Downplayer], "bd" (35) [Self-talk], "^g" (36) [Tag-Question], "qw^d" (37) [Declarative Wh-Question], "fa" (38) [Apology], "ft" (39) [Thanking], "+" (40) [Unknown], "x" (41) [Unknown], "ny" (42) [Unknown], "sv_fx" (43) [Unknown], "qy_qr" (44) [Unknown] or "ba_fe" (45) [Unknown].
`From_Caller`: identifier of the from caller as a string.
`To_Caller`: identifier of the to caller as a string.
`Topic`: Topic as a string.
`Dialogue_ID`: identifier of the dialogue as a string.
`Conv_ID`: identifier of the conversation as a string.
### Data Splits
| Dataset name | Train | Valid | Test |
| ------------ | ----- | ----- | ---- |
| dyda_da | 87170 | 8069 | 7740 |
| dyda_e | 87170 | 8069 | 7740 |
| iemocap | 7213 | 805 | 2021 |
| maptask | 20905 | 2963 | 2894 |
| meld_e | 9989 | 1109 | 2610 |
| meld_s | 9989 | 1109 | 2610 |
| mrda | 83944 | 9815 | 15470 |
| oasis | 12076 | 1513 | 1478 |
| sem | 4264 | 485 | 878 |
| swda | 190709 | 21203 | 2714 |
## 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
### Benchmark Curators
Emile Chapuis, Pierre Colombo, Ebenge Usip.
### Licensing Information
This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@inproceedings{chapuis-etal-2020-hierarchical,
title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
author = "Chapuis, Emile and
Colombo, Pierre and
Manica, Matteo and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
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.239",
doi = "10.18653/v1/2020.findings-emnlp.239",
pages = "2636--2648",
abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.",
}
```
### Contributions
Thanks to [@eusip](https://github.com/eusip) and [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 22,988 | [
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e2e_nlg | 2022-11-18T19:59:40.000Z | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"meaning-representation-to-text",
"arxiv:1706.09254",
"arxiv:1901.11528",
"region:us"
] | null | The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena;
(2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances.
E2E is released in the following paper where you can find more details and baseline results:
https://arxiv.org/abs/1706.09254 | @article{dusek.etal2020:csl,
title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
year = {2020},
month = jan,
volume = {59},
pages = {123--156},
doi = {10.1016/j.csl.2019.06.009},
archivePrefix = {arXiv},
eprint = {1901.11528},
eprinttype = {arxiv},
journal = {Computer Speech & Language}
} | 10 | 2,034 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: e2e
pretty_name: End-to-End NLG Challenge
tags:
- meaning-representation-to-text
dataset_info:
features:
- name: meaning_representation
dtype: string
- name: human_reference
dtype: string
splits:
- name: train
num_bytes: 9435824
num_examples: 42061
- name: validation
num_bytes: 1171723
num_examples: 4672
- name: test
num_bytes: 1320205
num_examples: 4693
download_size: 11812316
dataset_size: 11927752
---
# Dataset Card for End-to-End NLG Challenge
## 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:** [homepage](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
- **Repository:** [repository](https://github.com/tuetschek/e2e-dataset/)
- **Paper:** [paper](https://arxiv.org/abs/1706.09254)
- **Leaderboard:** [leaderboard](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
### Dataset Summary
The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena;
(2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances.
E2E is released in the following paper where you can find more details and baseline results:
https://arxiv.org/abs/1706.09254
### Supported Tasks and Leaderboards
- `text2text-generation-other-meaning-representation-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider). The TGen model (Dusek and Jurcıcek, 2016a) was used a baseline, had the following scores:
| | BLEU | NIST | METEOR | ROUGE_L | CIDEr |
| -------- | ------ | ------ | ------ | ------- | ------ |
| BASELINE | 0.6593 | 8.6094 | 0.4483 | 0.6850 | 2.2338 |
This task has an inactive leaderboard which can be found [here](http://www.macs.hw.ac.uk/InteractionLab/E2E/) and ranks models based on the metrics above.
### Languages
The dataset is in english (en).
## Dataset Structure
### Data Instances
Example of one instance:
```
{'human_reference': 'The Vaults pub near Café Adriatic has a 5 star rating. Prices start at £30.',
'meaning_representation': 'name[The Vaults], eatType[pub], priceRange[more than £30], customer rating[5 out of 5], near[Café Adriatic]'}
```
### Data Fields
- `human_reference`: string, the text is natural language that describes the different characteristics in the meaning representation
- `meaning_representation`: list of slots and values to generate a description from
Each MR consists of 3–8 attributes (slots), such as name, food or area, and their values.
### Data Splits
The dataset is split into training, validation and testing sets (in a 76.5-8.5-15 ratio), keeping a similar distribution of MR and reference text lengths and ensuring that MRs in different sets are distinct.
| | train | validation | test |
| ----- |-------:|------------:|------:|
| N. Instances | 42061 | 4672 | 4693 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
The data was collected using the CrowdFlower platform and quality-controlled following Novikova et al. (2016).
#### Who are the source language producers?
[More Information Needed]
### Annotations
Following Novikova et al. (2016), the E2E data was collected using pictures as stimuli, which was shown to elicit significantly more natural, more informative, and better phrased human references than textual MRs.
#### 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
```
@article{dusek.etal2020:csl,
title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
year = {2020},
month = jan,
volume = {59},
pages = {123--156},
doi = {10.1016/j.csl.2019.06.009},
archivePrefix = {arXiv},
eprint = {1901.11528},
eprinttype = {arxiv},
journal = {Computer Speech \& Language}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 6,719 | [
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sem_eval_2018_task_1 | 2022-11-18T21:45:06.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"language:en",
"language:es",
"license:unknown",
"emotion-classification",
"region:us"
] | null | SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification.
This is a dataset for multilabel emotion classification for tweets.
'Given a tweet, classify it as 'neutral or no emotion' or as one, or more, of eleven given emotions that best represent the mental state of the tweeter.'
It contains 22467 tweets in three languages manually annotated by crowdworkers using Best–Worst Scaling. | @InProceedings{SemEval2018Task1,
author = {Mohammad, Saif M. and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
title = {SemEval-2018 {T}ask 1: {A}ffect in Tweets},
booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)},
address = {New Orleans, LA, USA},
year = {2018}} | 9 | 2,020 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
- en
- es
license:
- unknown
multilinguality:
- multilingual
pretty_name: 'SemEval-2018 Task 1: Affect in Tweets'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
tags:
- emotion-classification
dataset_info:
- config_name: subtask5.english
features:
- name: ID
dtype: string
- name: Tweet
dtype: string
- name: anger
dtype: bool
- name: anticipation
dtype: bool
- name: disgust
dtype: bool
- name: fear
dtype: bool
- name: joy
dtype: bool
- name: love
dtype: bool
- name: optimism
dtype: bool
- name: pessimism
dtype: bool
- name: sadness
dtype: bool
- name: surprise
dtype: bool
- name: trust
dtype: bool
splits:
- name: train
num_bytes: 809768
num_examples: 6838
- name: test
num_bytes: 384519
num_examples: 3259
- name: validation
num_bytes: 104660
num_examples: 886
download_size: 5975590
dataset_size: 1298947
- config_name: subtask5.spanish
features:
- name: ID
dtype: string
- name: Tweet
dtype: string
- name: anger
dtype: bool
- name: anticipation
dtype: bool
- name: disgust
dtype: bool
- name: fear
dtype: bool
- name: joy
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- name: love
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- name: optimism
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- name: pessimism
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- name: surprise
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splits:
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num_bytes: 362549
num_examples: 3561
- name: test
num_bytes: 288692
num_examples: 2854
- name: validation
num_bytes: 67259
num_examples: 679
download_size: 5975590
dataset_size: 718500
- config_name: subtask5.arabic
features:
- name: ID
dtype: string
- name: Tweet
dtype: string
- name: anger
dtype: bool
- name: anticipation
dtype: bool
- name: disgust
dtype: bool
- name: fear
dtype: bool
- name: joy
dtype: bool
- name: love
dtype: bool
- name: optimism
dtype: bool
- name: pessimism
dtype: bool
- name: sadness
dtype: bool
- name: surprise
dtype: bool
- name: trust
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splits:
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num_bytes: 414458
num_examples: 2278
- name: test
num_bytes: 278715
num_examples: 1518
- name: validation
num_bytes: 105452
num_examples: 585
download_size: 5975590
dataset_size: 798625
---
# Dataset Card for SemEval-2018 Task 1: Affect in Tweets
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://competitions.codalab.org/competitions/17751
- **Repository:**
- **Paper:** http://saifmohammad.com/WebDocs/semeval2018-task1.pdf
- **Leaderboard:**
- **Point of Contact:** https://www.saifmohammad.com/
### Dataset Summary
Tasks: We present an array of tasks where systems have to automatically determine the intensity of emotions (E) and intensity of sentiment (aka valence V) of the tweeters from their tweets. (The term tweeter refers to the person who has posted the tweet.) We also include a multi-label emotion classification task for tweets. For each task, we provide separate training and test datasets for English, Arabic, and Spanish tweets. The individual tasks are described below:
1. EI-reg (an emotion intensity regression task): Given a tweet and an emotion E, determine the intensity of E that best represents the mental state of the tweeter—a real-valued score between 0 (least E) and 1 (most E).
Separate datasets are provided for anger, fear, joy, and sadness.
2. EI-oc (an emotion intensity ordinal classification task): Given a tweet and an emotion E, classify the tweet into one of four ordinal classes of intensity of E that best represents the mental state of the tweeter.
Separate datasets are provided for anger, fear, joy, and sadness.
3. V-reg (a sentiment intensity regression task): Given a tweet, determine the intensity of sentiment or valence (V) that best represents the mental state of the tweeter—a real-valued score between 0 (most negative) and 1 (most positive).
4. V-oc (a sentiment analysis, ordinal classification, task): Given a tweet, classify it into one of seven ordinal classes, corresponding to various levels of positive and negative sentiment intensity, that best represents the mental state of the tweeter.
5. E-c (an emotion classification task): Given a tweet, classify it as 'neutral or no emotion' or as one, or more, of eleven given emotions that best represent the mental state of the tweeter.
Here, E refers to emotion, EI refers to emotion intensity, V refers to valence or sentiment intensity, reg refers to regression, oc refers to ordinal classification, c refers to classification.
Together, these tasks encompass various emotion and sentiment analysis tasks. You are free to participate in any number of tasks and on any of the datasets.
**Currently only the subtask 5 (E-c) is available on the Hugging Face Dataset Hub.**
### Supported Tasks and Leaderboards
### Languages
English, Arabic and Spanish
## Dataset Structure
### Data Instances
An example from the `subtask5.english` config is:
```
{'ID': '2017-En-21441',
'Tweet': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer. #motivation #leadership #worry",
'anger': False,
'anticipation': True,
'disgust': False,
'fear': False,
'joy': False,
'love': False,
'optimism': True,
'pessimism': False,
'sadness': False,
'surprise': False,
'trust': True}
```
### Data Fields
For any config of the subtask 5:
- ID: string id of the tweet
- Tweet: text content of the tweet as a string
- anger: boolean, True if anger represents the mental state of the tweeter
- anticipation: boolean, True if anticipation represents the mental state of the tweeter
- disgust: boolean, True if disgust represents the mental state of the tweeter
- fear: boolean, True if fear represents the mental state of the tweeter
- joy: boolean, True if joy represents the mental state of the tweeter
- love: boolean, True if love represents the mental state of the tweeter
- optimism: boolean, True if optimism represents the mental state of the tweeter
- pessimism: boolean, True if pessimism represents the mental state of the tweeter
- sadness: boolean, True if sadness represents the mental state of the tweeter
- surprise: boolean, True if surprise represents the mental state of the tweeter
- trust: boolean, True if trust represents the mental state of the tweeter
Note that the test set has no labels, and therefore all labels are set to False.
### Data Splits
| | train | validation | test |
|---------|------:|-----------:|------:|
| English | 6,838 | 886 | 3,259 |
| Arabic | 2,278 | 585 | 1,518 |
| Spanish | 3,561 | 679 | 2,854 |
## Dataset Creation
### Curation Rationale
### Source Data
Tweets
#### Initial Data Collection and Normalization
#### Who are the source language producers?
Twitter users.
### Annotations
#### Annotation process
We presented one tweet at a time to the annotators
and asked which of the following options best de-
scribed the emotional state of the tweeter:
– anger (also includes annoyance, rage)
– anticipation (also includes interest, vigilance)
– disgust (also includes disinterest, dislike, loathing)
– fear (also includes apprehension, anxiety, terror)
– joy (also includes serenity, ecstasy)
– love (also includes affection)
– optimism (also includes hopefulness, confidence)
– pessimism (also includes cynicism, no confidence)
– sadness (also includes pensiveness, grief)
– surprise (also includes distraction, amazement)
– trust (also includes acceptance, liking, admiration)
– neutral or no emotion
Example tweets were provided in advance with ex-
amples of suitable responses.
On the Figure Eight task settings, we specified
that we needed annotations from seven people for
each tweet. However, because of the way the gold
tweets were set up, they were annotated by more
than seven people. The median number of anno-
tations was still seven. In total, 303 people anno-
tated between 10 and 4,670 tweets each. A total of
174,356 responses were obtained.
Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). SemEval-2018 task 1: Affect in tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, 1–17. https://doi.org/10.18653/v1/S18-1001
#### Who are the annotators?
Crowdworkers on Figure Eight.
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
Saif M. Mohammad, Felipe Bravo-Marquez, Mohammad Salameh and Svetlana Kiritchenko
### Licensing Information
See the official [Terms and Conditions](https://competitions.codalab.org/competitions/17751#learn_the_details-terms_and_conditions)
### Citation Information
@InProceedings{SemEval2018Task1,
author = {Mohammad, Saif M. and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
title = {SemEval-2018 {T}ask 1: {A}ffect in Tweets},
booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)},
address = {New Orleans, LA, USA},
year = {2018}}
### Contributions
Thanks to [@maxpel](https://github.com/maxpel) for adding this dataset. | 10,534 | [
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drop | 2023-04-05T10:05:02.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:extractive-qa",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | null | DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or
sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was
necessary for prior datasets. | @inproceedings{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
} | 10 | 2,015 | 2022-03-02T23:29:22 | ---
pretty_name: DROP
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text2text-generation
task_ids:
- extractive-qa
- abstractive-qa
paperswithcode_id: drop
dataset_info:
features:
- name: section_id
dtype: string
- name: query_id
dtype: string
- name: passage
dtype: string
- name: question
dtype: string
- name: answers_spans
sequence:
- name: spans
dtype: string
- name: types
dtype: string
splits:
- name: train
num_bytes: 105572762
num_examples: 77400
- name: validation
num_bytes: 11737787
num_examples: 9535
download_size: 8308692
dataset_size: 117310549
---
# Dataset Card for "drop"
## 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://allennlp.org/drop](https://allennlp.org/drop)
- **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:** 8.30 MB
- **Size of the generated dataset:** 110.91 MB
- **Total amount of disk used:** 119.21 MB
### Dataset Summary
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or
sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was
necessary for prior datasets.
### 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:** 8.30 MB
- **Size of the generated dataset:** 110.91 MB
- **Total amount of disk used:** 119.21 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers_spans": {
"spans": ["Chaz Schilens"]
},
"passage": "\" Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oak...",
"question": "Who scored the first touchdown of the game?"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `passage`: a `string` feature.
- `question`: a `string` feature.
- `answers_spans`: a dictionary feature containing:
- `spans`: a `string` feature.
### Data Splits
| name |train|validation|
|-------|----:|---------:|
|default|77409| 9536|
## 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{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={ {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. | 6,853 | [
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tiny_shakespeare | 2023-04-05T13:42:24.000Z | [
"region:us"
] | null | 40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
To use for e.g. character modelling:
```
d = datasets.load_dataset(name='tiny_shakespeare')['train']
d = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8'))
# train split includes vocabulary for other splits
vocabulary = sorted(set(next(iter(d)).numpy()))
d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})
d = d.unbatch()
seq_len = 100
batch_size = 2
d = d.batch(seq_len)
d = d.batch(batch_size)
``` | @misc{
author={Karpathy, Andrej},
title={char-rnn},
year={2015},
howpublished={\\url{https://github.com/karpathy/char-rnn}}
} | 17 | 2,015 | 2022-03-02T23:29:22 | ---
paperswithcode_id: null
pretty_name: TinyShakespeare
dataset_info:
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 55780
num_examples: 1
- name: train
num_bytes: 1003864
num_examples: 1
- name: validation
num_bytes: 55780
num_examples: 1
download_size: 1115394
dataset_size: 1115424
---
# Dataset Card for "tiny_shakespeare"
## 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/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt](https://github.com/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt)
- **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:** 1.11 MB
- **Size of the generated dataset:** 1.11 MB
- **Total amount of disk used:** 2.23 MB
### Dataset Summary
40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
To use for e.g. character modelling:
```
d = datasets.load_dataset(name='tiny_shakespeare')['train']
d = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8'))
# train split includes vocabulary for other splits
vocabulary = sorted(set(next(iter(d)).numpy()))
d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})
d = d.unbatch()
seq_len = 100
batch_size = 2
d = d.batch(seq_len)
d = d.batch(batch_size)
```
### 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:** 1.11 MB
- **Size of the generated dataset:** 1.11 MB
- **Total amount of disk used:** 2.23 MB
An example of 'train' looks as follows.
```
{
"text": "First Citizen:\nBefore we proceed any further, hear me "
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `text`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 1| 1| 1|
## 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
```
@misc{
author={Karpathy, Andrej},
title={char-rnn},
year={2015},
howpublished={\url{https://github.com/karpathy/char-rnn}}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | 6,101 | [
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] |
C-MTEB/T2Retrieval | 2023-07-28T10:11:06.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 2,011 | 2023-07-28T10:08:40 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 265607316
num_examples: 118605
- name: queries
num_bytes: 1000130
num_examples: 22812
download_size: 157606535
dataset_size: 266607446
---
# Dataset Card for "T2Retrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 590 | [
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clips/mfaq | 2022-10-20T11:32:50.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:cs",
"language:da",
"language:de",
"language:en",
"language:es",
"language:fi",
"language:fr",
"language:he",
"language:hr",
"language:hu",
"language:id",
"language:it",
"language:nl",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sv",
"language:tr",
"language:vi",
"license:cc0-1.0",
"arxiv:2109.12870",
"region:us"
] | clips | We present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages. | @InProceedings{mfaq_a_multilingual_dataset,
title={MFAQ: a Multilingual FAQ Dataset},
author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
year={2021},
booktitle={MRQA @ EMNLP 2021}
} | 26 | 2,010 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- cs
- da
- de
- en
- es
- fi
- fr
- he
- hr
- hu
- id
- it
- nl
- 'no'
- pl
- pt
- ro
- ru
- sv
- tr
- vi
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: MFAQ - a Multilingual FAQ Dataset
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
---
# MFAQ
🚨 See [MQA](https://huggingface.co/datasets/clips/mqa) or [MFAQ Light](maximedb/mfaq_light) for an updated version of the dataset.
MFAQ is a multilingual corpus of *Frequently Asked Questions* parsed from the [Common Crawl](https://commoncrawl.org/).
```
from datasets import load_dataset
load_dataset("clips/mfaq", "en")
{
"qa_pairs": [
{
"question": "Do I need a rental Car in Cork?",
"answer": "If you plan on travelling outside of Cork City, for instance to Kinsale [...]"
},
...
]
}
```
## Languages
We collected around 6M pairs of questions and answers in 21 different languages. To download a language specific subset you need to specify the language key as configuration. See below for an example.
```
load_dataset("clips/mfaq", "en") # replace "en" by any language listed below
```
| Language | Key | Pairs | Pages |
|------------|-----|-----------|-----------|
| All | all | 6,346,693 | 1,035,649 |
| English | en | 3,719,484 | 608,796 |
| German | de | 829,098 | 111,618 |
| Spanish | es | 482,818 | 75,489 |
| French | fr | 351,458 | 56,317 |
| Italian | it | 155,296 | 24,562 |
| Dutch | nl | 150,819 | 32,574 |
| Portuguese | pt | 138,778 | 26,169 |
| Turkish | tr | 102,373 | 19,002 |
| Russian | ru | 91,771 | 22,643 |
| Polish | pl | 65,182 | 10,695 |
| Indonesian | id | 45,839 | 7,910 |
| Norwegian | no | 37,711 | 5,143 |
| Swedish | sv | 37,003 | 5,270 |
| Danish | da | 32,655 | 5,279 |
| Vietnamese | vi | 27,157 | 5,261 |
| Finnish | fi | 20,485 | 2,795 |
| Romanian | ro | 17,066 | 3,554 |
| Czech | cs | 16,675 | 2,568 |
| Hebrew | he | 11,212 | 1,921 |
| Hungarian | hu | 8,598 | 1,264 |
| Croatian | hr | 5,215 | 819 |
## Data Fields
#### Nested (per page - default)
The data is organized by page. Each page contains a list of questions and answers.
- **id**
- **language**
- **num_pairs**: the number of FAQs on the page
- **domain**: source web domain of the FAQs
- **qa_pairs**: a list of questions and answers
- **question**
- **answer**
- **language**
#### Flattened
The data is organized by pair (i.e. pages are flattened). You can access the flat version of any language by appending `_flat` to the configuration (e.g. `en_flat`). The data will be returned pair-by-pair instead of page-by-page.
- **domain_id**
- **pair_id**
- **language**
- **domain**: source web domain of the FAQs
- **question**
- **answer**
## Source Data
This section was adapted from the source data description of [OSCAR](https://huggingface.co/datasets/oscar#source-data)
Common Crawl is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected nofollow and robots.txt policies.
To construct MFAQ, the WARC files of Common Crawl were used. We looked for `FAQPage` markup in the HTML and subsequently parsed the `FAQItem` from the page.
## People
This model was developed by [Maxime De Bruyn](https://www.linkedin.com/in/maximedebruyn/), Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.
## Licensing Information
```
These data are released under this licensing scheme.
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
```
## Citation information
```
@misc{debruyn2021mfaq,
title={MFAQ: a Multilingual FAQ Dataset},
author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
year={2021},
eprint={2109.12870},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 5,144 | [
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zeroshot/twitter-financial-news-sentiment | 2022-12-12T14:32:59.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"twitter",
"finance",
"markets",
"stocks",
"wallstreet",
"quant",
"hedgefunds",
"region:us"
] | zeroshot | null | null | 32 | 2,008 | 2022-09-01T21:21:56 | ---
annotations_creators:
- other
language:
- en
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: twitter financial news
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- twitter
- finance
- markets
- stocks
- wallstreet
- quant
- hedgefunds
- markets
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
Read this [BLOG](https://neuralmagic.com/blog/classifying-finance-tweets-in-real-time-with-sparse-transformers/) to see how I fine-tuned a sparse transformer on this dataset.
### Dataset Description
The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.
1. The dataset holds 11,932 documents annotated with 3 labels:
```python
sentiments = {
"LABEL_0": "Bearish",
"LABEL_1": "Bullish",
"LABEL_2": "Neutral"
}
```
The data was collected using the Twitter API. The current dataset supports the multi-class classification task.
### Task: Sentiment Analysis
# Data Splits
There are 2 splits: train and validation. Below are the statistics:
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 9,938 |
| Validation | 2,486 |
# Licensing Information
The Twitter Financial Dataset (sentiment) version 1.0.0 is released under the MIT License. | 1,566 | [
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jamescalam/llama-2-arxiv-papers-chunked | 2023-07-25T03:12:24.000Z | [
"language:en",
"arxiv:2307.09288",
"region:us"
] | jamescalam | null | null | 11 | 2,005 | 2023-07-25T03:06:58 | ---
language:
- en
pretty_name: Chunked Arxiv Papers for Llama 2
---
This dataset contains chunked extracts (of ~300 tokens) from papers related to (and including) the [Llama 2 research paper](https://arxiv.org/abs/2307.09288). Related papers were identified by following a trail of references, extracting those papers with the [`arxiv-bot`](https://github.com/aurelio-labs/arxiv-bot) package, and repeating. | 409 | [
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] |
BeIR/dbpedia-entity | 2022-10-23T06:03:56.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 3 | 2,001 | 2022-06-05T16:54:24 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
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TIGER-Lab/MathInstruct | 2023-10-16T13:57:57.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"math",
"arxiv:2309.05653",
"region:us"
] | TIGER-Lab | null | null | 94 | 1,983 | 2023-09-11T14:21:02 | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: MathInstruct
size_categories:
- 100K<n<1M
tags:
- math
---
# 🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields.
Project Page: [https://tiger-ai-lab.github.io/MAmmoTH/](https://tiger-ai-lab.github.io/MAmmoTH/)
Paper: [https://arxiv.org/pdf/2309.05653.pdf](https://arxiv.org/pdf/2309.05653.pdf)
Code: [https://github.com/TIGER-AI-Lab/MAmmoTH](https://github.com/TIGER-AI-Lab/MAmmoTH)
Models:
| | **Base Model: Llama-2** | **Base Model: Code Llama** |
|-----|---------------------------------------------------------------|--------------------------------------------------------------------------|
| 7B | 🦣 [MAmmoTH-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B) | 🦣 [MAmmoTH-Coder-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B) |
| 13B | 🦣 [MAmmoTH-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-13B) | 🦣 [MAmmoTH-Coder-13B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-13B)|
| 34B | - | 🦣 [MAmmoTH-Coder-34B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-34B)|
| 70B | 🦣 [MAmmoTH-70B](https://huggingface.co/TIGER-Lab/MAmmoTH-70B) | - |
## **License**
Please check out the license of each subset in our curated dataset MathInstruct.
| Dataset Name | License Type |
|--------------|----------------|
| GSM8K | MIT |
| GSM8K-RFT | Non listed |
| AQuA-RAT | Apache 2.0 |
| MATH | MIT |
| TheoremQA | MIT |
| Camel-Math | Attribution-NonCommercial 4.0 International |
| NumGLUE | Apache-2.0 |
| CrowdSourced (Lila) | Attribution 4.0 International |
| MathQA | Apache-2.0 |
| Our Curated | MIT |
## **Citation**
Please cite our paper if you use our data, model or code. Please also kindly cite the original dataset papers.
```
@article{yue2023mammoth,
title={MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning},
author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, Wenhu Chen},
journal={arXiv preprint arXiv:2309.05653},
year={2023}
}
``` | 2,756 | [
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mkb | 2023-06-01T14:59:56.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"multilinguality:translation",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:bn",
"language:en",
"language:gu",
"language:hi",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"language:ur",
"license:cc-by-4.0",
"arxiv:2007.07691",
"region:us"
] | null | The Prime Minister's speeches - Mann Ki Baat, on All India Radio, translated into many languages. | @misc{siripragada2020multilingual,
title={A Multilingual Parallel Corpora Collection Effort for Indian Languages},
author={Shashank Siripragada and Jerin Philip and Vinay P. Namboodiri and C V Jawahar},
year={2020},
eprint={2007.07691},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 1 | 1,981 | 2022-03-02T23:29:22 | ---
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task_ids:
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language:
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annotations_creators:
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source_datasets:
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size_categories:
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license:
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paperswithcode_id: null
pretty_name: CVIT MKB
dataset_info:
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dataset_size: 2318080
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features:
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dtype:
translation:
languages:
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splits:
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num_bytes: 1243583
num_examples: 2491
download_size: 52428800
dataset_size: 1243583
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features:
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dtype:
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splits:
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num_bytes: 1906073
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download_size: 52428800
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- config_name: en-mr
features:
- name: translation
dtype:
translation:
languages:
- en
- mr
splits:
- name: train
num_bytes: 2140298
num_examples: 5867
download_size: 52428800
dataset_size: 2140298
config_names:
- bn-en
- bn-gu
- bn-hi
- bn-ml
- bn-mr
- bn-or
- bn-ta
- bn-te
- bn-ur
- en-gu
- en-hi
- en-ml
- en-mr
- en-or
- en-ta
- en-te
- en-ur
- gu-hi
- gu-ml
- gu-mr
- gu-or
- gu-ta
- gu-te
- gu-ur
- hi-ml
- hi-mr
- hi-or
- hi-ta
- hi-te
- hi-ur
- ml-mr
- ml-or
- ml-ta
- ml-te
- ml-ur
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- mr-ta
- mr-te
- mr-ur
- or-ta
- or-te
- or-ur
- ta-te
- ta-ur
- te-ur
---
# Dataset Card for CVIT MKB
## 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:** [Link](http://preon.iiit.ac.in/~jerin/bhasha/)
- **Repository:**
- **Paper:** [ARXIV](https://arxiv.org/abs/2007.07691)
- **Leaderboard:**
- **Point of Contact:** [email](cvit-bhasha@googlegroups.com)
### Dataset Summary
Indian Prime Minister's speeches - Mann Ki Baat, on All India Radio, translated into many languages.
### Supported Tasks and Leaderboards
[MORE INFORMATION NEEDED]
### Languages
Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English
## Dataset Structure
### Data Instances
[MORE INFORMATION NEEDED]
### Data Fields
- `src_tag`: `string` text in source language
- `tgt_tag`: `string` translation of source language in target language
### 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
#### Annotation process
[MORE INFORMATION NEEDED]
#### Who are the annotators?
[MORE INFORMATION NEEDED]
### Personal and Sensitive Information
[MORE INFORMATION NEEDED]
## Considerations for Using the Data
### Social Impact of Dataset
[MORE INFORMATION NEEDED]
### Discussion of Biases
[MORE INFORMATION NEEDED]
### Other Known Limitations
[MORE INFORMATION NEEDED]
## Additional Information
### Dataset Curators
[MORE INFORMATION NEEDED]
### Licensing Information
The datasets and pretrained models provided here are licensed under Creative Commons Attribution-ShareAlike 4.0 International License.
### Citation Information
```
@misc{siripragada2020multilingual,
title={A Multilingual Parallel Corpora Collection Effort for Indian Languages},
author={Shashank Siripragada and Jerin Philip and Vinay P. Namboodiri and C V Jawahar},
year={2020},
eprint={2007.07691},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset. | 15,282 | [
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setimes | 2022-11-03T16:47:00.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:bg",
"language:bs",
"language:el",
"language:en",
"language:hr",
"language:mk",
"language:ro",
"language:sq",
"language:sr",
"language:tr",
"license:cc-by-sa-4.0",
"region:us"
] | null | SETimes – A Parallel Corpus of English and South-East European Languages
The corpus is based on the content published on the SETimes.com news portal. The news portal publishes “news and views from Southeast Europe” in ten languages: Bulgarian, Bosnian, Greek, English, Croatian, Macedonian, Romanian, Albanian and Serbian. This version of the corpus tries to solve the issues present in an older version of the corpus (published inside OPUS, described in the LREC 2010 paper by Francis M. Tyers and Murat Serdar Alperen). The following procedures were applied to resolve existing issues:
- stricter extraction process – no HTML residues present
- language identification on every non-English document – non-English online documents contain English material in case the article was not translated into that language
- resolving encoding issues in Croatian and Serbian – diacritics were partially lost due to encoding errors – text was rediacritized. | null | 0 | 1,959 | 2022-03-02T23:29:22 | ---
pretty_name: SETimes – A Parallel Corpus of English and South-East European Languages
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---
# Dataset Card for SETimes – A Parallel Corpus of English and South-East European Languages
## 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://nlp.ffzg.hr/resources/corpora/setimes/
- **Repository:** None
- **Paper:** None
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### 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. | 16,009 | [
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] |
AdaptLLM/finance-tasks | 2023-10-21T11:45:45.000Z | [
"arxiv:2309.09530",
"region:us"
] | AdaptLLM | null | null | 5 | 1,953 | 2023-09-19T03:17:07 | ---
configs:
- config_name: ConvFinQA
data_files:
- split: test
path: "ConviFinQA/test.json"
- config_name: FiQA_SA
data_files:
- split: test
path: "FiQA_SA/test.json"
- config_name: FPB
data_files:
- split: test
path: "FPB/test.json"
- config_name: Headline
data_files:
- split: test
path: "Headline/test.json"
- config_name: NER
data_files:
- split: test
path: "NER/test.json"
---
# Adapting Large Language Models via Reading Comprehension
This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530)
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in **biomedicine, finance, and law domains**. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
## GitHub repo:
https://github.com/microsoft/LMOps
## Domain-specific LLMs:
Our models of different domains are now available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="./comparison.png" width="700">
</p>
## Domain-specific Tasks:
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
## Citation:
```bibtex
@inproceedings{AdaptLLM,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
url={https://arxiv.org/abs/2309.09530},
year={2023},
}
```
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adv_glue | 2023-06-01T14:57:45.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:sentiment-classification",
"annotations_creators:other",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|glue",
"language:en",
"license:cc-by-sa-4.0",
"paraphrase-identification",
"qa-nli",
"arxiv:2111.02840",
"region:us"
] | null | Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark
that focuses on the adversarial robustness evaluation of language models. It covers five
natural language understanding tasks from the famous GLUE tasks and is an adversarial
version of GLUE benchmark. | @article{Wang2021AdversarialGA,
title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},
author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},
journal={ArXiv},
year={2021},
volume={abs/2111.02840}
} | 4 | 1,947 | 2022-03-28T11:12:33 | ---
annotations_creators:
- other
language_creators:
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- natural-language-inference
- sentiment-classification
pretty_name: Adversarial GLUE
tags:
- paraphrase-identification
- qa-nli
dataset_info:
- config_name: adv_sst2
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 16595
num_examples: 148
download_size: 40662
dataset_size: 16595
- config_name: adv_qqp
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_duplicate
'1': duplicate
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 9926
num_examples: 78
download_size: 40662
dataset_size: 9926
- config_name: adv_mnli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 23736
num_examples: 121
download_size: 40662
dataset_size: 23736
- config_name: adv_mnli_mismatched
features:
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dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 40982
num_examples: 162
download_size: 40662
dataset_size: 40982
- config_name: adv_qnli
features:
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dtype: string
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 34877
num_examples: 148
download_size: 40662
dataset_size: 34877
- config_name: adv_rte
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 25998
num_examples: 81
download_size: 40662
dataset_size: 25998
config_names:
- adv_mnli
- adv_mnli_mismatched
- adv_qnli
- adv_qqp
- adv_rte
- adv_sst2
---
# Dataset Card for Adversarial GLUE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://adversarialglue.github.io/
- **Repository:**
- **Paper:** [arXiv](https://arxiv.org/pdf/2111.02840.pdf)
- **Leaderboard:**
- **Point of Contact:**
- **Size of downloaded dataset files:** 202.75 kB
### Dataset Summary
Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark that focuses on the adversarial robustness evaluation of language models. It covers five natural language understanding tasks from the famous GLUE tasks and is an adversarial version of GLUE benchmark.
AdvGLUE considers textual adversarial attacks from different perspectives and hierarchies, including word-level transformations, sentence-level manipulations, and human-written adversarial examples, which provide comprehensive coverage of various adversarial linguistic phenomena.
### Supported Tasks and Leaderboards
Leaderboard available on the homepage: [https://adversarialglue.github.io/](https://adversarialglue.github.io/).
### Languages
AdvGLUE deviates from the GLUE dataset, which has a base language of English.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 202.75 kB
- **Example**:
```python
>>> datasets.load_dataset('adv_glue', 'adv_sst2')['validation'][0]
{'sentence': "it 's an uneven treat that bores fun at the democratic exercise while also examining its significance for those who take part .", 'label': 1, 'idx': 0}
```
### Data Fields
The data fields are the same as in the GLUE dataset, which differ by task.
The data fields are the same among all splits.
#### adv_mnli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### adv_mnli_matched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### adv_mnli_mismatched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### adv_qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### adv_qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### adv_rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### adv_sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Splits
Adversarial GLUE provides only a 'dev' split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is distributed under the [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/legalcode) license.
### Citation Information
```bibtex
@article{Wang2021AdversarialGA,
title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},
author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},
journal={ArXiv},
year={2021},
volume={abs/2111.02840}
}
```
### Contributions
Thanks to [@jxmorris12](https://github.com/jxmorris12) for adding this dataset. | 8,183 | [
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] |
cyrilzhang/TinyStories2-ascii-bpe-2k | 2023-09-22T23:24:28.000Z | [
"region:us"
] | cyrilzhang | null | null | 0 | 1,943 | 2023-09-22T23:23:58 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 2369808200
num_examples: 578002
- name: validation
num_bytes: 23866100
num_examples: 5821
download_size: 827963790
dataset_size: 2393674300
---
# Dataset Card for "TinyStories2-ascii-bpe-2k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 588 | [
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JulesBelveze/tldr_news | 2022-08-05T12:17:50.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_ids:news-articles-headline-generation",
"task_ids:text-simplification",
"task_ids:language-modeling",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"region:us"
] | JulesBelveze | The `tldr_news` dataset was constructed by collecting a daily tech newsletter (available at
https://tldr.tech/newsletter). Then for every piece of news, the "headline" and its corresponding "content" were
collected. Such a dataset can be used to train a model to generate a headline from a input piece of text. | null | 12 | 1,942 | 2022-06-21T14:35:34 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
multilinguality:
- monolingual
pretty_name: tldr_news
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids:
- news-articles-headline-generation
- text-simplification
- language-modeling
---
# Dataset Card for `tldr_news`
## 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://tldr.tech/newsletter
### Dataset Summary
The `tldr_news` dataset was constructed by collecting a daily tech newsletter (available
[here](https://tldr.tech/newsletter)). Then, for every piece of news, the `headline` and its corresponding `
content` were extracted.
Also, the newsletter contain different sections. We add this extra information to every piece of news.
Such a dataset can be used to train a model to generate a headline from a input piece of text.
### Supported Tasks and Leaderboards
There is no official supported tasks nor leaderboard for this dataset. However, it could be used for the following
tasks:
- summarization
- headline generation
### Languages
en
## Dataset Structure
### Data Instances
A data point comprises a "headline" and its corresponding "content".
An example is as follows:
```
{
"headline": "Cana Unveils Molecular Beverage Printer, a ‘Netflix for Drinks’ That Can Make Nearly Any Type of Beverage ",
"content": "Cana has unveiled a drink machine that can synthesize almost any drink. The machine uses a cartridge that contains flavor compounds that can be combined to create the flavor of nearly any type of drink. It is about the size of a toaster and could potentially save people from throwing hundreds of containers away every month by allowing people to create whatever drinks they want at home. Around $30 million was spent building Cana’s proprietary hardware platform and chemistry system. Cana plans to start full production of the device and will release pricing by the end of February.",
"category": "Science and Futuristic Technology"
}
```
### Data Fields
- `headline (str)`: the piece of news' headline
- `content (str)`: the piece of news
- `category (str)`: newsletter section
### Data Splits
- `all`: all existing daily newsletters available [here](https://tldr.tech/newsletter).
## Dataset Creation
### Curation Rationale
This dataset was obtained by scrapping the collecting all the existing newsletter
available [here](https://tldr.tech/newsletter).
Every single newsletter was then processed to extract all the different pieces of news. Then for every collected piece
of news the headline and the news content were extracted.
### Source Data
#### Initial Data Collection and Normalization
The dataset was has been collected from https://tldr.tech/newsletter.
In order to clean up the samples and to construct a dataset better suited for headline generation we have applied a
couple of normalization steps:
1. The headlines initially contain an estimated read time in parentheses; we stripped this information from the
headline.
2. Some news are sponsored and thus do not belong to any newsletter section. We create an additional category "Sponsor"
for such samples.
#### Who are the source language producers?
The people (or person) behind the https://tldr.tech/ newsletter.
### Annotations
#### Annotation process
Disclaimers: The dataset was generated from a daily newsletter. The author had no intention for those newsletters to be
used as such.
#### Who are the annotators?
The newsletters were written by the people behind *TLDR tech*.
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
This dataset only contains tech news. A model trained on such a dataset might not be able to generalize to other domain.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was obtained by collecting newsletters from this website: https://tldr.tech/newsletter
### Contributions
Thanks to [@JulesBelveze](https://github.com/JulesBelveze) for adding this dataset. | 5,231 | [
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frgfm/imagenette | 2022-12-11T22:26:06.000Z | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:en",
"license:apache-2.0",
"region:us"
] | frgfm | Imagenette is a subset of 10 easily classified classes from Imagenet
(tench, English springer, cassette player, chain saw, church, French
horn, garbage truck, gas pump, golf ball, parachute). | @software{Howard_Imagenette_2019,
title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette}
} | 7 | 1,934 | 2022-07-18T00:13:35 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality: []
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- image-classification
task_ids: []
paperswithcode_id: imagenette
pretty_name: Imagenette
---
# Dataset Card for Imagenette
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/fastai/imagenette
- **Repository:** https://github.com/fastai/imagenette
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenette
### Dataset Summary
A smaller subset of 10 easily classified classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary), and a little more French.
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.
### Supported Tasks and Leaderboards
- `image-classification`: The dataset can be used to train a model for Image Classification.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
A data point comprises an image URL and its classification label.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>,
'label': 'tench',
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image.
- `label`: the expected class label of the image.
### Data Splits
| |train|validation|
|----------|----:|---------:|
|imagenette| 9469| 3925|
## Dataset Creation
### Curation Rationale
cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale
### Source Data
#### Initial Data Collection and Normalization
Imagenette is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
### Annotations
#### Annotation process
cf. https://huggingface.co/datasets/imagenet-1k#annotation-process
#### Who are the annotators?
cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators
### Personal and Sensitive Information
cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information
## Considerations for Using the Data
### Social Impact of Dataset
cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset
### Discussion of Biases
cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases
### Other Known Limitations
cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations
## Additional Information
### Dataset Curators
cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators
and Jeremy Howard
### Licensing Information
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@software{Howard_Imagenette_2019,
title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette}
}
```
### Contributions
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).
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] |
BeIR/trec-covid | 2022-10-23T06:00:45.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 0 | 1,932 | 2022-06-05T14:49:49 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
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EleutherAI/proof-pile-2 | 2023-10-25T06:16:04.000Z | [
"task_categories:text-generation",
"size_categories:10B<n<100B",
"language:en",
"math",
"arxiv:2310.10631",
"arxiv:2310.06786",
"region:us"
] | EleutherAI | A dataset of high quality mathematical text. | null | 76 | 1,931 | 2023-10-12T00:11:33 | ---
task_categories:
- text-generation
language:
- en
tags:
- math
size_categories:
- 10B<n<100B
---
<img src="proofpile_logo.jpg" width="500">
[ArXiv](http://arxiv.org/abs/2310.10631) | [Models](https://huggingface.co/EleutherAI/llemma_34b) | [Data](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | [Code](https://github.com/EleutherAI/math-lm) | [Blog](https://blog.eleuther.ai/llemma/) | [Sample Explorer](https://llemma-demo.github.io/)
[Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Hailey Schoelkopf](https://github.com/haileyschoelkopf), [Keiran Paster](https://keirp.com), [Marco Dos Santos](https://github.com/dsantosmarco), [Stephen McAleer](https://www.andrew.cmu.edu/user/smcaleer/), [Albert Q. Jiang](https://albertqjiang.github.io/), [Jia Deng](https://www.cs.princeton.edu/~jiadeng/), [Stella Biderman](https://www.stellabiderman.com/), [Sean Welleck](https://wellecks.com/)
The **Proof-Pile-2** is a 55 billion token dataset of mathematical and scientific documents. This dataset was created in order to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b) models. It consists of three subsets:
- `arxiv` (29B tokens): the ArXiv subset of [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
- `open-web-math` (15B tokens): The [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) dataset, which contains much of the high-quality mathematical text from the internet.
- `algebraic-stack` (11B tokens): A new dataset of mathematical code, including numerical computing, computer algebra, and formal mathematics.
You can download the dataset as follows
```python
from datasets import load_dataset
ds = load_dataset("EleutherAI/proof-pile-2")
# To load only a specific subset, pass it as an argument, e.g
ds_arxiv = load_dataset("EleutherAI/proof-pile-2", "arxiv")
```
### Schema
Each dataset row has the following structure
```python
{
"text": ..., # document text
"meta": ..., # JSON string of metadata, schema specific to data source
}
```
### Dataset Contents
For detailed documentation of the ArXiv and web subsets, refer to [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) and [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math). The following table enumerates the contents of the AlgebraicStack by programming language. The AlgebraicStack is filtered to only include documents that contain mathematics, as judged by hand-crafted, language-specific heuristics.
| Language | AlgebraicStack tokens |
|-----------|-----------------------|
| Agda | 35.2 M |
| C | 25.1 M |
| C++ | 954.1 M |
| Coq | 281.9 M |
| Fortran | 724.9 M |
| GAP | 3.6 M |
| Haskell | 9.1 M |
| Idris | 10.9 M |
| Isabelle | 1,089.7 M |
| Julia | 531.0 M |
| Jupyter | 199.1 M |
| Lean | 285.6 M |
| Maple | 2.0 M |
| Matlab | 65.8 M |
| Python | 6,098.8 M |
| R | 71.3 M |
| Tex | 567.7 M |
| **Total** | **10,955.7 M** |
### License
We do not alter the license of any of the underlying data.
### Version History
**v1.1.0**: Contains an updated version of OpenWebMath, precisely the one available at [open-web-math/open-web-math](https://huggingface.co/datasets/open-web-math/open-web-math). This version of OpenWebMath has slightly improved filtering, for example, removal of very short documents.
**v1.0.0**: The data used to train the [Llemma 7B](https://huggingface.co/EleutherAI/llemma_7b) and [Llemma 34B](https://huggingface.co/EleutherAI/llemma_34b). Uses a development version of OpenWebMath.
### Citation
For the entire Proof-Pile-2, cite
```
@misc{azerbayev2023llemma,
title={Llemma: An Open Language Model For Mathematics},
author={Zhangir Azerbayev and Hailey Schoelkopf and Keiran Paster and Marco Dos Santos and Stephen McAleer and Albert Q. Jiang and Jia Deng and Stella Biderman and Sean Welleck},
year={2023},
eprint={2310.10631},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
For the ArXiv subset, cite
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
For OpenWebMath, cite
```
@misc{paster2023openwebmath,
title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
year={2023},
eprint={2310.06786},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
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textvqa | 2022-11-18T22:07:01.000Z | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1904.08920",
"arxiv:2007.00398",
"region:us"
] | null | TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset. | @inproceedings{singh2019towards,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8317-8326},
year={2019}
} | 9 | 1,923 | 2022-05-05T06:44:56 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: TextVQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
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download_size: 8070116310
dataset_size: 28390930
---
# Dataset Card for TextVQA
## 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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://textvqa.org
- **Repository:** https://github.com/facebookresearch/mmf
- **Paper:** https://arxiv.org/abs/1904.08920
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/874/overview
- **Point of Contact:** mailto:amanpreet@nyu.edu
### Dataset Summary
TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset. The dataset uses [VQA accuracy](https://visualqa.org/evaluation.html) metric for evaluation.
### Supported Tasks and Leaderboards
- `visual-question-answering`: The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image.
### Languages
The questions in the dataset are in English.
## Dataset Structure
### Data Instances
A typical sample mainly contains the question in `question` field, an image object in `image` field, OpenImage image id in `image_id` and lot of other useful metadata. 10 answers per questions are contained in the `answers` attribute. For test set, 10 empty strings are contained in the `answers` field as the answers are not available for it.
An example look like below:
```
{'question': 'who is this copyrighted by?',
'image_id': '00685bc495504d61',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
'image_classes': ['Vehicle', 'Tower', 'Airplane', 'Aircraft'],
'flickr_original_url': 'https://farm2.staticflickr.com/5067/5620759429_4ea686e643_o.jpg',
'flickr_300k_url': 'https://c5.staticflickr.com/6/5067/5620759429_f43a649fb5_z.jpg',
'image_width': 786,
'image_height': 1024,
'answers': ['simon clancy',
'simon ciancy',
'simon clancy',
'simon clancy',
'the brand is bayard',
'simon clancy',
'simon clancy',
'simon clancy',
'simon clancy',
'simon clancy'],
'question_tokens': ['who', 'is', 'this', 'copyrighted', 'by'],
'question_id': 3,
'set_name': 'train'
},
```
### Data Fields
- `question`: string, the question that is being asked about the image
- `image_id`: string, id of the image which is same as the OpenImages id
- `image`: A `PIL.Image.Image` object containing the image about which the question is being asked. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `image_classes`: List[str], The OpenImages classes to which the image belongs to.
- `flickr_original_url`: string, URL to original image on Flickr
- `flickr_300k_url`: string, URL to resized and low-resolution image on Flickr.
- `image_width`: int, Width of the original image.
- `image_height`: int, Height of the original image.
- `question_tokens`: List[str], A pre-tokenized list of question.
- `answers`: List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers.
- `question_id`: int, Unique id of the question.
- `set_name`: string, the set to which this question belongs.
### Data Splits
There are three splits. `train`, `validation` and `test`. The `train` and `validation` sets share images with OpenImages `train` set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on `test` set, you need to go to the [EvalAI leaderboard](https://eval.ai/web/challenges/challenge-page/874/overview) and upload your predictions there. Please see instructions at [https://textvqa.org/challenge/](https://textvqa.org/challenge/).
## Dataset Creation
### Curation Rationale
From the paper:
> Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer.
### Source Data
#### Initial Data Collection and Normalization
The initial images were sourced from [OpenImages](https://storage.googleapis.com/openimages/web/factsfigures_v4.html) v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See [annotation process](#annotation-process) section to understand the next stages.
#### Who are the source language producers?
English Crowdsource Annotators
### Annotations
#### Annotation process
After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage.
#### Who are the annotators?
Annotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper.
### Personal and Sensitive Information
The dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided `image_classes` data field is one option to try to filter out some of this information.
## Considerations for Using the Data
### Social Impact of Dataset
The paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including [TextCaps](https://textvqa.org/textcaps) and [TextOCR](https://textvqa.org/textocr). Similar datasets were introduced over the time which specifically focus on sight-disabled users such as [VizWiz](https://vizwiz.org) or focusing specifically on the same problem as TextVQA like [STVQA](https://paperswithcode.com/dataset/st-vqa), [DocVQA](https://arxiv.org/abs/2007.00398v3) and [OCRVQA](https://ocr-vqa.github.io/). Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets.
### Discussion of Biases
Question-only bias where a model is able to answer the question without even looking at the image is discussed in the [paper](https://arxiv.org/abs/1904.08920) which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric.
### Other Known Limitations
- The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding.
- The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers.
- The metric used for calculating accuracy is same as [VQA accuracy](https://visualqa.org/evaluation.html). This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR.
## Additional Information
### Dataset Curators
- [Amanpreet Singh](https://github.com/apsdehal)
- Vivek Natarjan
- Meet Shah
- Yu Jiang
- Xinlei Chen
- Dhruv Batra
- Devi Parikh
- Marcus Rohrbach
### Licensing Information
CC by 4.0
### Citation Information
```bibtex
@inproceedings{singh2019towards,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8317-8326},
year={2019}
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset. | 13,205 | [
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crows_pairs | 2023-07-06T09:23:23.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"bias-evaluation",
"region:us"
] | null | CrowS-Pairs, a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models (MLMs). | @inproceedings{nangia2020crows,
title = "{CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models}",
author = "Nangia, Nikita and
Vania, Clara and
Bhalerao, Rasika and
Bowman, Samuel R.",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics"
} | 4 | 1,921 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: crows-pairs
pretty_name: CrowS-Pairs
tags:
- bias-evaluation
dataset_info:
features:
- name: id
dtype: int32
- name: sent_more
dtype: string
- name: sent_less
dtype: string
- name: stereo_antistereo
dtype:
class_label:
names:
'0': stereo
'1': antistereo
- name: bias_type
dtype:
class_label:
names:
'0': race-color
'1': socioeconomic
'2': gender
'3': disability
'4': nationality
'5': sexual-orientation
'6': physical-appearance
'7': religion
'8': age
- name: annotations
sequence:
sequence:
class_label:
names:
'0': race-color
'1': socioeconomic
'2': gender
'3': disability
'4': nationality
'5': sexual-orientation
'6': physical-appearance
'7': religion
'8': age
- name: anon_writer
dtype: string
- name: anon_annotators
sequence: string
config_name: crows_pairs
splits:
- name: test
num_bytes: 419976
num_examples: 1508
download_size: 437764
dataset_size: 419976
---
# Dataset Card for CrowS-Pairs
## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** https://github.com/nyu-mll/crows-pairs
- **Paper:** https://aclanthology.org/2020.emnlp-main.154
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### 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
[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
CrowS-Pairs is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
It is created using prompts taken from the [ROCStories corpora](https://cs.rochester.edu/nlp/rocstories/) and the fiction part of [MNLI](https://cims.nyu.edu/~sbowman/multinli/). Please refer to their papers for more details.
### Citation Information
```
@inproceedings{nangia-etal-2020-crows,
title = "{C}row{S}-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models",
author = "Nangia, Nikita and
Vania, Clara and
Bhalerao, Rasika and
Bowman, Samuel R.",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.154",
doi = "10.18653/v1/2020.emnlp-main.154",
pages = "1953--1967",
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | 5,257 | [
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lighteval/MATH | 2023-10-17T20:52:35.000Z | [
"region:us"
] | lighteval | MATH is a dataset of 12,500 challenging competition mathematics problems. Each
problem in Math has a full step-by-step solution which can be used to teach
models to generate answer derivations and explanations. | @article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the Math Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
} | 3 | 1,919 | 2023-04-20T15:05:44 | Entry not found | 15 | [
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teknium/GPT4-LLM-Cleaned | 2023-05-04T01:48:35.000Z | [
"region:us"
] | teknium | null | null | 84 | 1,884 | 2023-05-02T20:11:04 | This is the GPT4-LLM dataset from : https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
It has been filtered of all OpenAI disclaimers and refusals. (Disclaimer: It may have removed some additional things besides just OAI disclaimers, as I used the followings script which is a bit more broad: https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered/blob/main/wizardlm_clean.py)
There is a modified script of that in the repo that was used specifically for this. | 501 | [
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] |
L4NLP/LEval | 2023-10-11T03:56:48.000Z | [
"task_categories:summarization",
"task_categories:question-answering",
"task_categories:multiple-choice",
"size_categories:1K<n<10K",
"language:en",
"license:gpl-3.0",
"Long_context",
"region:us"
] | L4NLP | A benchmark to evaluate long document understanding and generation ability of LLM | } | 8 | 1,864 | 2023-06-14T11:51:39 | ---
license: gpl-3.0
task_categories:
- summarization
- question-answering
- multiple-choice
language:
- en
size_categories:
- 1K<n<10K
viewer: true
tags:
- Long_context
---
### *L-Eval: Instituting Standardized Evaluation for Long Context Language Models*
L-Eval is a comprehensive long-context language models evaluation suite with 18 long document tasks across multiple domains that require reasoning over long texts, including summarization, question answering, in-context learning with long CoT examples, topic retrieval, and paper writing assistance. L-Eval is a high-quality test set with 411 long documents and 2043 query-response pairs. All samples in L-Eval have been manually annotated and checked by the authors. There have been many studies exploring the expansion of context length in large models. However, it remains to be explored whether these methods perform well enough in downstream tasks and whether they can surpass previous methods based on retrieval or chunking.
We hope L-Eval could help researchers and developers track the progress of long-context language models (LCLMs) and understand the strengths/shortcomings of different methods.
Dataset list:
```
["coursera", "gsm100", "quality", "topic_retrieval_longchat", "tpo", "financial_qa", "gov_report_summ", "legal_contract_qa", "meeting_summ", "multidoc_qa", "narrative_qa", "natural_question", "news_summ", "paper_assistant", "patent_summ", "review_summ", "scientific_qa", "tv_show_summ"]
```
Detailed descriptions and how we collect the data can be found [https://github.com/OpenLMLab/LEval](https://github.com/OpenLMLab/LEval). | 1,616 | [
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clinc_oos | 2023-01-25T14:28:10.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | This dataset is for evaluating the performance of intent classification systems in the
presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
into any of the system-supported intent classes. Most datasets include only data that is
"in-scope". Our dataset includes both in-scope and out-of-scope data. You might also know
the term "out-of-scope" by other terms, including "out-of-domain" or "out-of-distribution". | @inproceedings{larson-etal-2019-evaluation,
title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
author = "Larson, Stefan and
Mahendran, Anish and
Peper, Joseph J. and
Clarke, Christopher and
Lee, Andrew and
Hill, Parker and
Kummerfeld, Jonathan K. and
Leach, Kevin and
Laurenzano, Michael A. and
Tang, Lingjia and
Mars, Jason",
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)",
year = "2019",
url = "https://www.aclweb.org/anthology/D19-1131"
} | 10 | 1,863 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: clinc150
pretty_name: CLINC150
dataset_info:
- config_name: small
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
'0': restaurant_reviews
'1': nutrition_info
'2': account_blocked
'3': oil_change_how
'4': time
'5': weather
'6': redeem_rewards
'7': interest_rate
'8': gas_type
'9': accept_reservations
'10': smart_home
'11': user_name
'12': report_lost_card
'13': repeat
'14': whisper_mode
'15': what_are_your_hobbies
'16': order
'17': jump_start
'18': schedule_meeting
'19': meeting_schedule
'20': freeze_account
'21': what_song
'22': meaning_of_life
'23': restaurant_reservation
'24': traffic
'25': make_call
'26': text
'27': bill_balance
'28': improve_credit_score
'29': change_language
'30': 'no'
'31': measurement_conversion
'32': timer
'33': flip_coin
'34': do_you_have_pets
'35': balance
'36': tell_joke
'37': last_maintenance
'38': exchange_rate
'39': uber
'40': car_rental
'41': credit_limit
'42': oos
'43': shopping_list
'44': expiration_date
'45': routing
'46': meal_suggestion
'47': tire_change
'48': todo_list
'49': card_declined
'50': rewards_balance
'51': change_accent
'52': vaccines
'53': reminder_update
'54': food_last
'55': change_ai_name
'56': bill_due
'57': who_do_you_work_for
'58': share_location
'59': international_visa
'60': calendar
'61': translate
'62': carry_on
'63': book_flight
'64': insurance_change
'65': todo_list_update
'66': timezone
'67': cancel_reservation
'68': transactions
'69': credit_score
'70': report_fraud
'71': spending_history
'72': directions
'73': spelling
'74': insurance
'75': what_is_your_name
'76': reminder
'77': where_are_you_from
'78': distance
'79': payday
'80': flight_status
'81': find_phone
'82': greeting
'83': alarm
'84': order_status
'85': confirm_reservation
'86': cook_time
'87': damaged_card
'88': reset_settings
'89': pin_change
'90': replacement_card_duration
'91': new_card
'92': roll_dice
'93': income
'94': taxes
'95': date
'96': who_made_you
'97': pto_request
'98': tire_pressure
'99': how_old_are_you
'100': rollover_401k
'101': pto_request_status
'102': how_busy
'103': application_status
'104': recipe
'105': calendar_update
'106': play_music
'107': 'yes'
'108': direct_deposit
'109': credit_limit_change
'110': gas
'111': pay_bill
'112': ingredients_list
'113': lost_luggage
'114': goodbye
'115': what_can_i_ask_you
'116': book_hotel
'117': are_you_a_bot
'118': next_song
'119': change_speed
'120': plug_type
'121': maybe
'122': w2
'123': oil_change_when
'124': thank_you
'125': shopping_list_update
'126': pto_balance
'127': order_checks
'128': travel_alert
'129': fun_fact
'130': sync_device
'131': schedule_maintenance
'132': apr
'133': transfer
'134': ingredient_substitution
'135': calories
'136': current_location
'137': international_fees
'138': calculator
'139': definition
'140': next_holiday
'141': update_playlist
'142': mpg
'143': min_payment
'144': change_user_name
'145': restaurant_suggestion
'146': travel_notification
'147': cancel
'148': pto_used
'149': travel_suggestion
'150': change_volume
splits:
- name: train
num_bytes: 394128
num_examples: 7600
- name: validation
num_bytes: 160302
num_examples: 3100
- name: test
num_bytes: 286970
num_examples: 5500
download_size: 1702451
dataset_size: 841400
- config_name: imbalanced
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
'0': restaurant_reviews
'1': nutrition_info
'2': account_blocked
'3': oil_change_how
'4': time
'5': weather
'6': redeem_rewards
'7': interest_rate
'8': gas_type
'9': accept_reservations
'10': smart_home
'11': user_name
'12': report_lost_card
'13': repeat
'14': whisper_mode
'15': what_are_your_hobbies
'16': order
'17': jump_start
'18': schedule_meeting
'19': meeting_schedule
'20': freeze_account
'21': what_song
'22': meaning_of_life
'23': restaurant_reservation
'24': traffic
'25': make_call
'26': text
'27': bill_balance
'28': improve_credit_score
'29': change_language
'30': 'no'
'31': measurement_conversion
'32': timer
'33': flip_coin
'34': do_you_have_pets
'35': balance
'36': tell_joke
'37': last_maintenance
'38': exchange_rate
'39': uber
'40': car_rental
'41': credit_limit
'42': oos
'43': shopping_list
'44': expiration_date
'45': routing
'46': meal_suggestion
'47': tire_change
'48': todo_list
'49': card_declined
'50': rewards_balance
'51': change_accent
'52': vaccines
'53': reminder_update
'54': food_last
'55': change_ai_name
'56': bill_due
'57': who_do_you_work_for
'58': share_location
'59': international_visa
'60': calendar
'61': translate
'62': carry_on
'63': book_flight
'64': insurance_change
'65': todo_list_update
'66': timezone
'67': cancel_reservation
'68': transactions
'69': credit_score
'70': report_fraud
'71': spending_history
'72': directions
'73': spelling
'74': insurance
'75': what_is_your_name
'76': reminder
'77': where_are_you_from
'78': distance
'79': payday
'80': flight_status
'81': find_phone
'82': greeting
'83': alarm
'84': order_status
'85': confirm_reservation
'86': cook_time
'87': damaged_card
'88': reset_settings
'89': pin_change
'90': replacement_card_duration
'91': new_card
'92': roll_dice
'93': income
'94': taxes
'95': date
'96': who_made_you
'97': pto_request
'98': tire_pressure
'99': how_old_are_you
'100': rollover_401k
'101': pto_request_status
'102': how_busy
'103': application_status
'104': recipe
'105': calendar_update
'106': play_music
'107': 'yes'
'108': direct_deposit
'109': credit_limit_change
'110': gas
'111': pay_bill
'112': ingredients_list
'113': lost_luggage
'114': goodbye
'115': what_can_i_ask_you
'116': book_hotel
'117': are_you_a_bot
'118': next_song
'119': change_speed
'120': plug_type
'121': maybe
'122': w2
'123': oil_change_when
'124': thank_you
'125': shopping_list_update
'126': pto_balance
'127': order_checks
'128': travel_alert
'129': fun_fact
'130': sync_device
'131': schedule_maintenance
'132': apr
'133': transfer
'134': ingredient_substitution
'135': calories
'136': current_location
'137': international_fees
'138': calculator
'139': definition
'140': next_holiday
'141': update_playlist
'142': mpg
'143': min_payment
'144': change_user_name
'145': restaurant_suggestion
'146': travel_notification
'147': cancel
'148': pto_used
'149': travel_suggestion
'150': change_volume
splits:
- name: train
num_bytes: 546909
num_examples: 10625
- name: validation
num_bytes: 160302
num_examples: 3100
- name: test
num_bytes: 286970
num_examples: 5500
download_size: 2016773
dataset_size: 994181
- config_name: plus
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
'0': restaurant_reviews
'1': nutrition_info
'2': account_blocked
'3': oil_change_how
'4': time
'5': weather
'6': redeem_rewards
'7': interest_rate
'8': gas_type
'9': accept_reservations
'10': smart_home
'11': user_name
'12': report_lost_card
'13': repeat
'14': whisper_mode
'15': what_are_your_hobbies
'16': order
'17': jump_start
'18': schedule_meeting
'19': meeting_schedule
'20': freeze_account
'21': what_song
'22': meaning_of_life
'23': restaurant_reservation
'24': traffic
'25': make_call
'26': text
'27': bill_balance
'28': improve_credit_score
'29': change_language
'30': 'no'
'31': measurement_conversion
'32': timer
'33': flip_coin
'34': do_you_have_pets
'35': balance
'36': tell_joke
'37': last_maintenance
'38': exchange_rate
'39': uber
'40': car_rental
'41': credit_limit
'42': oos
'43': shopping_list
'44': expiration_date
'45': routing
'46': meal_suggestion
'47': tire_change
'48': todo_list
'49': card_declined
'50': rewards_balance
'51': change_accent
'52': vaccines
'53': reminder_update
'54': food_last
'55': change_ai_name
'56': bill_due
'57': who_do_you_work_for
'58': share_location
'59': international_visa
'60': calendar
'61': translate
'62': carry_on
'63': book_flight
'64': insurance_change
'65': todo_list_update
'66': timezone
'67': cancel_reservation
'68': transactions
'69': credit_score
'70': report_fraud
'71': spending_history
'72': directions
'73': spelling
'74': insurance
'75': what_is_your_name
'76': reminder
'77': where_are_you_from
'78': distance
'79': payday
'80': flight_status
'81': find_phone
'82': greeting
'83': alarm
'84': order_status
'85': confirm_reservation
'86': cook_time
'87': damaged_card
'88': reset_settings
'89': pin_change
'90': replacement_card_duration
'91': new_card
'92': roll_dice
'93': income
'94': taxes
'95': date
'96': who_made_you
'97': pto_request
'98': tire_pressure
'99': how_old_are_you
'100': rollover_401k
'101': pto_request_status
'102': how_busy
'103': application_status
'104': recipe
'105': calendar_update
'106': play_music
'107': 'yes'
'108': direct_deposit
'109': credit_limit_change
'110': gas
'111': pay_bill
'112': ingredients_list
'113': lost_luggage
'114': goodbye
'115': what_can_i_ask_you
'116': book_hotel
'117': are_you_a_bot
'118': next_song
'119': change_speed
'120': plug_type
'121': maybe
'122': w2
'123': oil_change_when
'124': thank_you
'125': shopping_list_update
'126': pto_balance
'127': order_checks
'128': travel_alert
'129': fun_fact
'130': sync_device
'131': schedule_maintenance
'132': apr
'133': transfer
'134': ingredient_substitution
'135': calories
'136': current_location
'137': international_fees
'138': calculator
'139': definition
'140': next_holiday
'141': update_playlist
'142': mpg
'143': min_payment
'144': change_user_name
'145': restaurant_suggestion
'146': travel_notification
'147': cancel
'148': pto_used
'149': travel_suggestion
'150': change_volume
splits:
- name: train
num_bytes: 791255
num_examples: 15250
- name: validation
num_bytes: 160302
num_examples: 3100
- name: test
num_bytes: 286970
num_examples: 5500
download_size: 2509789
dataset_size: 1238527
---
# Dataset Card for CLINC150
## 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/clinc/oos-eval/)
- **Repository:** [Github](https://github.com/clinc/oos-eval/)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/D19-1131)
- **Leaderboard:** [PapersWithCode](https://paperswithcode.com/sota/text-classification-on-clinc-oos)
- **Point of Contact:**
### Dataset Summary
Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope (OOS), i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. It offers a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.
### Supported Tasks and Leaderboards
- `intent-classification`: This dataset is for evaluating the performance of intent classification systems in the presence of "out-of-scope" queries, i.e., queries that do not fall into any of the system-supported intent classes. The dataset includes both in-scope and out-of-scope data. [here](https://paperswithcode.com/sota/text-classification-on-clinc-oos).
### Languages
English
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'text' : 'can you walk me through setting up direct deposits to my bank of internet savings account',
'label' : 108
}
```
### Data Fields
- text : Textual data
- label : 150 intent classes over 10 domains, the dataset contains one label for 'out-of-scope' intent.
The Label Id to Label Name map is mentioned in the table below:
| **Label Id** | **Label name** |
|--- |--- |
| 0 | restaurant_reviews |
| 1 | nutrition_info |
| 2 | account_blocked |
| 3 | oil_change_how |
| 4 | time |
| 5 | weather |
| 6 | redeem_rewards |
| 7 | interest_rate |
| 8 | gas_type |
| 9 | accept_reservations |
| 10 | smart_home |
| 11 | user_name |
| 12 | report_lost_card |
| 13 | repeat |
| 14 | whisper_mode |
| 15 | what_are_your_hobbies |
| 16 | order |
| 17 | jump_start |
| 18 | schedule_meeting |
| 19 | meeting_schedule |
| 20 | freeze_account |
| 21 | what_song |
| 22 | meaning_of_life |
| 23 | restaurant_reservation |
| 24 | traffic |
| 25 | make_call |
| 26 | text |
| 27 | bill_balance |
| 28 | improve_credit_score |
| 29 | change_language |
| 30 | no |
| 31 | measurement_conversion |
| 32 | timer |
| 33 | flip_coin |
| 34 | do_you_have_pets |
| 35 | balance |
| 36 | tell_joke |
| 37 | last_maintenance |
| 38 | exchange_rate |
| 39 | uber |
| 40 | car_rental |
| 41 | credit_limit |
| 42 | oos |
| 43 | shopping_list |
| 44 | expiration_date |
| 45 | routing |
| 46 | meal_suggestion |
| 47 | tire_change |
| 48 | todo_list |
| 49 | card_declined |
| 50 | rewards_balance |
| 51 | change_accent |
| 52 | vaccines |
| 53 | reminder_update |
| 54 | food_last |
| 55 | change_ai_name |
| 56 | bill_due |
| 57 | who_do_you_work_for |
| 58 | share_location |
| 59 | international_visa |
| 60 | calendar |
| 61 | translate |
| 62 | carry_on |
| 63 | book_flight |
| 64 | insurance_change |
| 65 | todo_list_update |
| 66 | timezone |
| 67 | cancel_reservation |
| 68 | transactions |
| 69 | credit_score |
| 70 | report_fraud |
| 71 | spending_history |
| 72 | directions |
| 73 | spelling |
| 74 | insurance |
| 75 | what_is_your_name |
| 76 | reminder |
| 77 | where_are_you_from |
| 78 | distance |
| 79 | payday |
| 80 | flight_status |
| 81 | find_phone |
| 82 | greeting |
| 83 | alarm |
| 84 | order_status |
| 85 | confirm_reservation |
| 86 | cook_time |
| 87 | damaged_card |
| 88 | reset_settings |
| 89 | pin_change |
| 90 | replacement_card_duration |
| 91 | new_card |
| 92 | roll_dice |
| 93 | income |
| 94 | taxes |
| 95 | date |
| 96 | who_made_you |
| 97 | pto_request |
| 98 | tire_pressure |
| 99 | how_old_are_you |
| 100 | rollover_401k |
| 101 | pto_request_status |
| 102 | how_busy |
| 103 | application_status |
| 104 | recipe |
| 105 | calendar_update |
| 106 | play_music |
| 107 | yes |
| 108 | direct_deposit |
| 109 | credit_limit_change |
| 110 | gas |
| 111 | pay_bill |
| 112 | ingredients_list |
| 113 | lost_luggage |
| 114 | goodbye |
| 115 | what_can_i_ask_you |
| 116 | book_hotel |
| 117 | are_you_a_bot |
| 118 | next_song |
| 119 | change_speed |
| 120 | plug_type |
| 121 | maybe |
| 122 | w2 |
| 123 | oil_change_when |
| 124 | thank_you |
| 125 | shopping_list_update |
| 126 | pto_balance |
| 127 | order_checks |
| 128 | travel_alert |
| 129 | fun_fact |
| 130 | sync_device |
| 131 | schedule_maintenance |
| 132 | apr |
| 133 | transfer |
| 134 | ingredient_substitution |
| 135 | calories |
| 136 | current_location |
| 137 | international_fees |
| 138 | calculator |
| 139 | definition |
| 140 | next_holiday |
| 141 | update_playlist |
| 142 | mpg |
| 143 | min_payment |
| 144 | change_user_name |
| 145 | restaurant_suggestion |
| 146 | travel_notification |
| 147 | cancel |
| 148 | pto_used |
| 149 | travel_suggestion |
| 150 | change_volume |
### Data Splits
The dataset comes in different subsets:
- `small` : Small, in which there are only 50 training queries per each in-scope intent
- `imbalanced` : Imbalanced, in which intents have either 25, 50, 75, or 100 training queries.
- `plus`: OOS+, in which there are 250 out-of-scope training examples, rather than 100.
| name |train|validation|test|
|----------|----:|---------:|---:|
|small|7600| 3100| 5500 |
|imbalanced|10625| 3100| 5500|
|plus|15250| 3100| 5500|
## 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
```
@inproceedings{larson-etal-2019-evaluation,
title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
author = "Larson, Stefan and
Mahendran, Anish and
Peper, Joseph J. and
Clarke, Christopher and
Lee, Andrew and
Hill, Parker and
Kummerfeld, Jonathan K. and
Leach, Kevin and
Laurenzano, Michael A. and
Tang, Lingjia and
Mars, Jason",
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)",
year = "2019",
url = "https://www.aclweb.org/anthology/D19-1131"
}
```
### Contributions
Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset. | 23,440 | [
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subjqa | 2023-03-16T13:27:54.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|yelp_review_full",
"source_datasets:extended|other-amazon_reviews_ucsd",
"source_datasets:extended|other-tripadvisor_reviews",
"language:en",
"license:unknown",
"arxiv:2004.14283",
"region:us"
] | null | SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants. | @inproceedings{bjerva20subjqa,
title = "SubjQA: A Dataset for Subjectivity and Review Comprehension",
author = "Bjerva, Johannes and
Bhutani, Nikita and
Golahn, Behzad and
Tan, Wang-Chiew and
Augenstein, Isabelle",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = November,
year = "2020",
publisher = "Association for Computational Linguistics",
} | 7 | 1,858 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
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- found
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- en
license:
- unknown
multilinguality:
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- 1K<n<10K
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- extended|yelp_review_full
- extended|other-amazon_reviews_ucsd
- extended|other-tripadvisor_reviews
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---
# Dataset Card for subjqa
## 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/lewtun/SubjQA
- **Paper:** https://arxiv.org/abs/2004.14283
- **Point of Contact:** [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com)
### Dataset Summary
SubjQA is a question answering dataset that focuses on subjective (as opposed to factual) questions and answers. The dataset consists of roughly **10,000** questions over reviews from 6 different domains: books, movies, grocery, electronics, TripAdvisor (i.e. hotels), and restaurants. Each question is paired with a review and a span is highlighted as the answer to the question (with some questions having no answer). Moreover, both questions and answer spans are assigned a _subjectivity_ label by annotators. Questions such as _"How much does this product weigh?"_ is a factual question (i.e., low subjectivity), while "Is this easy to use?" is a subjective question (i.e., high subjectivity).
In short, SubjQA provides a setting to study how well extractive QA systems perform on finding answer that are less factual and to what extent modeling subjectivity can improve the performance of QA systems.
_Note:_ Much of the information provided on this dataset card is taken from the README provided by the authors in their GitHub repository ([link](https://github.com/megagonlabs/SubjQA)).
To load a domain with `datasets` you can run the following:
```python
from datasets import load_dataset
# other options include: electronics, grocery, movies, restaurants, tripadvisor
dataset = load_dataset("subjqa", "books")
```
### Supported Tasks and Leaderboards
* `question-answering`: The dataset can be used to train a model for extractive question answering, which involves questions whose answer can be identified as a span of text in a review. Success on this task is typically measured by achieving a high Exact Match or F1 score. The BERT model that is first fine-tuned on SQuAD 2.0 and then further fine-tuned on SubjQA achieves the scores shown in the figure below.
### Languages
The text in the dataset is in English and the associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An example from `books` domain is shown below:
```json
{
"answers": {
"ans_subj_score": [1.0],
"answer_start": [324],
"answer_subj_level": [2],
"is_ans_subjective": [true],
"text": ["This is a wonderfully written book"],
},
"context": "While I would not recommend this book to a young reader due to a couple pretty explicate scenes I would recommend it to any adult who just loves a good book. Once I started reading it I could not put it down. I hesitated reading it because I didn't think that the subject matter would be interesting, but I was so wrong. This is a wonderfully written book.",
"domain": "books",
"id": "0255768496a256c5ed7caed9d4e47e4c",
"is_ques_subjective": false,
"nn_asp": "matter",
"nn_mod": "interesting",
"q_reviews_id": "a907837bafe847039c8da374a144bff9",
"query_asp": "part",
"query_mod": "fascinating",
"ques_subj_score": 0.0,
"question": "What are the parts like?",
"question_subj_level": 2,
"review_id": "a7f1a2503eac2580a0ebbc1d24fffca1",
"title": "0002007770",
}
```
### Data Fields
Each domain and split consists of the following columns:
* ```title```: The id of the item/business discussed in the review.
* ```question```: The question (written based on a query opinion).
* ```id```: A unique id assigned to the question-review pair.
* ```q_reviews_id```: A unique id assigned to all question-review pairs with a shared question.
* ```question_subj_level```: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).
* ```ques_subj_score```: The subjectivity score of the question computed using the [TextBlob](https://textblob.readthedocs.io/en/dev/) package.
* ```context```: The review (that mentions the neighboring opinion).
* ```review_id```: A unique id associated with the review.
* ```answers.text```: The span labeled by annotators as the answer.
* ```answers.answer_start```: The (character-level) start index of the answer span highlighted by annotators.
* ```is_ques_subjective```: A boolean subjectivity label derived from ```question_subj_level``` (i.e., scores below 4 are considered as subjective)
* ```answers.answer_subj_level```: The subjectiviy level of the answer span (on a 1 to 5 scale with 5 being the most subjective).
* ```answers.ans_subj_score```: The subjectivity score of the answer span computed usign the [TextBlob](https://textblob.readthedocs.io/en/dev/) package.
* ```answers.is_ans_subjective```: A boolean subjectivity label derived from ```answer_subj_level``` (i.e., scores below 4 are considered as subjective)
* ```domain```: The category/domain of the review (e.g., hotels, books, ...).
* ```nn_mod```: The modifier of the neighboring opinion (which appears in the review).
* ```nn_asp```: The aspect of the neighboring opinion (which appears in the review).
* ```query_mod```: The modifier of the query opinion (around which a question is manually written).
* ```query_asp```: The aspect of the query opinion (around which a question is manually written).
### Data Splits
The question-review pairs from each domain are split into training, development, and test sets. The table below shows the size of the dataset per each domain and split.
| Domain | Train | Dev | Test | Total |
|-------------|-------|-----|------|-------|
| TripAdvisor | 1165 | 230 | 512 | 1686 |
| Restaurants | 1400 | 267 | 266 | 1683 |
| Movies | 1369 | 261 | 291 | 1677 |
| Books | 1314 | 256 | 345 | 1668 |
| Electronics | 1295 | 255 | 358 | 1659 |
| Grocery | 1124 | 218 | 591 | 1725 |
Based on the subjectivity labels provided by annotators, one observes that 73% of the questions and 74% of the answers in the dataset are subjective. This provides a substantial number of subjective QA pairs as well as a reasonable number of factual questions to compare and constrast the performance of QA systems on each type of QA pairs.
Finally, the next table summarizes the average length of the question, the review, and the highlighted answer span for each category.
| Domain | Review Len | Question Len | Answer Len | % answerable |
|-------------|------------|--------------|------------|--------------|
| TripAdvisor | 187.25 | 5.66 | 6.71 | 78.17 |
| Restaurants | 185.40 | 5.44 | 6.67 | 60.72 |
| Movies | 331.56 | 5.59 | 7.32 | 55.69 |
| Books | 285.47 | 5.78 | 7.78 | 52.99 |
| Electronics | 249.44 | 5.56 | 6.98 | 58.89 |
| Grocery | 164.75 | 5.44 | 7.25 | 64.69 |
## Dataset Creation
### Curation Rationale
Most question-answering datasets like SQuAD and Natural Questions focus on answering questions over factual data such as Wikipedia and news articles. However, in domains like e-commerce the questions and answers are often _subjective_, that is, they depend on the personal experience of the users. For example, a customer on Amazon may ask "Is the sound quality any good?", which is more difficult to answer than a factoid question like "What is the capital of Australia?" These considerations motivate the creation of SubjQA as a tool to investigate the relationship between subjectivity and question-answering.
### Source Data
#### Initial Data Collection and Normalization
The SubjQA dataset is constructed based on publicly available review datasets. Specifically, the _movies_, _books_, _electronics_, and _grocery_ categories are constructed using reviews from the [Amazon Review dataset](http://jmcauley.ucsd.edu/data/amazon/links.html). The _TripAdvisor_ category, as the name suggests, is constructed using reviews from TripAdvisor which can be found [here](http://times.cs.uiuc.edu/~wang296/Data/). Finally, the _restaurants_ category is constructed using the [Yelp Dataset](https://www.yelp.com/dataset) which is also publicly available.
The process of constructing SubjQA is discussed in detail in the [paper](https://arxiv.org/abs/2004.14283). In a nutshell, the dataset construction consists of the following steps:
1. First, all _opinions_ expressed in reviews are extracted. In the pipeline, each opinion is modeled as a (_modifier_, _aspect_) pair which is a pair of spans where the former describes the latter. (good, hotel), and (terrible, acting) are a few examples of extracted opinions.
2. Using Matrix Factorization techniques, implication relationships between different expressed opinions are mined. For instance, the system mines that "responsive keys" implies "good keyboard". In our pipeline, we refer to the conclusion of an implication (i.e., "good keyboard" in this examples) as the _query_ opinion, and we refer to the premise (i.e., "responsive keys") as its _neighboring_ opinion.
3. Annotators are then asked to write a question based on _query_ opinions. For instance given "good keyboard" as the query opinion, they might write "Is this keyboard any good?"
4. Each question written based on a _query_ opinion is then paired with a review that mentions its _neighboring_ opinion. In our example, that would be a review that mentions "responsive keys".
5. The question and review pairs are presented to annotators to select the correct answer span, and rate the subjectivity level of the question as well as the subjectivity level of the highlighted answer span.
A visualisation of the data collection pipeline is shown in the image below.
#### Who are the source language producers?
As described above, the source data for SubjQA is customer reviews of products and services on e-commerce websites like Amazon and TripAdvisor.
### Annotations
#### Annotation process
The generation of questions and answer span labels were obtained through the [Appen](https://appen.com/) platform. From the SubjQA paper:
> The platform provides quality control by showing the workers 5 questions at a time, out of which one is labeled by the experts. A worker who fails to maintain 70% accuracy is kicked out by the platform and his judgements are ignored ... To ensure good quality labels, we paid each worker 5 cents per annotation.
The instructions for generating a question are shown in the following figure:
<img width="874" alt="ques_gen" src="https://user-images.githubusercontent.com/26859204/117259092-03d67300-ae4e-11eb-81f2-9077fee1085f.png">
Similarly, the interface for the answer span and subjectivity labelling tasks is shown below:
As described in the SubjQA paper, the workers assign subjectivity scores (1-5) to each question and the selected answer span. They can also indicate if a question cannot be answered from the given review.
#### Who are the annotators?
Workers on the Appen platform.
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
The SubjQA dataset can be used to develop question-answering systems that can provide better on-demand answers to e-commerce customers who are interested in subjective questions about products and services.
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The people involved in creating the SubjQA dataset are the authors of the accompanying paper:
* Johannes Bjerva1, Department of Computer Science, University of Copenhagen, Department of Computer Science, Aalborg University
* Nikita Bhutani, Megagon Labs, Mountain View
* Behzad Golshan, Megagon Labs, Mountain View
* Wang-Chiew Tan, Megagon Labs, Mountain View
* Isabelle Augenstein, Department of Computer Science, University of Copenhagen
### Licensing Information
The SubjQA dataset is provided "as-is", and its creators make no representation as to its accuracy.
The SubjQA dataset is constructed based on the following datasets and thus contains subsets of their data:
* [Amazon Review Dataset](http://jmcauley.ucsd.edu/data/amazon/links.html) from UCSD
* Used for _books_, _movies_, _grocery_, and _electronics_ domains
* [The TripAdvisor Dataset](http://times.cs.uiuc.edu/~wang296/Data/) from UIUC's Database and Information Systems Laboratory
* Used for the _TripAdvisor_ domain
* [The Yelp Dataset](https://www.yelp.com/dataset)
* Used for the _restaurants_ domain
Consequently, the data within each domain of the SubjQA dataset should be considered under the same license as the dataset it was built upon.
### Citation Information
If you are using the dataset, please cite the following in your work:
```
@inproceedings{bjerva20subjqa,
title = "SubjQA: A Dataset for Subjectivity and Review Comprehension",
author = "Bjerva, Johannes and
Bhutani, Nikita and
Golahn, Behzad and
Tan, Wang-Chiew and
Augenstein, Isabelle",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = November,
year = "2020",
publisher = "Association for Computational Linguistics",
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset. | 21,615 | [
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polyglot_ner | 2023-04-05T13:36:52.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:ar",
"language:bg",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fa",
"language:fi",
"language:fr",
"language:he",
"language:hi",
"language:hr",
"language:hu",
"language:id",
"language:it",
"language:ja",
"language:ko",
"language:lt",
"language:lv",
"language:ms",
"language:nl",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sk",
"language:sl",
"language:sr",
"language:sv",
"language:th",
"language:tl",
"language:tr",
"language:uk",
"language:vi",
"language:zh",
"license:unknown",
"arxiv:1410.3791",
"region:us"
] | null | Polyglot-NER
A training dataset automatically generated from Wikipedia and Freebase the task
of named entity recognition. The dataset contains the basic Wikipedia based
training data for 40 languages we have (with coreference resolution) for the task of
named entity recognition. The details of the procedure of generating them is outlined in
Section 3 of the paper (https://arxiv.org/abs/1410.3791). Each config contains the data
corresponding to a different language. For example, "es" includes only spanish examples. | @article{polyglotner,
author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven},
title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition},
journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, Canada, April 30- May 2, 2015}},
month = {April},
year = {2015},
publisher = {SIAM},
} | 21 | 1,854 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- ar
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- fa
- fi
- fr
- he
- hi
- hr
- hu
- id
- it
- ja
- ko
- lt
- lv
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- sk
- sl
- sr
- sv
- th
- tl
- tr
- uk
- vi
- zh
license:
- unknown
multilinguality:
- multilingual
pretty_name: Polyglot-NER
size_categories:
- unknown
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: polyglot-ner
dataset_info:
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---
# Dataset Card for Polyglot-NER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://sites.google.com/site/rmyeid/projects/polylgot-ner](https://sites.google.com/site/rmyeid/projects/polylgot-ner)
- **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:** 45.39 GB
- **Size of the generated dataset:** 12.54 GB
- **Total amount of disk used:** 57.93 GB
### Dataset Summary
Polyglot-NER
A training dataset automatically generated from Wikipedia and Freebase the task
of named entity recognition. The dataset contains the basic Wikipedia based
training data for 40 languages we have (with coreference resolution) for the task of
named entity recognition. The details of the procedure of generating them is outlined in
Section 3 of the paper (https://arxiv.org/abs/1410.3791). Each config contains the data
corresponding to a different language. For example, "es" includes only spanish examples.
### 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:** 1.11 GB
- **Size of the generated dataset:** 183.55 MB
- **Total amount of disk used:** 1.29 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": "2",
"lang": "ar",
"ner": ["O", "O", "O", "O", "O", "O", "O", "O", "LOC", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "PER", "PER", "PER", "PER", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
"words": "[\"وفي\", \"مرحلة\", \"موالية\", \"أنشأت\", \"قبيلة\", \"مكناسة\", \"الزناتية\", \"مكناسة\", \"تازة\", \",\", \"وأقام\", \"بها\", \"المرابطون\", \"قلعة\", \"..."
}
```
#### bg
- **Size of downloaded dataset files:** 1.11 GB
- **Size of the generated dataset:** 190.51 MB
- **Total amount of disk used:** 1.30 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": "1",
"lang": "bg",
"ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
"words": "[\"Дефиниция\", \"Наименованията\", \"\\\"\", \"книжовен\", \"\\\"/\\\"\", \"литературен\", \"\\\"\", \"език\", \"на\", \"български\", \"за\", \"тази\", \"кодифи..."
}
```
#### ca
- **Size of downloaded dataset files:** 1.11 GB
- **Size of the generated dataset:** 143.75 MB
- **Total amount of disk used:** 1.25 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": "2",
"lang": "ca",
"ner": "[\"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O\", \"O...",
"words": "[\"Com\", \"a\", \"compositor\", \"deixà\", \"un\", \"immens\", \"llegat\", \"que\", \"inclou\", \"8\", \"simfonies\", \"(\", \"1822\", \"),\", \"diverses\", ..."
}
```
#### combined
- **Size of downloaded dataset files:** 1.11 GB
- **Size of the generated dataset:** 6.29 GB
- **Total amount of disk used:** 7.39 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": "18",
"lang": "es",
"ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
"words": "[\"Los\", \"cambios\", \"en\", \"la\", \"energía\", \"libre\", \"de\", \"Gibbs\", \"\\\\\", \"Delta\", \"G\", \"nos\", \"dan\", \"una\", \"cuantificación\", \"de..."
}
```
#### cs
- **Size of downloaded dataset files:** 1.11 GB
- **Size of the generated dataset:** 156.79 MB
- **Total amount of disk used:** 1.26 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": "3",
"lang": "cs",
"ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"],
"words": "[\"Historie\", \"Symfonická\", \"forma\", \"se\", \"rozvinula\", \"se\", \"především\", \"v\", \"období\", \"klasicismu\", \"a\", \"romantismu\", \",\", \"..."
}
```
### Data Fields
The data fields are the same among all splits.
#### ar
- `id`: a `string` feature.
- `lang`: a `string` feature.
- `words`: a `list` of `string` features.
- `ner`: a `list` of `string` features.
#### bg
- `id`: a `string` feature.
- `lang`: a `string` feature.
- `words`: a `list` of `string` features.
- `ner`: a `list` of `string` features.
#### ca
- `id`: a `string` feature.
- `lang`: a `string` feature.
- `words`: a `list` of `string` features.
- `ner`: a `list` of `string` features.
#### combined
- `id`: a `string` feature.
- `lang`: a `string` feature.
- `words`: a `list` of `string` features.
- `ner`: a `list` of `string` features.
#### cs
- `id`: a `string` feature.
- `lang`: a `string` feature.
- `words`: a `list` of `string` features.
- `ner`: a `list` of `string` features.
### Data Splits
| name | train |
|----------|---------:|
| ar | 339109 |
| bg | 559694 |
| ca | 372665 |
| combined | 21070925 |
| cs | 564462 |
## 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{polyglotner,
author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven},
title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition},
journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, Canada, April 30- May 2, 2015}},
month = {April},
year = {2015},
publisher = {SIAM},
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. | 22,312 | [
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bigcode/starcoderdata | 2023-05-16T10:05:48.000Z | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"region:us"
] | bigcode | null | null | 202 | 1,851 | 2023-03-30T12:02:21 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: The-Stack
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
extra_gated_prompt: >-
## Terms of Use for The Stack
The Stack dataset is a collection of source code in over 300 programming
languages. We ask that you read and acknowledge the following points before
using the dataset:
1. The Stack is a collection of source code from repositories with various
licenses. Any use of all or part of the code gathered in The Stack must abide
by the terms of the original licenses, including attribution clauses when
relevant. We facilitate this by providing provenance information for each data
point.
2. The Stack is regularly updated to enact validated data removal requests. By
clicking on "Access repository", you agree to update your own version of The
Stack to the most recent usable version specified by the maintainers in [the
following
thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If
you have questions about dataset versions and allowed uses, please also ask
them in the dataset’s [community
discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new).
We will also notify users via email when the latest usable version changes.
3. To host, share, or otherwise provide access to The Stack dataset, you must
include [these Terms of
Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack)
and require users to agree to it.
By clicking on "Access repository" below, you accept that your contact
information (email address and username) can be shared with the dataset
maintainers as well.
extra_gated_fields:
Email: text
I have read the License and agree with its terms: checkbox
---
# StarCoder Training Dataset
## Dataset description
This is the dataset used for training [StarCoder](https://huggingface.co/bigcode/starcoder) and [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,
and 32GB of GitHub commits, which is approximately 250 Billion tokens.
## Dataset creation
The creation and filtering of The Stack is explained in the [original dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup), we additionally decontaminate and clean all 86 programming
languages in the dataset, in addition to GitHub issues, Jupyter Notebooks and GitHub commits. We also apply near-deduplication and remove PII, all details are mentionned in our [Paper: 💫 StarCoder, May The Source Be With You](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view)
## How to use the dataset
```python
from datasets import load_dataset
# to load python for example
ds = load_dataset("bigcode/starcoderdata", data_dir="python", split="train")
```
GitHub issues, GitHub commits and Jupyter notebooks subsets have different columns from the rest so loading the entire dataset at once may fail, we suggest loading programming languages separatly from these categories.
````
jupyter-scripts-dedup-filtered
jupyter-structured-clean-dedup
github-issues-filtered-structured
git-commits-cleaned
````
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lama | 2023-06-01T14:59:53.000Z | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:fact-checking-retrieval",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:extended|conceptnet5",
"source_datasets:extended|squad",
"language:en",
"license:cc-by-4.0",
"probing",
"region:us"
] | null | LAMA is a dataset used to probe and analyze the factual and commonsense knowledge contained in pretrained language models. See https://github.com/facebookresearch/LAMA. | @inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={2019}
}
@inproceedings{petroni2020how,
title={How Context Affects Language Models' Factual Predictions},
author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=025X0zPfn}
} | 8 | 1,848 | 2022-03-02T23:29:22 | ---
pretty_name: 'LAMA: LAnguage Model Analysis'
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n<1K
source_datasets:
- extended|conceptnet5
- extended|squad
task_categories:
- text-retrieval
- text-classification
task_ids:
- fact-checking-retrieval
- text-scoring
paperswithcode_id: lama
tags:
- probing
dataset_info:
- config_name: trex
features:
- name: uuid
dtype: string
- name: obj_uri
dtype: string
- name: obj_label
dtype: string
- name: sub_uri
dtype: string
- name: sub_label
dtype: string
- name: predicate_id
dtype: string
- name: sub_surface
dtype: string
- name: obj_surface
dtype: string
- name: masked_sentence
dtype: string
- name: template
dtype: string
- name: template_negated
dtype: string
- name: label
dtype: string
- name: description
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 656913189
num_examples: 1304391
download_size: 74652201
dataset_size: 656913189
- config_name: squad
features:
- name: id
dtype: string
- name: sub_label
dtype: string
- name: obj_label
dtype: string
- name: negated
dtype: string
- name: masked_sentence
dtype: string
splits:
- name: train
num_bytes: 57188
num_examples: 305
download_size: 74639115
dataset_size: 57188
- config_name: google_re
features:
- name: pred
dtype: string
- name: sub
dtype: string
- name: obj
dtype: string
- name: evidences
dtype: string
- name: judgments
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
splits:
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num_bytes: 7638657
num_examples: 6106
download_size: 74639115
dataset_size: 7638657
- config_name: conceptnet
features:
- name: uuid
dtype: string
- name: sub
dtype: string
- name: obj
dtype: string
- name: pred
dtype: string
- name: obj_label
dtype: string
- name: masked_sentence
dtype: string
- name: negated
dtype: string
splits:
- name: train
num_bytes: 4130000
num_examples: 29774
download_size: 74639115
dataset_size: 4130000
config_names:
- conceptnet
- google_re
- squad
- trex
---
# Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models.
## 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/facebookresearch/LAMA
- **Repository:**
https://github.com/facebookresearch/LAMA
- **Paper:**
@inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={2019}
}
@inproceedings{petroni2020how,
title={How Context Affects Language Models' Factual Predictions},
author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=025X0zPfn}
}
### Dataset Summary
This dataset provides the data for LAMA. The dataset include a subset
of Google_RE
(https://code.google.com/archive/p/relation-extraction-corpus/), TRex
(subset of wikidata triples), Conceptnet
(https://github.com/commonsense/conceptnet5/wiki) and Squad. There are
configs for each of "google_re", "trex", "conceptnet" and "squad",
respectively.
The dataset includes some cleanup, and addition of a masked sentence
and associated answers for the [MASK] token. The accuracy in
predicting the [MASK] token shows how well the language model knows
facts and common sense information. The [MASK] tokens are only for the
"object" slots.
This version of the dataset includes "negated" sentences as well as
the masked sentence. Also, certain of the config includes "template"
and "template_negated" fields of the form "[X] some text [Y]", where
[X] and [Y] are the subject and object slots respectively of certain
relations.
See the paper for more details. For more information, also see:
https://github.com/facebookresearch/LAMA
### Languages
en
## Dataset Structure
### Data Instances
The trex config has the following fields:
``
{'description': 'the item (an institution, law, public office ...) or statement belongs to or has power over or applies to the value (a territorial jurisdiction: a country, state, municipality, ...)', 'label': 'applies to jurisdiction', 'masked_sentence': 'It is known as a principality as it is a monarchy headed by two Co-Princes – the Spanish/Roman Catholic Bishop of Urgell and the President of [MASK].', 'obj_label': 'France', 'obj_surface': 'France', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'president of the French Republic', 'sub_surface': 'President', 'sub_uri': 'Q191954', 'template': '[X] is a legal term in [Y] .', 'template_negated': '[X] is not a legal term in [Y] .', 'type': 'N-M', 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'}
``
The conceptnet config has the following fields:
``
{'masked_sentence': 'One of the things you do when you are alive is [MASK].', 'negated': '', 'obj': 'think', 'obj_label': 'think', 'pred': 'HasSubevent', 'sub': 'alive', 'uuid': 'd4f11631dde8a43beda613ec845ff7d1'}
``
The squad config has the following fields:
``
{'id': '56be4db0acb8001400a502f0_0', 'masked_sentence': 'To emphasize the 50th anniversary of the Super Bowl the [MASK] color was used.', 'negated': "['To emphasize the 50th anniversary of the Super Bowl the [MASK] color was not used.']", 'obj_label': 'gold', 'sub_label': 'Squad'}
``
The google_re config has the following fields:
``
{'evidences': '[{\'url\': \'http://en.wikipedia.org/wiki/Peter_F._Martin\', \'snippet\': "Peter F. Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives. He has represented the 75th District Newport since 6 January 2009. He is currently serves on the House Committees on Judiciary, Municipal Government, and Veteran\'s Affairs. During his first term of office he served on the House Committees on Small Business and Separation of Powers & Government Oversight. In August 2010, Representative Martin was appointed as a Commissioner on the Atlantic States Marine Fisheries Commission", \'considered_sentences\': [\'Peter F Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives .\']}]', 'judgments': "[{'rater': '18349444711114572460', 'judgment': 'yes'}, {'rater': '17595829233063766365', 'judgment': 'yes'}, {'rater': '4593294093459651288', 'judgment': 'yes'}, {'rater': '7387074196865291426', 'judgment': 'yes'}, {'rater': '17154471385681223613', 'judgment': 'yes'}]", 'masked_sentence': 'Peter F Martin (born [MASK]) is an American politician who is a Democratic member of the Rhode Island House of Representatives .', 'obj': '1941', 'obj_aliases': '[]', 'obj_label': '1941', 'obj_w': 'None', 'pred': '/people/person/date_of_birth', 'sub': '/m/09gb0bw', 'sub_aliases': '[]', 'sub_label': 'Peter F. Martin', 'sub_w': 'None', 'template': '[X] (born [Y]).', 'template_negated': '[X] (not born [Y]).', 'uuid': '18af2dac-21d3-4c42-aff5-c247f245e203'}
``
### Data Fields
The trex config has the following fields:
* uuid: the id
* obj_uri: a uri for the object slot
* obj_label: a label for the object slot
* sub_uri: a uri for the subject slot
* sub_label: a label for the subject slot
* predicate_id: the predicate/relationship
* sub_surface: the surface text for the subject
* obj_surface: The surface text for the object. This is the word that should be predicted by the [MASK] token.
* masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
* template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. template may be missing and replaced with an empty string.
* template_negated: Same as above, except the [Y] is not the object. template_negated may be missing and replaced with empty strings.
* label: the label for the relationship/predicate. label may be missing and replaced with an empty string.
* description': a description of the relationship/predicate. description may be missing and replaced with an empty string.
* type: a type id for the relationship/predicate. type may be missing and replaced with an empty string.
The conceptnet config has the following fields:
* uuid: the id
* sub: the subject. subj may be missing and replaced with an empty string.
* obj: the object to be predicted. obj may be missing and replaced with an empty string.
* pred: the predicate/relationship
* obj_label: the object label
* masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
* negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings.
The squad config has the following fields:
* id: the id
* sub_label: the subject label
* obj_label: the object label that is being predicted
* masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
* negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings.
The google_re config has the following fields:
* uuid: the id
* pred: the predicate
* sub: the subject. subj may be missing and replaced with an empty string.
* obj: the object. obj may be missing and replaced with an empty string.
* evidences: flattened json string that provides evidence for predicate. parse this json string to get more 'snippet' information.
* judgments: data about judgments
* sub_q: unknown
* sub_label: label for the subject
* sub_aliases: unknown
* obj_w: unknown
* obj_label: label for the object
* obj_aliases: unknown
* masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
* template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively.
* template_negated: Same as above, except the [Y] is not the object.
### Data Splits
There are no data splits.
## Dataset Creation
### Curation Rationale
This dataset was gathered and created to probe what language models understand.
### Source Data
#### Initial Data Collection and Normalization
See the reaserch paper and website for more detail. The dataset was
created gathered from various other datasets with cleanups for probing.
#### Who are the source language producers?
The LAMA authors and the original authors of the various configs.
### Annotations
#### Annotation process
Human annotations under the original datasets (conceptnet), and various machine annotations.
#### Who are the annotators?
Human annotations and machine annotations.
### Personal and Sensitive Information
Unkown, but likely names of famous people.
## Considerations for Using the Data
### Social Impact of Dataset
The goal for the work is to probe the understanding of language models.
### Discussion of Biases
Since the data is from human annotators, there is likely to be baises.
[More Information Needed]
### Other Known Limitations
The original documentation for the datafields are limited.
## Additional Information
### Dataset Curators
The authors of LAMA at Facebook and the authors of the original datasets.
### Licensing Information
The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE
### Citation Information
@inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={2019}
}
@inproceedings{petroni2020how,
title={How Context Affects Language Models' Factual Predictions},
author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=025X0zPfn}
}
### Contributions
Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset. | 14,286 | [
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openbmb/UltraFeedback | 2023-09-30T16:39:29.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | openbmb | null | null | 140 | 1,846 | 2023-09-23T15:41:04 | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
## Introduction
- [GitHub Repo](https://github.com/thunlp/UltraFeedback)
- [UltraRM-13b](https://huggingface.co/openbmb/UltraRM-13b)
- [UltraCM-13b](https://huggingface.co/openbmb/UltraCM-13b)
UltraFeedback is a **large-scale, fine-grained, diverse preference dataset**, used for training powerful reward models and critic models. We collect about 64k prompts from diverse resources (including UltraChat, ShareGPT, Evol-Instruct, TruthfulQA, FalseQA, and FLAN). We then use these prompts to query multiple LLMs (see Table for model lists) and generate 4 different responses for each prompt, resulting in a total of 256k samples.
To collect high-quality preference and textual feedback, we design a fine-grained annotation instruction, which contains 4 different aspects, namely **instruction-following**, **truthfulness**, **honesty** and **helpfulness**. We then ask GPT-4 to annotate the collected samples based on the instructions.
## Features
- 🆚 **Scale**: UltraFeedback consists of 64k prompts, 256k responses and 380k high-quality feedback. RLHF researchers could further construct around 1 million comparison pairs to train their reward models.
- 🌈 **Diversity**: As a preference dataset, diversity is the core requirement for UltraFeedback. We collect prompts from various sources and query a diverse set of state-of-the-art open-source and prestigious models. To further increase diversity, we intended to select different base models, i.e., LLaMA, Falcon, StarChat, MPT, GPT and Bard. We also apply various principles to stimulate models completing instructions in different ways.
- 🤯 **High-density**: UltraFeedback provides both numerical and textual feedback. Moreover, we wrote fine-grained annotation documents to help rate responses in all dimensions
## Dataset Construction
### Instruction Sampling
We sample 63,967 instructions from 6 public available and high-quality datasets. We include all instructions from TruthfulQA and FalseQA, randomly sampling 10k instructions from Evol-Instruct, 10k from UltraChat, and 20k from ShareGPT. For Flan, we adopt a stratified sampling strtegy, randomly samping 3k instructions from"Co" subset whereas sampling 10 instructions per task for the other three subsets, excluding those with overly long instructions.
```json
{
"evol_instruct": 10000,
"false_qa": 2339,
"flan": 20939,
"sharegpt": 19949,
"truthful_qa": 811,
"ultrachat": 9929
}
```
### Model Sampling
To prevent reward model from overfiting to certain text style or capturing spurious correlation between text style and rewards, we select different base models of all levels, with varying sizes, architectures and training data, to complete the instructions. We set up a pool of 17 models:
- Commercial Models: GPT-4, GPT-3.5 Turbo, Bard
- LLaMA family:
1. LLaMA-2-7B-chat, LLaMA-2-13B-chat, LLaMA-2-70B-chat
2. UltraLM-13B, UltraLM-65B
3. WizardLM-7B, WizardLM-13B, WizardLM-70B
4. Vicuna-33B
5. Alpaca-7B
- Non-LLaMA series:
1. Falcon-40B-instruct
2. MPT-30B-chat
3. StarChat-Beta
4. Pythia-12B
### Principle Sampling
Following [1] and [2], we define a set of principles to explicitly align model behaviors from different aspects. We set up a pool of 5 principles: Helpfulness, Truthfulness, Honesty, Verbalized Calibration and Harmless. For each instruction, we randomly sample 4 models to complete the instruction, and for each completion, we sample a principle and add it to system prompt to align the model behavior. Considering different datasets outline different characteristics, not all dataset are suitable for all principles. We provide the following table to show the principle distribution for each dataset.
| Datset | Principle |
| ------------- | ------------------------------------------------------------ |
| Evol Instruct | 100% Helpful |
| FalseQA | 100% TruthfulQA |
| Flan | 60% Helpful, 20% Truthful, 20% Verbalized Calibration |
| ShareGPT | 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration |
| TruthfulQA | 100% Truthful |
| UltraChat | 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration |
[1] Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision. Sun et al.
[2] Orca: Progressive Learning from Complex Explanation Traces of GPT-4. Mukherjee et al.
## Dataset Format
```JSONC
{
"source": "sharegpt", // the dataset where the instruction comes from
"id": "D2TC9GP_0", // the id of the instruction in the source dataset
"instruction": "I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports",
"models": [ // four models that are randomly sampled from the model pool to complete the instruction
"falcon-40b-instruct",
"gpt-4",
"starchat",
"wizardlm-7b"
],
"completions": [ // four completions corresponding to the four models
{ // completion 1
"model": "falcon-40b-instruct",
"principle": "helpfulness", // priciple that used to align model behavior
"custom_system_prompt": "As an AI assistant, ensure your response offers the perfect blend of accuracy, positivity, and intrigue. Strive to be educational while keeping the user engaged.", // the system prompt corresponding to the principle, sampled from a pool of GPT-4 generated system prompts
"response": "Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip!",
"annotations": {
"instruction_following": [
{
"Rating": "2",
"Rationale": "The text only partially addresses the task goal by providing a general list of activities in Cairo without considering the specific needs of the family members, such as limited walking ability and adventure preferences."
}
],
"honesty": [
{
"Rating": "3",
"Rationale": "The response is confident but contains subtle mistakes. It suggests whitewater rafting on the Nile and diving in the Red Sea, which are not feasible for a 4-5 day trip to Cairo. The rest of the suggestions are accurate and relevant to the family's preferences."
}
],
"truthfulness": [
{
"Type": [
"1",
"2"
],
"Rationale": "The text suggests whitewater rafting on the Nile and diving in the Red Sea, which are not available in Cairo. These activities are factually incorrect and not aligned with the instruction.",
"Rating": "3",
"Rationale For Rating": "The text provides some truthful information, but the inclusion of whitewater rafting and diving in the Red Sea causes partial hallucination and misunderstanding."
}
],
"helpfulness": [
{
"Type": [
"1",
"2"
],
"Rationale": "The response is clear and relevant, providing useful information, but it does not address the limited walking ability of the grandparents or the adventure sports preferences of the sister, mom, and the user.",
"Rating": "3",
"Rationale For Rating": "The text is correct and provides useful information, but it does not fully address the specific needs and preferences of the family."
}
]
}
},
{ // completion 2
...
},
{ // completion 3
...
},
{ // completion 4
...
}
]
},
```
## Dataset Example
Here we present an example of UltraFeedback
> **User:** I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports
> **Assisstant 1 (falcon-40b-instruct):** Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip!
> **Assisstant 2 (gpt-4):** In Cairo for 4-5 days, the following activities cater to your family's preferences, with consideration for the limited walking ability of your grandparents:\n\n1. Visit the Pyramids of Giza and the Great Sphinx: A must-see in Cairo, it's an incredible experience for all ages. You can explore the premises using a hired camel, horse, or a horse-drawn carriage which accommodate the needs of your grandparents while keeping the experience enjoyable.\n\n2. Egyptian Museum: This museum in Tahrir Square contains over 120,000 artifacts of Egypt's history. A wheelchair can be arranged for the grandparents to comfortably explore the museum at their own pace.\n\n3. Khan el-Khalili Bazaar: A famous market in Cairo, suitable for souvenir shopping and experiencing the local culture. There are many cafes and restaurants your family can rest at during the visit.\n\n4. Nile River Cruise: Enjoy a dinner cruise with your family on the famous Nile River. It includes entertainment and a beautiful view of the city by night. Boats often have ramps and accessible facilities for limited mobility passangers.\n\n5. Felucca Ride: During the day, go for a more traditional sailboat ride called a Felucca. This can be done on an off-day or as a relaxation activity after an active day.\n\nFor adventure activities, you and your mom and sister can explore these options:\n\n1. Sandboarding: Head to the desert for a thrilling sandboarding experience on the dunes just outside Cairo.\n\n2. Hot Air Balloon Ride: Experience Egypt from above with a hot air balloon ride in Luxor. It would require a day trip from Cairo but is an incredible experience.\n\n3. Quad Biking: Go for a guided quad biking adventure in the desert, enjoying panoramic views of the iconic Pyramids.\n\nSpace the activities according to your family's preferences and ensure rest periods in the afternoon. Consider hiring a private guide or joining a wheelchair-accessible tour since you're concerned about your grandparents' walking ability.
> **Assisstant 3 (starchat):** Here are some of the top things to do in Cairo, Egypt, with a family of 5:\n\n- Giza Pyramids: The Giza Pyramids, located just outside Cairo, are a must-see for anyone visiting the city. The Great Pyramid of Khufu, the Great Pyramid of Khafre, and the Great Pyramid of Menkaure make up one of the Seven Wonders of the Ancient World and are an impressive sight to behold.\n\n- Egyptian Museum: The Egyptian Museum, located in the heart of Cairo, is home to one of the world's largest and most impressive collections of ancient Egyptian art and artifacts. Visitors can explore the museum's extensive collection, which includes items such as mummies, sarcophagi, statues, paintings, and more.\n\n- Citadel of Salah El-Din: Located on a hill overlooking Cairo, the Citadel of Salah El-Din offers stunning views of the city and is an excellent place to get a sense of Cairo's rich history. Visitors can explore the various museums and mosques within the citadel, and also take a stroll through the beautiful gardens.\n\n- Khan El-Khalili Bazaar: Khan El-Khalili Bazaar is one of Cairo's most popular attractions, a bustling market where visitors can find everything from spices and pottery to clothes and jewelry. It's a great place to do some shopping and explore the local culture.\n\n- Alexandria: Just an hour's drive from Cairo, Alexandria is a historic city with a rich legacy. Visitors can explore the ancient Roman Amphitheater, the Qaitbay Citadel, and the Catacombs of Kom el-Shoqafa. They can also take a boat trip to the island of Montazah, where they can see the famous Graffiti Wall and other street art.\n\nAll of these attractions are family-friendly, with plenty of things to see and do. The Giza Pyramids in particular are very accessible and well-maintained, making them suitable for all ages.
> **Assisstant 4 (wizardlm-7b):** If you are looking for adventure sports, you can explore the pyramids of Giza and go hot air ballooning over the desert. Another option could be to take a camel ride in the desert or go kayaking in the Nile River. For family-friendly activities, you can visit the Egyptian Museum, Cairo Tower, and Khan el-Khalili Bazaar. Additionally, you can take a day trip to the historical city of Alexandria and visit the Pompey's Pillar and the Roman Amphitheater. Another option could be to go on a guided tour of the ancient city of Petra in Jordan, which is a short flight from Cairo.
## Limitations
- Although GPT-4 can provide well-aligned annotation and textual feedback for most samples, we must note that GPT-4 also makes mistakes and provides inaccurate feedbacks. | 15,004 | [
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] |
scan | 2023-06-01T14:59:55.000Z | [
"task_categories:text2text-generation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:bsd",
"multi-turn",
"arxiv:1711.00350",
"region:us"
] | null | SCAN tasks with various splits.
SCAN is a set of simple language-driven navigation tasks for studying
compositional learning and zero-shot generalization.
See https://github.com/brendenlake/SCAN for a description of the splits.
Example usage:
data = datasets.load_dataset('scan/length') | @inproceedings{Lake2018GeneralizationWS,
title={Generalization without Systematicity: On the Compositional Skills of
Sequence-to-Sequence Recurrent Networks},
author={Brenden M. Lake and Marco Baroni},
booktitle={ICML},
year={2018},
url={https://arxiv.org/pdf/1711.00350.pdf},
} | 2 | 1,840 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- bsd
multilinguality:
- monolingual
pretty_name: SCAN
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: scan
tags:
- multi-turn
dataset_info:
- config_name: simple
features:
- name: commands
dtype: string
- name: actions
dtype: string
splits:
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num_bytes: 3217770
num_examples: 16728
- name: test
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num_examples: 4182
download_size: 4080388
dataset_size: 4017682
- config_name: addprim_jump
features:
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dtype: string
- name: actions
dtype: string
splits:
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num_bytes: 2535625
num_examples: 14670
- name: test
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num_examples: 7706
download_size: 4111174
dataset_size: 4044070
- config_name: addprim_turn_left
features:
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dtype: string
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dtype: string
splits:
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num_bytes: 3908891
num_examples: 21890
- name: test
num_bytes: 170063
num_examples: 1208
download_size: 4148216
dataset_size: 4078954
- config_name: filler_num0
features:
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dtype: string
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dtype: string
splits:
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num_bytes: 2513034
num_examples: 15225
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num_bytes: 330087
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download_size: 2892291
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features:
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dtype: string
- name: actions
dtype: string
splits:
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num_bytes: 330087
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download_size: 3185317
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dtype: string
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dtype: string
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dtype: string
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dtype: string
splits:
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num_examples: 18528
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num_bytes: 330087
num_examples: 1173
download_size: 3801870
dataset_size: 3742791
- config_name: length
features:
- name: commands
dtype: string
- name: actions
dtype: string
splits:
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num_examples: 3920
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- name: commands
dtype: string
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dtype: string
splits:
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num_examples: 4476
download_size: 3801870
dataset_size: 3742791
- config_name: template_jump_around_right
features:
- name: commands
dtype: string
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dtype: string
splits:
- name: train
num_bytes: 3412704
num_examples: 18528
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num_bytes: 330087
num_examples: 1173
download_size: 3801870
dataset_size: 3742791
- config_name: template_opposite_right
features:
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dtype: string
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dtype: string
splits:
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dtype: string
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dtype: string
splits:
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num_bytes: 3127623
num_examples: 15225
- name: test
num_bytes: 716403
num_examples: 4476
download_size: 3903105
dataset_size: 3844026
config_names:
- addprim_jump
- addprim_turn_left
- filler_num0
- filler_num1
- filler_num2
- filler_num3
- length
- simple
- template_around_right
- template_jump_around_right
- template_opposite_right
- template_right
---
# Dataset Card for "scan"
## 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/brendenlake/SCAN](https://github.com/brendenlake/SCAN)
- **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:** 224.18 MB
- **Size of the generated dataset:** 44.53 MB
- **Total amount of disk used:** 268.71 MB
### Dataset Summary
SCAN tasks with various splits.
SCAN is a set of simple language-driven navigation tasks for studying
compositional learning and zero-shot generalization.
See https://github.com/brendenlake/SCAN for a description of the splits.
Example usage:
data = datasets.load_dataset('scan/length')
### 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
#### addprim_jump
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 4.05 MB
- **Total amount of disk used:** 22.73 MB
An example of 'train' looks as follows.
```
```
#### addprim_turn_left
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 4.09 MB
- **Total amount of disk used:** 22.76 MB
An example of 'train' looks as follows.
```
```
#### filler_num0
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 2.85 MB
- **Total amount of disk used:** 21.53 MB
An example of 'train' looks as follows.
```
```
#### filler_num1
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 3.14 MB
- **Total amount of disk used:** 21.82 MB
An example of 'train' looks as follows.
```
```
#### filler_num2
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 3.44 MB
- **Total amount of disk used:** 22.12 MB
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### addprim_jump
- `commands`: a `string` feature.
- `actions`: a `string` feature.
#### addprim_turn_left
- `commands`: a `string` feature.
- `actions`: a `string` feature.
#### filler_num0
- `commands`: a `string` feature.
- `actions`: a `string` feature.
#### filler_num1
- `commands`: a `string` feature.
- `actions`: a `string` feature.
#### filler_num2
- `commands`: a `string` feature.
- `actions`: a `string` feature.
### Data Splits
| name |train|test|
|-----------------|----:|---:|
|addprim_jump |14670|7706|
|addprim_turn_left|21890|1208|
|filler_num0 |15225|1173|
|filler_num1 |16290|1173|
|filler_num2 |17391|1173|
## 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{Lake2018GeneralizationWS,
title={Generalization without Systematicity: On the Compositional Skills of
Sequence-to-Sequence Recurrent Networks},
author={Brenden M. Lake and Marco Baroni},
booktitle={ICML},
year={2018},
url={https://arxiv.org/pdf/1711.00350.pdf},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | 10,878 | [
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Tevatron/wikipedia-nq | 2021-11-22T05:32:24.000Z | [
"region:us"
] | Tevatron | null | @inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov,
Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
doi = "10.18653/v1/2020.emnlp-main.550",
pages = "6769--6781",
} | 2 | 1,811 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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llm-book/JGLUE | 2023-10-06T00:58:24.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ja",
"license:cc-by-4.0",
"MARC",
"STS",
"NLI",
"SQuAD",
"CommonsenseQA",
"region:us"
] | llm-book | JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese. | @inproceedings{kurihara-etal-2022-jglue,
title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
author = "Kurihara, Kentaro and
Kawahara, Daisuke and
Shibata, Tomohide",
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-1.317",
pages = "2957--2966",
abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
}
@InProceedings{Kurihara_nlp2022,
author = "栗原健太郎 and 河原大輔 and 柴田知秀",
title = "JGLUE: 日本語言語理解ベンチマーク",
booktitle = "言語処理学会第28回年次大会",
year = "2022",
url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf"
note= "in Japanese"
} | 4 | 1,810 | 2023-05-01T13:00:36 | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: JGLUE
size_categories: []
source_datasets:
- original
tags:
- MARC
- STS
- NLI
- SQuAD
- CommonsenseQA
task_categories:
- multiple-choice
- question-answering
- sentence-similarity
- text-classification
task_ids:
- multiple-choice-qa
- open-domain-qa
- multi-class-classification
- sentiment-classification
---
# Dataset Card for JGLUE
[](https://aclanthology.org/2022.lrec-1.317)
書籍『大規模言語モデル入門』で使用する、JGLUEのデータセットです。
[オリジナルのリポジトリ](https://github.com/yahoojapan/JGLUE)で公開されているデータセットを利用しています。
### Licence
コードのライセンスは Creative Commons Attribution-ShareAlike 4.0 International License です。
データそのもののライセンスは[配布元](https://github.com/yahoojapan/JGLUE)のライセンスに従ってください。
### Citation
```bibtex
@inproceedings{kurihara-etal-2022-jglue,
title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
author = "Kurihara, Kentaro and
Kawahara, Daisuke and
Shibata, Tomohide",
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-1.317",
pages = "2957--2966",
abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
}
```
```bibtex
@InProceedings{Kurihara_nlp2022,
author = "栗原健太郎 and 河原大輔 and 柴田知秀",
title = "JGLUE: 日本語言語理解ベンチマーク",
booktitle = "言語処理学会第 28 回年次大会",
year = "2022",
url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf"
note= "in Japanese"
}
```
### Contributions
データセット作成者である [Kentaro Kurihara](https://twitter.com/kkurihara_cs), [Daisuke Kawahara](https://twitter.com/daisukekawahar1), [Tomohide Shibata](https://twitter.com/stomohide) に感謝を申し上げます。
また本リポジトリのコードは [Shunsuke Kitada](https://twitter.com/shunk031)の[こちらのリポジトリ](https://huggingface.co/datasets/shunk031/JGLUE)を基に作成されたものです。 | 2,671 | [
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lj_speech | 2022-11-03T16:16:34.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unlicense",
"region:us"
] | null | This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading
passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length
from 1 to 10 seconds and have a total length of approximately 24 hours.
Note that in order to limit the required storage for preparing this dataset, the audio
is stored in the .wav format and is not converted to a float32 array. To convert the audio
file to a float32 array, please make use of the `.map()` function as follows:
```python
import soundfile as sf
def map_to_array(batch):
speech_array, _ = sf.read(batch["file"])
batch["speech"] = speech_array
return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
``` | @misc{ljspeech17,
author = {Keith Ito and Linda Johnson},
title = {The LJ Speech Dataset},
howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}},
year = 2017
} | 10 | 1,794 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unlicense
multilinguality:
- monolingual
paperswithcode_id: ljspeech
pretty_name: LJ Speech
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
train-eval-index:
- config: main
task: automatic-speech-recognition
task_id: speech_recognition
splits:
train_split: train
col_mapping:
file: path
text: text
metrics:
- type: wer
name: WER
- type: cer
name: CER
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 22050
- name: file
dtype: string
- name: text
dtype: string
- name: normalized_text
dtype: string
config_name: main
splits:
- name: train
num_bytes: 4667022
num_examples: 13100
download_size: 2748572632
dataset_size: 4667022
---
# Dataset Card for lj_speech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/)
- **Repository:** [N/A]
- **Paper:** [N/A]
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech)
- **Point of Contact:** [Keith Ito](mailto:kito@kito.us)
### Dataset Summary
This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.
The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS).
- `other:automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text.
The most common ASR evaluation metric is the word error rate (WER).
- `other:text-to-speech`: A TTS model is given a written text in natural language and asked to generate a speech audio file.
A reasonable evaluation metric is the mean opinion score (MOS) of audio quality.
The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech
### Languages
The transcriptions and audio are in English.
## Dataset Structure
### Data Instances
A data point comprises the path to the audio file, called `file` and its transcription, called `text`.
A normalized version of the text is also provided.
```
{
'id': 'LJ002-0026',
'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav',
'audio': {'path': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 22050},
'text': 'in the three years between 1813 and 1816,'
'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,',
}
```
Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz.
### Data Fields
- id: unique id of the data sample.
- file: a path to the downloaded audio file in .wav format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words.
### Data Splits
The dataset is not pre-split. Some statistics:
- Total Clips: 13,100
- Total Words: 225,715
- Total Characters: 1,308,678
- Total Duration: 23:55:17
- Mean Clip Duration: 6.57 sec
- Min Clip Duration: 1.11 sec
- Max Clip Duration: 10.10 sec
- Mean Words per Clip: 17.23
- Distinct Words: 13,821
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
This dataset consists of excerpts from the following works:
- Morris, William, et al. Arts and Crafts Essays. 1893.
- Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
- Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
- Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
- Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
- Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
- President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.
Some details about normalization:
- The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
- 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
- The following abbreviations appear in the text. They may be expanded as follows:
| Abbreviation | Expansion |
|--------------|-----------|
| Mr. | Mister |
| Mrs. | Misess (*) |
| Dr. | Doctor |
| No. | Number |
| St. | Saint |
| Co. | Company |
| Jr. | Junior |
| Maj. | Major |
| Gen. | General |
| Drs. | Doctors |
| Rev. | Reverend |
| Lt. | Lieutenant |
| Hon. | Honorable |
| Sgt. | Sergeant |
| Capt. | Captain |
| Esq. | Esquire |
| Ltd. | Limited |
| Col. | Colonel |
| Ft. | Fort |
(*) there's no standard expansion for "Mrs."
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
- The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
- The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio.
#### Who are the annotators?
Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito.
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
- The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding.
## Additional Information
### Dataset Curators
The dataset was initially created by Keith Ito and Linda Johnson.
### Licensing Information
Public Domain ([LibriVox](https://librivox.org/pages/public-domain/))
### Citation Information
```
@misc{ljspeech17,
author = {Keith Ito and Linda Johnson},
title = {The LJ Speech Dataset},
howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
year = 2017
}
```
### Contributions
Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset. | 8,837 | [
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] |
AdaptLLM/medicine-tasks | 2023-10-21T11:44:55.000Z | [
"arxiv:2309.09530",
"region:us"
] | AdaptLLM | null | null | 2 | 1,790 | 2023-09-19T14:53:35 | ---
configs:
- config_name: ChemProt
data_files:
- split: test
path: "ChemProt/test.json"
- config_name: MQP
data_files:
- split: test
path: "MedQs/test.json"
- config_name: PubMedQA
data_files:
- split: test
path: "pubmed_qa/test.json"
- config_name: RCT
data_files:
- split: test
path: "RCT/test.json"
- config_name: USMLE
data_files:
- split: test
path: "usmle/test.json"
---
# Adapting Large Language Models via Reading Comprehension
This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530)
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in **biomedicine, finance, and law domains**. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
## GitHub repo:
https://github.com/microsoft/LMOps
## Domain-specific LLMs:
Our models of different domains are now available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="./comparison.png" width="700">
</p>
## Domain-specific Tasks:
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
## Citation:
```bibtex
@inproceedings{AdaptLLM,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
url={https://arxiv.org/abs/2309.09530},
year={2023},
}
```
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] |
C-MTEB/T2Retrieval-qrels | 2023-07-28T10:11:11.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,788 | 2023-07-28T10:11:07 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 3133383
num_examples: 118932
download_size: 1146734
dataset_size: 3133383
---
# Dataset Card for "T2Retrieval-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 505 | [
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] |
BeIR/hotpotqa | 2022-10-23T06:02:40.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 2 | 1,786 | 2022-06-05T16:40:18 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
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] |
C-MTEB/DuRetrieval | 2023-07-28T09:48:49.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,781 | 2023-07-28T09:47:41 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 91213303
num_examples: 100001
- name: queries
num_bytes: 131354
num_examples: 2000
download_size: 64531170
dataset_size: 91344657
---
# Dataset Card for "DuRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 585 | [
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] |
tweettemposhift/tweet_temporal_shift | 2023-10-31T12:30:20.000Z | [
"region:us"
] | tweettemposhift | """
_TWEET_TEMPORAL_CITATION = | """
_TWEET_TOPIC_DESCRIPTION = | 0 | 1,776 | 2023-10-20T13:44:44 | # Tweet Temporal Shift Benchmark
| 33 | [
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] |
kyujinpy/KOpen-platypus | 2023-11-01T20:18:07.000Z | [
"size_categories:10K<n<100K",
"language:en",
"language:ko",
"license:cc-by-4.0",
"arxiv:2308.07317",
"region:us"
] | kyujinpy | null | null | 22 | 1,760 | 2023-08-21T14:59:26 | ---
language:
- en
- ko
license: cc-by-4.0
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 34213211
num_examples: 24926
download_size: 16662523
dataset_size: 34213211
---
# KOpenPlatypus: Korean Translation dataset about Open-Platypus
## Korean Translation Method
I use [DeepL-pro-API](https://www.deepl.com/ko/pro/change-plan?cta=header-pro#single) and selenium.
It takes about 140h times.
+) 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭
## Korean Translation post-processing
And also, applying post-processing. See below lists. (*약 2000개 이상의 코드 관련 데이터를 수작업으로 수정함)
1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정
2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존
3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴
4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음)
5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정
6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역
7. `고유명사`는 최대한 유지함
- 95% 이상의 번역 오류는 전부 고친 것으로 생각됨.
- 약 144h 정도 번역 작업을 진행함. (72h/72h; Translation/Post-processing)
## Introdcution
This dataset is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. It is comprised of the following datasets, which were filtered using keyword search and then Sentence Transformers to remove questions with a similarity above 80%:
| Dataset Name | License Type |
|--------------------------------------------------------------|--------------|
| [PRM800K](https://github.com/openai/prm800k) | MIT |
| [ScienceQA](https://github.com/lupantech/ScienceQA) | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| [SciBench](https://github.com/mandyyyyii/scibench) | MIT |
| [ReClor](https://whyu.me/reclor/) | Non-commercial |
| [TheoremQA](https://huggingface.co/datasets/wenhu/TheoremQA) | MIT |
| [`nuprl/leetcode-solutions-python-testgen-gpt4`](https://huggingface.co/datasets/nuprl/leetcode-solutions-python-testgen-gpt4/viewer/nuprl--leetcode-solutions-python-testgen-gpt4/train?p=1) | None listed |
| [`jondurbin/airoboros-gpt4-1.4.1`](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) | other |
| [`TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k`](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k/viewer/TigerResearch--tigerbot-kaggle-leetcodesolutions-en-2k/train?p=2) | apache-2.0 |
| [openbookQA](https://huggingface.co/datasets/openbookqa/viewer/additional/train?row=35) | apache-2.0 |
| [ARB](https://arb.duckai.org) | MIT |
| [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | apache-2.0 |
## Data Contamination Check
We've removed approximately 200 questions that appear in the Hugging Face benchmark test sets. Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information.
## Model Info
Please see models at [`garage-bAInd`](https://huggingface.co/garage-bAInd).
## Training and filtering code
Please see the [Platypus GitHub repo](https://github.com/arielnlee/Platypus).
## Citations
```bibtex
@article{platypus2023,
title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
booktitle={arXiv preprint arxiv:2308.07317},
year={2023}
}
```
```bibtex
@article{lightman2023lets,
title={Let's Verify Step by Step},
author={Lightman, Hunter and Kosaraju, Vineet and Burda, Yura and Edwards, Harri and Baker, Bowen and Lee, Teddy and Leike, Jan and Schulman, John and Sutskever, Ilya and Cobbe, Karl},
journal={preprint arXiv:2305.20050},
year={2023}
}
```
```bibtex
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}
```
```bibtex
@misc{wang2023scibench,
title={SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models},
author={Xiaoxuan Wang and Ziniu Hu and Pan Lu and Yanqiao Zhu and Jieyu Zhang and Satyen Subramaniam and Arjun R. Loomba and Shichang Zhang and Yizhou Sun and Wei Wang},
year={2023},
arXiv eprint 2307.10635
}
```
```bibtex
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year = {2020}
}
```
```bibtex
@article{chen2023theoremqa,
title={TheoremQA: A Theorem-driven Question Answering dataset},
author={Chen, Wenhu and Ming Yin, Max Ku, Elaine Wan, Xueguang Ma, Jianyu Xu, Tony Xia, Xinyi Wang, Pan Lu},
journal={preprint arXiv:2305.12524},
year={2023}
}
```
```bibtex
@inproceedings{OpenBookQA2018,
title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},
booktitle={EMNLP},
year={2018}
}
```
```bibtex
@misc{sawada2023arb,
title={ARB: Advanced Reasoning Benchmark for Large Language Models},
author={Tomohiro Sawada and Daniel Paleka and Alexander Havrilla and Pranav Tadepalli and Paula Vidas and Alexander Kranias and John J. Nay and Kshitij Gupta and Aran Komatsuzaki},
arXiv eprint 2307.13692,
year={2023}
}
``` | 6,313 | [
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ccdv/pubmed-summarization | 2022-10-24T20:33:04.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | ccdv | PubMed dataset for summarization.
From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al.
See: https://aclanthology.org/N18-2097.pdf
See: https://github.com/armancohan/long-summarization | @inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, Nazli",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2097",
doi = "10.18653/v1/N18-2097",
pages = "615--621",
abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.",
} | 32 | 1,757 | 2022-03-02T23:29:22 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# PubMed dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/armancohan/long-summarization).\
Note that original data are pre-tokenized so this dataset returns " ".join(text) and add "\n" for paragraphs. \
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/pubmed-summarization": ("article", "abstract")
```
### Data Fields
- `id`: paper id
- `article`: a string containing the body of the paper
- `abstract`: a string containing the abstract of the paper
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
Token counts are white space based.
| Dataset Split | Number of Instances | Avg. tokens |
| ------------- | --------------------|:----------------------|
| Train | 119,924 | 3043 / 215 |
| Validation | 6,633 | 3111 / 216 |
| Test | 6,658 | 3092 / 219 |
# Cite original article
```
@inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, Nazli",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2097",
doi = "10.18653/v1/N18-2097",
pages = "615--621",
abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.",
}
```
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sms_spam | 2023-01-25T14:44:29.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-nus-sms-corpus",
"language:en",
"license:unknown",
"region:us"
] | null | The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam. | @inproceedings{Almeida2011SpamFiltering,
title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
year={2011},
booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
} | 13 | 1,756 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- found
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-nus-sms-corpus
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: sms-spam-collection-data-set
pretty_name: SMS Spam Collection Data Set
dataset_info:
features:
- name: sms
dtype: string
- name: label
dtype:
class_label:
names:
'0': ham
'1': spam
config_name: plain_text
splits:
- name: train
num_bytes: 521756
num_examples: 5574
download_size: 203415
dataset_size: 521756
train-eval-index:
- config: plain_text
task: text-classification
task_id: binary_classification
splits:
train_split: train
col_mapping:
sms: 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
---
# 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://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
- **Repository:**
- **Paper:** Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (ACM DOCENG'11), Mountain View, CA, USA, 2011.
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sms: the sms message
- label: indicating if the sms message is ham or spam, ham means it is not spam
### 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
@inproceedings{Almeida2011SpamFiltering,
title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
year={2011},
booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
}
### Contributions
Thanks to [@czabo](https://github.com/czabo) for adding this dataset. | 4,872 | [
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] |
BeIR/nq | 2022-10-23T06:02:24.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 2 | 1,752 | 2022-06-05T16:37:56 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
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conll2012_ontonotesv5 | 2023-01-25T15:03:49.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"task_ids:coreference-resolution",
"task_ids:parsing",
"task_ids:lemmatization",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"language:en",
"language:zh",
"license:cc-by-nc-nd-4.0",
"semantic-role-labeling",
"region:us"
] | null | OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).
The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility.
See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1)
For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above. | @inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Zhong, Zhi},
booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-3516",
pages = "143--152",
}
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston. OntoNotes Release 5.0 LDC2013T19. Web Download. Philadelphia: Linguistic Data Consortium, 2013. | 25 | 1,751 | 2022-03-15T10:48:28 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
- en
- zh
license:
- cc-by-nc-nd-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
- coreference-resolution
- parsing
- lemmatization
- word-sense-disambiguation
paperswithcode_id: ontonotes-5-0
pretty_name: CoNLL2012 shared task data based on OntoNotes 5.0
tags:
- semantic-role-labeling
dataset_info:
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features:
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dtype: string
- name: sentences
list:
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dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence:
class_label:
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'1': '``'
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'40': VBD
'41': VBG
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sequence: float32
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sequence:
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- name: srl_frames
list:
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dtype: string
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sequence: string
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sequence:
sequence: int32
length: 3
splits:
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num_bytes: 112246121
num_examples: 1940
- name: validation
num_bytes: 14116925
num_examples: 222
- name: test
num_bytes: 14709044
num_examples: 222
download_size: 193644139
dataset_size: 141072090
- config_name: chinese_v4
features:
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dtype: string
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list:
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dtype: int32
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sequence: string
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sequence:
class_label:
names:
'0': X
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'18': LC
'19': M
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'21': NN
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'23': NT
'24': OD
'25': 'ON'
'26': P
'27': PN
'28': PU
'29': SB
'30': SP
'31': URL
'32': VA
'33': VC
'34': VE
'35': VV
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dtype: string
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sequence: string
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sequence: string
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sequence:
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'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 77195698
num_examples: 1391
- name: validation
num_bytes: 10828169
num_examples: 172
- name: test
num_bytes: 9585138
num_examples: 166
download_size: 193644139
dataset_size: 97609005
- config_name: arabic_v4
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence: string
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 42017761
num_examples: 359
- name: validation
num_bytes: 4859292
num_examples: 44
- name: test
num_bytes: 4900664
num_examples: 44
download_size: 193644139
dataset_size: 51777717
- config_name: english_v12
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': XX
'1': '``'
'2': $
'3': ''''''
'4': '*'
'5': ','
'6': -LRB-
'7': -RRB-
'8': .
'9': ':'
'10': ADD
'11': AFX
'12': CC
'13': CD
'14': DT
'15': EX
'16': FW
'17': HYPH
'18': IN
'19': JJ
'20': JJR
'21': JJS
'22': LS
'23': MD
'24': NFP
'25': NN
'26': NNP
'27': NNPS
'28': NNS
'29': PDT
'30': POS
'31': PRP
'32': PRP$
'33': RB
'34': RBR
'35': RBS
'36': RP
'37': SYM
'38': TO
'39': UH
'40': VB
'41': VBD
'42': VBG
'43': VBN
'44': VBP
'45': VBZ
'46': VERB
'47': WDT
'48': WP
'49': WP$
'50': WRB
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 174173192
num_examples: 10539
- name: validation
num_bytes: 24264804
num_examples: 1370
- name: test
num_bytes: 18254144
num_examples: 1200
download_size: 193644139
dataset_size: 216692140
---
# Dataset Card for CoNLL2012 shared task data based on OntoNotes 5.0
## 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:** [CoNLL-2012 Shared Task](https://conll.cemantix.org/2012/data.html), [Author's page](https://cemantix.org/data/ontonotes.html)
- **Repository:** [Mendeley](https://data.mendeley.com/datasets/zmycy7t9h9)
- **Paper:** [Towards Robust Linguistic Analysis using OntoNotes](https://aclanthology.org/W13-3516/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).
The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility.
See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1)
For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above.
### Supported Tasks and Leaderboards
- [Named Entity Recognition on Ontonotes v5 (English)](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ontonotes-v5)
- [Coreference Resolution on OntoNotes](https://paperswithcode.com/sota/coreference-resolution-on-ontonotes)
- [Semantic Role Labeling on OntoNotes](https://paperswithcode.com/sota/semantic-role-labeling-on-ontonotes)
- ...
### Languages
V4 data for Arabic, Chinese, English, and V12 data for English
## Dataset Structure
### Data Instances
```
{
{'document_id': 'nw/wsj/23/wsj_2311',
'sentences': [{'part_id': 0,
'words': ['CONCORDE', 'trans-Atlantic', 'flights', 'are', '$', '2, 'to', 'Paris', 'and', '$', '3, 'to', 'London', '.']},
'pos_tags': [25, 18, 27, 43, 2, 12, 17, 25, 11, 2, 12, 17, 25, 7],
'parse_tree': '(TOP(S(NP (NNP CONCORDE) (JJ trans-Atlantic) (NNS flights) )(VP (VBP are) (NP(NP(NP ($ $) (CD 2,400) )(PP (IN to) (NP (NNP Paris) ))) (CC and) (NP(NP ($ $) (CD 3,200) )(PP (IN to) (NP (NNP London) ))))) (. .) ))',
'predicate_lemmas': [None, None, None, 'be', None, None, None, None, None, None, None, None, None, None],
'predicate_framenet_ids': [None, None, None, '01', None, None, None, None, None, None, None, None, None, None],
'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None, None],
'speaker': None,
'named_entities': [7, 6, 0, 0, 0, 15, 0, 5, 0, 0, 15, 0, 5, 0],
'srl_frames': [{'frames': ['B-ARG1', 'I-ARG1', 'I-ARG1', 'B-V', 'B-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'O'],
'verb': 'are'}],
'coref_spans': [],
{'part_id': 0,
'words': ['In', 'a', 'Centennial', 'Journal', 'article', 'Oct.', '5', ',', 'the', 'fares', 'were', 'reversed', '.']}]}
'pos_tags': [17, 13, 25, 25, 24, 25, 12, 4, 13, 27, 40, 42, 7],
'parse_tree': '(TOP(S(PP (IN In) (NP (DT a) (NML (NNP Centennial) (NNP Journal) ) (NN article) ))(NP (NNP Oct.) (CD 5) ) (, ,) (NP (DT the) (NNS fares) )(VP (VBD were) (VP (VBN reversed) )) (. .) ))',
'predicate_lemmas': [None, None, None, None, None, None, None, None, None, None, None, 'reverse', None],
'predicate_framenet_ids': [None, None, None, None, None, None, None, None, None, None, None, '01', None],
'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None],
'speaker': None,
'named_entities': [0, 0, 4, 22, 0, 12, 30, 0, 0, 0, 0, 0, 0],
'srl_frames': [{'frames': ['B-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'B-ARGM-TMP', 'I-ARGM-TMP', 'O', 'B-ARG1', 'I-ARG1', 'O', 'B-V', 'O'],
'verb': 'reversed'}],
'coref_spans': [],
}
```
### Data Fields
- **`document_id`** (*`str`*): This is a variation on the document filename
- **`sentences`** (*`List[Dict]`*): All sentences of the same document are in a single example for the convenience of concatenating sentences.
Every element in `sentences` is a *`Dict`* composed of the following data fields:
- **`part_id`** (*`int`*) : Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
- **`words`** (*`List[str]`*) :
- **`pos_tags`** (*`List[ClassLabel]` or `List[str]`*) : This is the Penn-Treebank-style part of speech. When parse information is missing, all parts of speech except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag.
- tag set : Note tag sets below are founded by scanning all the data, and I found it seems to be a little bit different from officially stated tag sets. See official documents in the [Mendeley repo](https://data.mendeley.com/datasets/zmycy7t9h9)
- arabic : str. Because pos tag in Arabic is compounded and complex, hard to represent it by `ClassLabel`
- chinese v4 : `datasets.ClassLabel(num_classes=36, names=["X", "AD", "AS", "BA", "CC", "CD", "CS", "DEC", "DEG", "DER", "DEV", "DT", "ETC", "FW", "IJ", "INF", "JJ", "LB", "LC", "M", "MSP", "NN", "NR", "NT", "OD", "ON", "P", "PN", "PU", "SB", "SP", "URL", "VA", "VC", "VE", "VV",])`, where `X` is for pos tag missing
- english v4 : `datasets.ClassLabel(num_classes=49, names=["XX", "``", "$", "''", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`".
- english v12 : `datasets.ClassLabel(num_classes=51, names="english_v12": ["XX", "``", "$", "''", "*", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "VERB", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`".
- **`parse_tree`** (*`Optional[str]`*) : An serialized NLTK Tree representing the parse. It includes POS tags as pre-terminal nodes. When the parse information is missing, the parse will be `None`.
- **`predicate_lemmas`** (*`List[Optional[str]]`*) : The predicate lemma of the words for which we have semantic role information or word sense information. All other indices are `None`.
- **`predicate_framenet_ids`** (*`List[Optional[int]]`*) : The PropBank frameset ID of the lemmas in predicate_lemmas, or `None`.
- **`word_senses`** (*`List[Optional[float]]`*) : The word senses for the words in the sentence, or None. These are floats because the word sense can have values after the decimal, like 1.1.
- **`speaker`** (*`Optional[str]`*) : This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data. When it is not available, it will be `None`.
- **`named_entities`** (*`List[ClassLabel]`*) : The BIO tags for named entities in the sentence.
- tag set : `datasets.ClassLabel(num_classes=37, names=["O", "B-PERSON", "I-PERSON", "B-NORP", "I-NORP", "B-FAC", "I-FAC", "B-ORG", "I-ORG", "B-GPE", "I-GPE", "B-LOC", "I-LOC", "B-PRODUCT", "I-PRODUCT", "B-DATE", "I-DATE", "B-TIME", "I-TIME", "B-PERCENT", "I-PERCENT", "B-MONEY", "I-MONEY", "B-QUANTITY", "I-QUANTITY", "B-ORDINAL", "I-ORDINAL", "B-CARDINAL", "I-CARDINAL", "B-EVENT", "I-EVENT", "B-WORK_OF_ART", "I-WORK_OF_ART", "B-LAW", "I-LAW", "B-LANGUAGE", "I-LANGUAGE",])`
- **`srl_frames`** (*`List[{"word":str, "frames":List[str]}]`*) : A dictionary keyed by the verb in the sentence for the given Propbank frame labels, in a BIO format.
- **`coref spans`** (*`List[List[int]]`*) : The spans for entity mentions involved in coreference resolution within the sentence. Each element is a tuple composed of (cluster_id, start_index, end_index). Indices are inclusive.
### Data Splits
Each dataset (arabic_v4, chinese_v4, english_v4, english_v12) has 3 splits: _train_, _validation_, and _test_
## 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
```
@inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Zhong, Zhi},
booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-3516",
pages = "143--152",
}
```
### Contributions
Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset. | 22,908 | [
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dalle-mini/YFCC100M_OpenAI_subset | 2021-08-26T17:56:01.000Z | [
"arxiv:1503.01817",
"region:us"
] | dalle-mini | The YFCC100M is one of the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr, all of which were shared under one of the various Creative Commons licenses.
This version is a subset defined in openai/CLIP. | @article{thomee2016yfcc100m,
author = "Bart Thomee and David A. Shamma and Gerald Friedland and Benjamin Elizalde and Karl Ni and Douglas Poland and Damian Borth and Li-Jia Li",
title = "{YFCC100M}: The New Data in Multimedia Research",
journal = "Communications of the {ACM}",
volume = "59",
number = "2",
pages = "64--73",
year = "2016",
url = "http://cacm.acm.org/magazines/2016/2/197425-yfcc100m/fulltext",
} | 8 | 1,750 | 2022-03-02T23:29:22 | # YFCC100M subset from OpenAI
Subset of [YFCC100M](https://arxiv.org/abs/1503.01817) used by OpenAI for [CLIP](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md), filtered to contain only the images that we could retrieve.
| Split | train | validation |
| --- | --- | --- |
| Number of samples | 14,808,859 | 16,374 |
| Size | 1.9 TB | 2.1 GB |
Features:
* from the original dataset: `title`, `description`, `photoid`, `uid`, `unickname`, `datetaken`, `dateuploaded`, `capturedevice`, `usertags`, `machinetags`, `longitude`, `latitude`, `accuracy`, `pageurl`, `downloadurl`, `licensename`, `licenseurl`, `serverid`, `farmid`, `secret`, `secretoriginal`, `ext`, `marker`, `key`
* `img`: image content, can be loaded with `PIL.Image.open(io.BytesIO(item['img']))`
* `title_clean` and `description_clean`: derived from `title` and `description` using `clean_text` function detailed below
```python
def clean_text(text):
# decode url
text = urllib.parse.unquote_plus(text)
# remove html tags
text = re.sub('<[^<]+?>', '', text)
# remove multiple spaces + "\r" + "\n" + "\t"
text = " ".join(text.split())
return text
``` | 1,155 | [
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nielsr/ade20k-panoptic-demo | 2022-11-06T17:13:22.000Z | [
"region:us"
] | nielsr | null | null | 0 | 1,737 | 2022-11-05T21:16:00 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: segments_info
list:
- name: area
dtype: int64
- name: bbox
sequence: int64
- name: category_id
dtype: int64
- name: id
dtype: int64
- name: iscrowd
dtype: int64
splits:
- name: train
num_bytes: 492746.0
num_examples: 10
- name: validation
num_bytes: 461402.0
num_examples: 10
download_size: 949392
dataset_size: 954148.0
---
# Dataset Card for "ade20k-panoptic-demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 685 | [
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Dahoas/synthetic-instruct-gptj-pairwise | 2023-01-09T03:48:03.000Z | [
"region:us"
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facebook/multilingual_librispeech | 2023-02-13T11:33:31.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:nl",
"language:fr",
"language:it",
"language:es",
"language:pt",
"language:pl",
"license:cc-by-4.0",
"arxiv:2012.03411",
"region:us"
] | facebook | This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94)
to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research.
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages:
English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. | @article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
} | 28 | 1,731 | 2022-03-02T23:29:22 | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- nl
- fr
- it
- es
- pt
- pl
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: multilingual-librispeech
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
---
# Dataset Card for MultiLingual LibriSpeech
## 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 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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
### Dataset Summary
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
### Languages
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
```python
from datasets import load_dataset
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
print(next(iter(mls)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
Local:
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
dataloader = DataLoader(mls, batch_sampler=batch_sampler)
```
Streaming:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
dataloader = DataLoader(mls, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'file': '10900_6473_000030.flac',
'audio': {'path': '10900_6473_000030.flac',
'array': array([-1.52587891e-04, 6.10351562e-05, 0.00000000e+00, ...,
4.27246094e-04, 5.49316406e-04, 4.57763672e-04]),
'sampling_rate': 16000},
'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n',
'speaker_id': 10900,
'chapter_id': 6473,
'id': '10900_6473_000030'}
```
### Data Fields
- file: A filename .flac format.
- audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
| | Train | Train.9h | Train.1h | Dev | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
| french | 258213 | 2167 | 241 | 2416 | 2426 |
| spanish | 220701 | 2110 | 233 | 2408 | 2385 |
| italian | 59623 | 2173 | 240 | 1248 | 1262 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten)
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
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proteinea/secondary_structure_prediction | 2023-03-02T22:42:31.000Z | [
"doi:10.57967/hf/1104",
"region:us"
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scitail | 2023-04-05T13:39:52.000Z | [
"language:en",
"region:us"
] | null | The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
with neutral label | inproceedings{scitail,
Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
Booktitle = {AAAI},
Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
Year = {2018}
} | 4 | 1,729 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: scitail
pretty_name: SciTail
dataset_info:
- config_name: snli_format
features:
- name: sentence1_binary_parse
dtype: string
- name: sentence1_parse
dtype: string
- name: sentence1
dtype: string
- name: sentence2_parse
dtype: string
- name: sentence2
dtype: string
- name: annotator_labels
sequence: string
- name: gold_label
dtype: string
splits:
- name: train
num_bytes: 22495833
num_examples: 23596
- name: test
num_bytes: 2008631
num_examples: 2126
- name: validation
num_bytes: 1266529
num_examples: 1304
download_size: 14174621
dataset_size: 25770993
- config_name: tsv_format
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
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num_bytes: 4618115
num_examples: 23097
- name: test
num_bytes: 411343
num_examples: 2126
- name: validation
num_bytes: 261086
num_examples: 1304
download_size: 14174621
dataset_size: 5290544
- config_name: dgem_format
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
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dtype: string
splits:
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num_examples: 23088
- name: test
num_bytes: 608213
num_examples: 2126
- name: validation
num_bytes: 394040
num_examples: 1304
download_size: 14174621
dataset_size: 7834357
- config_name: predictor_format
features:
- name: answer
dtype: string
- name: sentence2_structure
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: gold_label
dtype: string
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dtype: string
splits:
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num_bytes: 8884823
num_examples: 23587
- name: test
num_bytes: 797161
num_examples: 2126
- name: validation
num_bytes: 511305
num_examples: 1304
download_size: 14174621
dataset_size: 10193289
---
# Dataset Card for "scitail"
## 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://allenai.org/data/scitail](https://allenai.org/data/scitail)
- **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:** 56.70 MB
- **Size of the generated dataset:** 49.09 MB
- **Total amount of disk used:** 105.79 MB
### Dataset Summary
The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We
crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create
the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples
with neutral label
### 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
#### dgem_format
- **Size of downloaded dataset files:** 14.18 MB
- **Size of the generated dataset:** 7.83 MB
- **Total amount of disk used:** 22.01 MB
An example of 'train' looks as follows.
```
```
#### predictor_format
- **Size of downloaded dataset files:** 14.18 MB
- **Size of the generated dataset:** 10.19 MB
- **Total amount of disk used:** 24.37 MB
An example of 'validation' looks as follows.
```
```
#### snli_format
- **Size of downloaded dataset files:** 14.18 MB
- **Size of the generated dataset:** 25.77 MB
- **Total amount of disk used:** 39.95 MB
An example of 'validation' looks as follows.
```
```
#### tsv_format
- **Size of downloaded dataset files:** 14.18 MB
- **Size of the generated dataset:** 5.30 MB
- **Total amount of disk used:** 19.46 MB
An example of 'validation' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### dgem_format
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a `string` feature.
- `hypothesis_graph_structure`: a `string` feature.
#### predictor_format
- `answer`: a `string` feature.
- `sentence2_structure`: a `string` feature.
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `gold_label`: a `string` feature.
- `question`: a `string` feature.
#### snli_format
- `sentence1_binary_parse`: a `string` feature.
- `sentence1_parse`: a `string` feature.
- `sentence1`: a `string` feature.
- `sentence2_parse`: a `string` feature.
- `sentence2`: a `string` feature.
- `annotator_labels`: a `list` of `string` features.
- `gold_label`: a `string` feature.
#### tsv_format
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a `string` feature.
### Data Splits
| name |train|validation|test|
|----------------|----:|---------:|---:|
|dgem_format |23088| 1304|2126|
|predictor_format|23587| 1304|2126|
|snli_format |23596| 1304|2126|
|tsv_format |23097| 1304|2126|
## 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{scitail,
Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
Booktitle = {AAAI},
Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
Year = {2018}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | 9,299 | [
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] |
beomi/KoAlpaca-v1.1a | 2023-05-26T06:32:02.000Z | [
"task_categories:text-generation",
"language:ko",
"KoAlpaca",
"region:us"
] | beomi | null | null | 10 | 1,729 | 2023-05-26T06:27:44 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 23371027
num_examples: 21155
download_size: 12856014
dataset_size: 23371027
task_categories:
- text-generation
language:
- ko
tags:
- KoAlpaca
pretty_name: KoAlpaca-v1.1a
---
# Dataset Card for "KoAlpaca-v1.1a"
## Project Repo
- Github Repo: [Beomi/KoAlpaca](https://github.com/Beomi/KoAlpaca)
## How to use
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("beomi/KoAlpaca-v1.1a", split="train")
>>> ds
Dataset({
features: ['instruction', 'input', 'output'],
num_rows: 21155
})
```
```python
>>> ds[0]
{'instruction': '양파는 어떤 식물 부위인가요? 그리고 고구마는 뿌리인가요?',
'output': '양파는 잎이 아닌 식물의 줄기 부분입니다. 고구마는 식물의 뿌리 부분입니다. \n\n식물의 부위의 구분에 대해 궁금해하는 분이라면 분명 이 질문에 대한 답을 찾고 있을 것입니다. 양파는 잎이 아닌 줄기 부분입니다. 고구마는 다른 질문과 답변에서 언급된 것과 같이 뿌리 부분입니다. 따라서, 양파는 식물의 줄기 부분이 되고, 고구마는 식물의 뿌리 부분입니다.\n\n 덧붙이는 답변: 고구마 줄기도 볶아먹을 수 있나요? \n\n고구마 줄기도 식용으로 볶아먹을 수 있습니다. 하지만 줄기 뿐만 아니라, 잎, 씨, 뿌리까지 모든 부위가 식용으로 활용되기도 합니다. 다만, 한국에서는 일반적으로 뿌리 부분인 고구마를 주로 먹습니다.',
'url': 'https://kin.naver.com/qna/detail.naver?d1id=11&dirId=1116&docId=55320268'}
``` | 1,226 | [
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vipulgupta/CALM | 2023-08-24T00:03:32.000Z | [
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] | vipulgupta | Bias Dataset | null | 1 | 1,724 | 2023-08-23T23:49:51 | Entry not found | 15 | [
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XiaHan19/cmmlu | 2023-10-20T19:55:23.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-4.0",
"chinese",
"llm",
"evaluation",
"arxiv:2306.09212",
"region:us"
] | XiaHan19 | CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context. | @misc{li2023cmmlu,
title={CMMLU: Measuring massive multitask language understanding in Chinese},
author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
year={2023},
eprint={2306.09212},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 0 | 1,718 | 2023-10-20T14:06:00 | ---
license: cc-by-nc-4.0
task_categories:
- multiple-choice
- question-answering
language:
- zh
tags:
- chinese
- llm
- evaluation
pretty_name: CMMLU
size_categories:
- 10K<n<100K
---
# CMMLU: Measuring massive multitask language understanding in Chinese
- **Homepage:** [https://github.com/haonan-li/CMMLU](https://github.com/haonan-li/CMMLU)
- **Repository:** [https://huggingface.co/datasets/haonan-li/cmmlu](https://huggingface.co/datasets/haonan-li/cmmlu)
- **Paper:** [CMMLU: Measuring Chinese Massive Multitask Language Understanding](https://arxiv.org/abs/2306.09212).
## Table of Contents
- [Introduction](#introduction)
- [Leaderboard](#leaderboard)
- [Data](#data)
- [Citation](#citation)
- [License](#license)
## Introduction
CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context.
CMMLU covers a wide range of subjects, comprising 67 topics that span from elementary to advanced professional levels. It includes subjects that require computational expertise, such as physics and mathematics, as well as disciplines within humanities and social sciences.
Many of these tasks are not easily translatable from other languages due to their specific contextual nuances and wording.
Furthermore, numerous tasks within CMMLU have answers that are specific to China and may not be universally applicable or considered correct in other regions or languages.
## Leaderboard
Latest leaderboard is in our [github](https://github.com/haonan-li/CMMLU).
## Data
We provide development and test dataset for each of 67 subjects, with 5 questions in development set and 100+ quesitons in test set.
Each question in the dataset is a multiple-choice questions with 4 choices and only one choice as the correct answer.
Here are two examples:
```
题目：同一物种的两类细胞各产生一种分泌蛋白，组成这两种蛋白质的各种氨基酸含量相同，但排列顺序不同。其原因是参与这两种蛋白质合成的：
A. tRNA种类不同
B. 同一密码子所决定的氨基酸不同
C. mRNA碱基序列不同
D. 核糖体成分不同
答案是：C
```
```
题目：某种植物病毒V是通过稻飞虱吸食水稻汁液在水稻间传播的。稻田中青蛙数量的增加可减少该病毒在水稻间的传播。下列叙述正确的是：
A. 青蛙与稻飞虱是捕食关系
B. 水稻和病毒V是互利共生关系
C. 病毒V与青蛙是寄生关系
D. 水稻与青蛙是竞争关系
答案是：
```
#### Load data
```python
from datasets import load_dataset
cmmlu=load_dataset(r"haonan-li/cmmlu", 'agronomy')
print(cmmlu['test'][0])
```
#### Load all data at once
```python
task_list = ['agronomy', 'anatomy', 'ancient_chinese', 'arts', 'astronomy', 'business_ethics', 'chinese_civil_service_exam', 'chinese_driving_rule', 'chinese_food_culture', 'chinese_foreign_policy', 'chinese_history', 'chinese_literature',
'chinese_teacher_qualification', 'clinical_knowledge', 'college_actuarial_science', 'college_education', 'college_engineering_hydrology', 'college_law', 'college_mathematics', 'college_medical_statistics', 'college_medicine', 'computer_science',
'computer_security', 'conceptual_physics', 'construction_project_management', 'economics', 'education', 'electrical_engineering', 'elementary_chinese', 'elementary_commonsense', 'elementary_information_and_technology', 'elementary_mathematics',
'ethnology', 'food_science', 'genetics', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_geography', 'high_school_mathematics', 'high_school_physics', 'high_school_politics', 'human_sexuality',
'international_law', 'journalism', 'jurisprudence', 'legal_and_moral_basis', 'logical', 'machine_learning', 'management', 'marketing', 'marxist_theory', 'modern_chinese', 'nutrition', 'philosophy', 'professional_accounting', 'professional_law',
'professional_medicine', 'professional_psychology', 'public_relations', 'security_study', 'sociology', 'sports_science', 'traditional_chinese_medicine', 'virology', 'world_history', 'world_religions']
from datasets import load_dataset
cmmlu = {k: load_dataset(r"haonan-li/cmmlu", k) for k in task_list}
```
## Citation
```
@misc{li2023cmmlu,
title={CMMLU: Measuring massive multitask language understanding in Chinese},
author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
year={2023},
eprint={2306.09212},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
The CMMLU dataset is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
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] |
reuters21578 | 2023-08-30T17:35:01.000Z | [
"language:en",
"license:other",
"region:us"
] | null | The Reuters-21578 dataset is one of the most widely used data collections for text
categorization research. It is collected from the Reuters financial newswire service in 1987. | @article{APTE94,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Automated Learning of Decision Rules for Text Categorization},
journal = {ACM Transactions on Information Systems},
year = {1994},
note = {To appear.}
}
@inproceedings{APTE94b,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Toward Language Independent Automated Learning of Text Categorization Models},
booktitle = {sigir94},
year = {1994},
note = {To appear.}
}
@inproceedings{HAYES8},
author = {Philip J. Hayes and Peggy M. Anderson and Irene B. Nirenburg and
Linda M. Schmandt},
title = {{TCS}: A Shell for Content-Based Text Categorization},
booktitle = {IEEE Conference on Artificial Intelligence Applications},
year = {1990}
}
@inproceedings{HAYES90b,
author = {Philip J. Hayes and Steven P. Weinstein},
title = {{CONSTRUE/TIS:} A System for Content-Based Indexing of a
Database of News Stories},
booktitle = {Second Annual Conference on Innovative Applications of
Artificial Intelligence},
year = {1990}
}
@incollection{HAYES92 ,
author = {Philip J. Hayes},
title = {Intelligent High-Volume Text Processing using Shallow,
Domain-Specific Techniques},
booktitle = {Text-Based Intelligent Systems},
publisher = {Lawrence Erlbaum},
address = {Hillsdale, NJ},
year = {1992},
editor = {Paul S. Jacobs}
}
@inproceedings{LEWIS91c ,
author = {David D. Lewis},
title = {Evaluating Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1991},
month = {feb},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {312--318}
}
@phdthesis{LEWIS91d,
author = {David Dolan Lewis},
title = {Representation and Learning in Information Retrieval},
school = {Computer Science Dept.; Univ. of Massachusetts; Amherst, MA 01003},
year = 1992},
note = {Technical Report 91--93.}
}
@inproceedings{LEWIS91e,
author = {David D. Lewis},
title = {Data Extraction as Text Categorization: An Experiment with
the {MUC-3} Corpus},
booktitle = {Proceedings of the Third Message Understanding Evaluation
and Conference},
year = {1991},
month = {may},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}
@inproceedings{LEWIS92b,
author = {David D. Lewis},
title = {An Evaluation of Phrasal and Clustered Representations on a Text
Categorization Task},
booktitle = {Fifteenth Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval},
year = {1992},
pages = {37--50}
}
@inproceedings{LEWIS92d ,
author = {David D. Lewis and Richard M. Tong},
title = {Text Filtering in {MUC-3} and {MUC-4}},
booktitle = {Proceedings of the Fourth Message Understanding Conference ({MUC-4})},
year = {1992},
month = {jun},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}
@inproceedings{LEWIS92e,
author = {David D. Lewis},
title = {Feature Selection and Feature Extraction for Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1992},
month = {feb} ,
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {212--217}
}
@inproceedings{LEWIS94b,
author = {David D. Lewis and Marc Ringuette},
title = {A Comparison of Two Learning Algorithms for Text Categorization},
booktitle = {Symposium on Document Analysis and Information Retrieval},
year = {1994},
organization = {ISRI; Univ. of Nevada, Las Vegas},
address = {Las Vegas, NV},
month = {apr},
pages = {81--93}
}
@article{LEWIS94d,
author = {David D. Lewis and Philip J. Hayes},
title = {Guest Editorial},
journal = {ACM Transactions on Information Systems},
year = {1994},
volume = {12},
number = {3},
pages = {231},
month = {jul}
}
@article{SPARCKJONES76,
author = {K. {Sparck Jones} and C. J. {van Rijsbergen}},
title = {Information Retrieval Test Collections},
journal = {Journal of Documentation},
year = {1976},
volume = {32},
number = {1},
pages = {59--75}
}
@book{WEISS91,
author = {Sholom M. Weiss and Casimir A. Kulikowski},
title = {Computer Systems That Learn},
publisher = {Morgan Kaufmann},
year = {1991},
address = {San Mateo, CA}
} | 8 | 1,717 | 2022-03-02T23:29:22 | ---
language:
- en
license: other
paperswithcode_id: reuters-21578
pretty_name: Reuters-21578 Text Categorization Collection
dataset_info:
- config_name: ModApte
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
dtype: string
- name: cgis_split
dtype: string
- name: old_id
dtype: string
- name: new_id
dtype: string
- name: places
sequence: string
- name: people
sequence: string
- name: orgs
sequence: string
- name: exchanges
sequence: string
- name: date
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 2971653
num_examples: 3299
- name: train
num_bytes: 9161179
num_examples: 9603
- name: unused
num_bytes: 948244
num_examples: 722
download_size: 8150596
dataset_size: 13081076
- config_name: ModHayes
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
dtype: string
- name: cgis_split
dtype: string
- name: old_id
dtype: string
- name: new_id
dtype: string
- name: places
sequence: string
- name: people
sequence: string
- name: orgs
sequence: string
- name: exchanges
sequence: string
- name: date
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 948244
num_examples: 722
- name: train
num_bytes: 19071106
num_examples: 20856
download_size: 8150596
dataset_size: 20019350
- config_name: ModLewis
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
dtype: string
- name: cgis_split
dtype: string
- name: old_id
dtype: string
- name: new_id
dtype: string
- name: places
sequence: string
- name: people
sequence: string
- name: orgs
sequence: string
- name: exchanges
sequence: string
- name: date
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 5400506
num_examples: 6188
- name: train
num_bytes: 12994591
num_examples: 13625
- name: unused
num_bytes: 948244
num_examples: 722
download_size: 8150596
dataset_size: 19343341
---
# Dataset Card for "reuters21578"
## 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://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection
- **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:** 24.45 MB
- **Size of the generated dataset:** 52.22 MB
- **Total amount of disk used:** 76.67 MB
### Dataset Summary
The Reuters-21578 dataset is one of the most widely used data collections for text
categorization research. It is collected from the Reuters financial newswire service in 1987.
### 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
#### ModApte
- **Size of downloaded dataset files:** 8.15 MB
- **Size of the generated dataset:** 13.05 MB
- **Total amount of disk used:** 21.21 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"cgis_split": "\"TRAINING-SET\"",
"date": "19-MAR-1987 06:17:22.36",
"exchanges": [],
"lewis_split": "\"TRAIN\"",
"new_id": "\"7001\"",
"old_id": "\"11914\"",
"orgs": [],
"people": [],
"places": ["australia"],
"text": "\"Media group John Fairfax Ltd <FFXA.S>\\nsaid that its flat first half net profit partly reflected the\\nimpact of changes in t...",
"title": "FAIRFAX SAYS HIGHER TAX HITS FIRST HALF EARNINGS",
"topics": ["earn"]
}
```
#### ModHayes
- **Size of downloaded dataset files:** 8.15 MB
- **Size of the generated dataset:** 19.79 MB
- **Total amount of disk used:** 27.93 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"cgis_split": "\"TRAINING-SET\"",
"date": "19-OCT-1987 23:49:31.45",
"exchanges": [],
"lewis_split": "\"TEST\"",
"new_id": "\"20001\"",
"old_id": "\"20596\"",
"orgs": [],
"people": [],
"places": ["japan", "usa"],
"text": "\"If the dollar goes the way of Wall Street,\\nJapanese will finally move out of dollar investments in a\\nserious way, Japan inves...",
"title": "IF DOLLAR FOLLOWS WALL STREET JAPANESE WILL DIVEST",
"topics": ["money-fx"]
}
```
#### ModLewis
- **Size of downloaded dataset files:** 8.15 MB
- **Size of the generated dataset:** 19.38 MB
- **Total amount of disk used:** 27.54 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"cgis_split": "\"TRAINING-SET\"",
"date": "19-MAR-1987 06:17:22.36",
"exchanges": [],
"lewis_split": "\"TRAIN\"",
"new_id": "\"7001\"",
"old_id": "\"11914\"",
"orgs": [],
"people": [],
"places": ["australia"],
"text": "\"Media group John Fairfax Ltd <FFXA.S>\\nsaid that its flat first half net profit partly reflected the\\nimpact of changes in t...",
"title": "FAIRFAX SAYS HIGHER TAX HITS FIRST HALF EARNINGS",
"topics": ["earn"]
}
```
### Data Fields
The data fields are the same among all splits.
#### ModApte
- `text`: a `string` feature.
- `topics`: a `list` of `string` features.
- `lewis_split`: a `string` feature.
- `cgis_split`: a `string` feature.
- `old_id`: a `string` feature.
- `new_id`: a `string` feature.
- `places`: a `list` of `string` features.
- `people`: a `list` of `string` features.
- `orgs`: a `list` of `string` features.
- `exchanges`: a `list` of `string` features.
- `date`: a `string` feature.
- `title`: a `string` feature.
#### ModHayes
- `text`: a `string` feature.
- `topics`: a `list` of `string` features.
- `lewis_split`: a `string` feature.
- `cgis_split`: a `string` feature.
- `old_id`: a `string` feature.
- `new_id`: a `string` feature.
- `places`: a `list` of `string` features.
- `people`: a `list` of `string` features.
- `orgs`: a `list` of `string` features.
- `exchanges`: a `list` of `string` features.
- `date`: a `string` feature.
- `title`: a `string` feature.
#### ModLewis
- `text`: a `string` feature.
- `topics`: a `list` of `string` features.
- `lewis_split`: a `string` feature.
- `cgis_split`: a `string` feature.
- `old_id`: a `string` feature.
- `new_id`: a `string` feature.
- `places`: a `list` of `string` features.
- `people`: a `list` of `string` features.
- `orgs`: a `list` of `string` features.
- `exchanges`: a `list` of `string` features.
- `date`: a `string` feature.
- `title`: a `string` feature.
### Data Splits
#### ModApte
| |train|unused|test|
|-------|----:|-----:|---:|
|ModApte| 8762| 720|3009|
#### ModHayes
| |train|test|
|--------|----:|---:|
|ModHayes|18323| 720|
#### ModLewis
| |train|unused|test|
|--------|----:|-----:|---:|
|ModLewis|12449| 720|5458|
## 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
According to the dataset website (https://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection),
this dataset is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
(C BY 4.0) license.
However, the source data file contains a `README.txt` file with the following information under the
**Copyright & Notification** section:
> The copyright for the text of newswire articles and Reuters
annotations in the Reuters-21578 collection resides with Reuters Ltd.
Reuters Ltd. and Carnegie Group, Inc. have agreed to allow the free
distribution of this data *for research purposes only*.
> If you publish results based on this data set, please acknowledge
its use, refer to the data set by the name "Reuters-21578,
Distribution 1.0", and inform your readers of the current location of
the data set (see "Availability & Questions").
### Citation Information
```
@article{APTE94,
author = {Chidanand Apt{'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Automated Learning of Decision Rules for Text Categorization},
journal = {ACM Transactions on Information Systems},
year = {1994},
note = {To appear.}
}
@inproceedings{APTE94b,
author = {Chidanand Apt{'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Toward Language Independent Automated Learning of Text Categorization Models},
booktitle = {sigir94},
year = {1994},
note = {To appear.}
}
@inproceedings{HAYES8},
author = {Philip J. Hayes and Peggy M. Anderson and Irene B. Nirenburg and
Linda M. Schmandt},
title = {{TCS}: A Shell for Content-Based Text Categorization},
booktitle = {IEEE Conference on Artificial Intelligence Applications},
year = {1990}
}
@inproceedings{HAYES90b,
author = {Philip J. Hayes and Steven P. Weinstein},
title = {{CONSTRUE/TIS:} A System for Content-Based Indexing of a
Database of News Stories},
booktitle = {Second Annual Conference on Innovative Applications of
Artificial Intelligence},
year = {1990}
}
@incollection{HAYES92 ,
author = {Philip J. Hayes},
title = {Intelligent High-Volume Text Processing using Shallow,
Domain-Specific Techniques},
booktitle = {Text-Based Intelligent Systems},
publisher = {Lawrence Erlbaum},
address = {Hillsdale, NJ},
year = {1992},
editor = {Paul S. Jacobs}
}
@inproceedings{LEWIS91c ,
author = {David D. Lewis},
title = {Evaluating Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1991},
month = {feb},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {312--318}
}
@phdthesis{LEWIS91d,
author = {David Dolan Lewis},
title = {Representation and Learning in Information Retrieval},
school = {Computer Science Dept.; Univ. of Massachusetts; Amherst, MA 01003},
year = 1992},
note = {Technical Report 91--93.}
}
@inproceedings{LEWIS91e,
author = {David D. Lewis},
title = {Data Extraction as Text Categorization: An Experiment with
the {MUC-3} Corpus},
booktitle = {Proceedings of the Third Message Understanding Evaluation
and Conference},
year = {1991},
month = {may},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}
@inproceedings{LEWIS92b,
author = {David D. Lewis},
title = {An Evaluation of Phrasal and Clustered Representations on a Text
Categorization Task},
booktitle = {Fifteenth Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval},
year = {1992},
pages = {37--50}
}
@inproceedings{LEWIS92d ,
author = {David D. Lewis and Richard M. Tong},
title = {Text Filtering in {MUC-3} and {MUC-4}},
booktitle = {Proceedings of the Fourth Message Understanding Conference ({MUC-4})},
year = {1992},
month = {jun},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}
@inproceedings{LEWIS92e,
author = {David D. Lewis},
title = {Feature Selection and Feature Extraction for Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1992},
month = {feb} ,
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {212--217}
}
@inproceedings{LEWIS94b,
author = {David D. Lewis and Marc Ringuette},
title = {A Comparison of Two Learning Algorithms for Text Categorization},
booktitle = {Symposium on Document Analysis and Information Retrieval},
year = {1994},
organization = {ISRI; Univ. of Nevada, Las Vegas},
address = {Las Vegas, NV},
month = {apr},
pages = {81--93}
}
@article{LEWIS94d,
author = {David D. Lewis and Philip J. Hayes},
title = {Guest Editorial},
journal = {ACM Transactions on Information Systems},
year = {1994},
volume = {12},
number = {3},
pages = {231},
month = {jul}
}
@article{SPARCKJONES76,
author = {K. {Sparck Jones} and C. J. {van Rijsbergen}},
title = {Information Retrieval Test Collections},
journal = {Journal of Documentation},
year = {1976},
volume = {32},
number = {1},
pages = {59--75}
}
@book{WEISS91,
author = {Sholom M. Weiss and Casimir A. Kulikowski},
title = {Computer Systems That Learn},
publisher = {Morgan Kaufmann},
year = {1991},
address = {San Mateo, CA}
}
```
### Contributions
Thanks to [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 15,960 | [
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laion/dalle-3-dataset | 2023-11-03T01:05:26.000Z | [
"language:en",
"license:cc0-1.0",
"image-text-dataset",
"synthetic-dataset",
"region:us"
] | laion | null | null | 173 | 1,717 | 2023-10-06T18:11:38 | ---
language:
- en
license:
- cc0-1.0
tags:
- image-text-dataset
- synthetic-dataset
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 5935576124.43196
num_examples: 3790
download_size: 6135429015
dataset_size: 5935576124.43196
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for LAION DALL·E 3 Discord Dataset
**Description**: This dataset consists of prompt and image URL pairs scraped from the LAION [share-dalle-3 discord channel](https://discord.com/channels/823813159592001537/1158354590463447092). The purpose is to collect image-text pairs for research and exploration.
**Source Code**: The code used to generate this data can be found [here](https://github.com/LAION-AI/Discord-Scrapers.git).
## Contributors
- [Zach Nagengast](https://github.com/ZachNagengast)
- [Eduardo Pach](https://github.com/EduardoPach)
- [Seva Maltsev](https://github.com/TwoAbove)
- The [LAION community](https://discord.com/invite/eq3cAMZtCC)
## Data Attributes
- **caption**: The text description or prompt associated with the image. Data type: string.
- **image**: The embedded image data from the discord message attachment. Data type: image.
- **link**: The URL to the associated image. Data type: string.
- **message_id**: The discord message id where the image was posted. Data type: string.
- **timestamp**: Time the original message was posted. Datatype: string. | 1,623 | [
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] |
yitingxie/rlhf-reward-datasets | 2023-01-01T12:23:04.000Z | [
"region:us"
] | yitingxie | null | null | 44 | 1,716 | 2023-01-01T12:22:09 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: test
num_bytes: 6093563
num_examples: 5103
- name: train
num_bytes: 90528217
num_examples: 76256
download_size: 57138483
dataset_size: 96621780
---
# Dataset Card for "rlhf-reward-datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 501 | [
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DeveloperOats/DBPedia_Classes | 2022-08-08T14:54:42.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"region:us"
] | DeveloperOats | null | null | 13 | 1,713 | 2022-08-08T09:15:05 | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: 'DBpedia'
size_categories:
- 1M<n<10M
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids:
- topic-classification
---
About Dataset
DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in Wikipedia.
This is an extract of the data (after cleaning, kernel included) that provides taxonomic, hierarchical categories ("classes") for 342,782 wikipedia articles. There are 3 levels, with 9, 70 and 219 classes respectively.
A version of this dataset is a popular baseline for NLP/text classification tasks. This version of the dataset is much tougher, especially if the L2/L3 levels are used as the targets.
This is an excellent benchmark for hierarchical multiclass/multilabel text classification.
Some example approaches are included as code snippets.
Content
DBPedia dataset with multiple levels of hierarchy/classes, as a multiclass dataset.
Original DBPedia ontology (triplets data): https://wiki.dbpedia.org/develop/datasets
Listing of the class tree/taxonomy: http://mappings.dbpedia.org/server/ontology/classes/
Acknowledgements
Thanks to the Wikimedia foundation for creating Wikipedia, DBPedia and associated open-data goodness!
Thanks to my colleagues at Sparkbeyond (https://www.sparkbeyond.com) for pointing me towards the taxonomical version of this dataset (as opposed to the classic 14 class version)
Inspiration
Try different NLP models.
See also https://www.kaggle.com/datasets/danofer/dbpedia-classes
Compare to the SOTA in Text Classification on DBpedia - https://paperswithcode.com/sota/text-classification-on-dbpedia | 1,766 | [
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] |
ade_corpus_v2 | 2023-06-01T14:59:53.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:coreference-resolution",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs and dosages.
ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain any drug-related adverse effects. | @article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Language Processing in Pharmacogenomics",
issn = "1532-0464",
doi = "https://doi.org/10.1016/j.jbi.2012.04.008",
url = "http://www.sciencedirect.com/science/article/pii/S1532046412000615",
author = "Harsha Gurulingappa and Abdul Mateen Rajput and Angus Roberts and Juliane Fluck and Martin Hofmann-Apitius and Luca Toldo",
keywords = "Adverse drug effect, Benchmark corpus, Annotation, Harmonization, Sentence classification",
abstract = "A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F1 score of 0.70 indicating a potential useful application of the corpus."
} | 19 | 1,704 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids:
- coreference-resolution
- fact-checking
pretty_name: Adverse Drug Reaction Data v2
dataset_info:
- config_name: Ade_corpus_v2_classification
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not-Related
'1': Related
splits:
- name: train
num_bytes: 3403711
num_examples: 23516
download_size: 3791162
dataset_size: 3403711
- config_name: Ade_corpus_v2_drug_ade_relation
features:
- name: text
dtype: string
- name: drug
dtype: string
- name: effect
dtype: string
- name: indexes
struct:
- name: drug
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
- name: effect
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
splits:
- name: train
num_bytes: 1546021
num_examples: 6821
download_size: 3791162
dataset_size: 1546021
- config_name: Ade_corpus_v2_drug_dosage_relation
features:
- name: text
dtype: string
- name: drug
dtype: string
- name: dosage
dtype: string
- name: indexes
struct:
- name: drug
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
- name: dosage
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
splits:
- name: train
num_bytes: 64725
num_examples: 279
download_size: 3791162
dataset_size: 64725
train-eval-index:
- config: Ade_corpus_v2_classification
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
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
config_names:
- Ade_corpus_v2_classification
- Ade_corpus_v2_drug_ade_relation
- Ade_corpus_v2_drug_dosage_relation
---
# Dataset Card for Adverse Drug Reaction Data v2
## 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.sciencedirect.com/science/article/pii/S1532046412000615
- **Repository:** [Needs More Information]
- **Paper:** https://www.sciencedirect.com/science/article/pii/S1532046412000615
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs and dosages.
ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain any drug-related adverse effects.
### Supported Tasks and Leaderboards
Sentiment classification, Relation Extraction
### Languages
English
## Dataset Structure
### Data Instances
#### Config - `Ade_corpus_v2_classification`
```
{
'label': 1,
'text': 'Intravenous azithromycin-induced ototoxicity.'
}
```
#### Config - `Ade_corpus_v2_drug_ade_relation`
```
{
'drug': 'azithromycin',
'effect': 'ototoxicity',
'indexes': {
'drug': {
'end_char': [24],
'start_char': [12]
},
'effect': {
'end_char': [44],
'start_char': [33]
}
},
'text': 'Intravenous azithromycin-induced ototoxicity.'
}
```
#### Config - `Ade_corpus_v2_drug_dosage_relation`
```
{
'dosage': '4 times per day',
'drug': 'insulin',
'indexes': {
'dosage': {
'end_char': [56],
'start_char': [41]
},
'drug': {
'end_char': [40],
'start_char': [33]}
},
'text': 'She continued to receive regular insulin 4 times per day over the following 3 years with only occasional hives.'
}
```
### Data Fields
#### Config - `Ade_corpus_v2_classification`
- `text` - Input text.
- `label` - Whether the adverse drug effect(ADE) related (1) or not (0).
-
#### Config - `Ade_corpus_v2_drug_ade_relation`
- `text` - Input text.
- `drug` - Name of drug.
- `effect` - Effect caused by the drug.
- `indexes.drug.start_char` - Start index of `drug` string in text.
- `indexes.drug.end_char` - End index of `drug` string in text.
- `indexes.effect.start_char` - Start index of `effect` string in text.
- `indexes.effect.end_char` - End index of `effect` string in text.
#### Config - `Ade_corpus_v2_drug_dosage_relation`
- `text` - Input text.
- `drug` - Name of drug.
- `dosage` - Dosage of the drug.
- `indexes.drug.start_char` - Start index of `drug` string in text.
- `indexes.drug.end_char` - End index of `drug` string in text.
- `indexes.dosage.start_char` - Start index of `dosage` string in text.
- `indexes.dosage.end_char` - End index of `dosage` string in text.
### Data Splits
| Train |
| ------ |
| 23516 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Language Processing in Pharmacogenomics",
issn = "1532-0464",
doi = "https://doi.org/10.1016/j.jbi.2012.04.008",
url = "http://www.sciencedirect.com/science/article/pii/S1532046412000615",
author = "Harsha Gurulingappa and Abdul Mateen Rajput and Angus Roberts and Juliane Fluck and Martin Hofmann-Apitius and Luca Toldo",
keywords = "Adverse drug effect, Benchmark corpus, Annotation, Harmonization, Sentence classification",
abstract = "A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F1 score of 0.70 indicating a potential useful application of the corpus."
}
```
### Contributions
Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 9,841 | [
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] |
lucadiliello/naturalquestionsshortqa | 2023-06-06T08:35:50.000Z | [
"region:us"
] | lucadiliello | null | null | 2 | 1,694 | 2023-02-25T18:03:29 | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: key
dtype: string
- name: labels
list:
- name: end
sequence: int64
- name: start
sequence: int64
splits:
- name: train
num_bytes: 100706304
num_examples: 104071
- name: validation
num_bytes: 12941478
num_examples: 12836
download_size: 61870589
dataset_size: 113647782
---
# Dataset Card for "naturalquestionsshortqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | 700 | [
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] |
C-MTEB/VideoRetrieval | 2023-07-28T08:45:16.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,687 | 2023-07-28T08:45:00 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 8176771
num_examples: 100930
- name: queries
num_bytes: 34156
num_examples: 1000
download_size: 7287165
dataset_size: 8210927
---
# Dataset Card for "VideoRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 584 | [
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covost2 | 2022-11-18T19:46:56.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-common-voice",
"language:ar",
"language:ca",
"language:cy",
"language:de",
"language:es",
"language:et",
"language:fa",
"language:fr",
"language:id",
"language:it",
"language:ja",
"language:lv",
"language:mn",
"language:nl",
"language:pt",
"language:ru",
"language:sl",
"language:sv",
"language:ta",
"language:tr",
"language:zh",
"license:cc-by-nc-4.0",
"arxiv:2007.10310",
"region:us"
] | null | CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. The dataset is created using Mozilla’s open source Common Voice database of crowdsourced voice recordings.
Note that in order to limit the required storage for preparing this dataset, the audio
is stored in the .mp3 format and is not converted to a float32 array. To convert, the audio
file to a float32 array, please make use of the `.map()` function as follows:
```python
import torchaudio
def map_to_array(batch):
speech_array, _ = torchaudio.load(batch["file"])
batch["speech"] = speech_array.numpy()
return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
``` | @misc{wang2020covost,
title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus},
author={Changhan Wang and Anne Wu and Juan Pino},
year={2020},
eprint={2007.10310},
archivePrefix={arXiv},
primaryClass={cs.CL} | 7 | 1,679 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ar
- ca
- cy
- de
- es
- et
- fa
- fr
- id
- it
- ja
- lv
- mn
- nl
- pt
- ru
- sl
- sv
- ta
- tr
- zh
language_bcp47:
- sv-SE
- zh-CN
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-common-voice
task_categories:
- automatic-speech-recognition
task_ids: []
paperswithcode_id: null
pretty_name: CoVoST 2
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---
# Dataset Card for covost2
## 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/facebookresearch/covost
- **Repository:** https://github.com/facebookresearch/covost
- **Paper:** https://arxiv.org/abs/2007.10310
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Changhan Wang (changhan@fb.com), Juan Miguel Pino (juancarabina@fb.com), Jiatao Gu (jgu@fb.com)
### Dataset Summary
CoVoST 2 is a large-scale multilingual speech translation corpus covering translations from 21 languages into English \
and from English into 15 languages. The dataset is created using Mozillas open-source Common Voice database of \
crowdsourced voice recordings. There are 2,900 hours of speech represented in the corpus.
### Supported Tasks and Leaderboards
`speech-translation`: The dataset can be used for Speech-to-text translation (ST). The model is presented with an audio file in one language and asked to transcribe the audio file to written text in another language. The most common evaluation metric is the BLEU score. Examples can be found at https://github.com/pytorch/fairseq/blob/master/examples/speech_to_text/docs/covost_example.md .
### Languages
The dataset contains the audio, transcriptions, and translations in the following languages, French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian, Chinese, Welsh, Catalan, Slovenian, Estonian, Indonesian, Arabic, Tamil, Portuguese, Latvian, and Japanese.
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file`, its transcription, called `sentence`, and the translation in target language called `translation`.
```
{'client_id': 'd277a1f3904ae00b09b73122b87674e7c2c78e08120721f37b5577013ead08d1ea0c053ca5b5c2fb948df2c81f27179aef2c741057a17249205d251a8fe0e658',
'file': '/home/suraj/projects/fairseq_s2t/covst/dataset/en/clips/common_voice_en_18540003.mp3',
'audio': {'path': '/home/suraj/projects/fairseq_s2t/covst/dataset/en/clips/common_voice_en_18540003.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000},
'id': 'common_voice_en_18540003',
'sentence': 'When water is scarce, avoid wasting it.',
'translation': 'Wenn Wasser knapp ist, verschwenden Sie es nicht.'}
```
### Data Fields
- file: A path to the downloaded audio file in .mp3 format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The transcription of the audio file in source language.
- translation: The transcription of the audio file in the target language.
- id: unique id of the data sample.
### Data Splits
| config | train | validation | test |
|----------|--------|------------|-------|
| en_de | 289430 | 15531 | 15531 |
| en_tr | 289430 | 15531 | 15531 |
| en_fa | 289430 | 15531 | 15531 |
| en_sv-SE | 289430 | 15531 | 15531 |
| en_mn | 289430 | 15531 | 15531 |
| en_zh-CN | 289430 | 15531 | 15531 |
| en_cy | 289430 | 15531 | 15531 |
| en_ca | 289430 | 15531 | 15531 |
| en_sl | 289430 | 15531 | 15531 |
| en_et | 289430 | 15531 | 15531 |
| en_id | 289430 | 15531 | 15531 |
| en_ar | 289430 | 15531 | 15531 |
| en_ta | 289430 | 15531 | 15531 |
| en_lv | 289430 | 15531 | 15531 |
| en_ja | 289430 | 15531 | 15531 |
| fr_en | 207374 | 14760 | 14760 |
| de_en | 127834 | 13511 | 13511 |
| es_en | 79015 | 13221 | 13221 |
| ca_en | 95854 | 12730 | 12730 |
| it_en | 31698 | 8940 | 8951 |
| ru_en | 12112 | 6110 | 6300 |
| zh-CN_en | 7085 | 4843 | 4898 |
| pt_en | 9158 | 3318 | 4023 |
| fa_en | 53949 | 3445 | 3445 |
| et_en | 1782 | 1576 | 1571 |
| mn_en | 2067 | 1761 | 1759 |
| nl_en | 7108 | 1699 | 1699 |
| tr_en | 3966 | 1624 | 1629 |
| ar_en | 2283 | 1758 | 1695 |
| sv-SE_en | 2160 | 1349 | 1595 |
| lv_en | 2337 | 1125 | 1629 |
| sl_en | 1843 | 509 | 360 |
| ta_en | 1358 | 384 | 786 |
| ja_en | 1119 | 635 | 684 |
| id_en | 1243 | 792 | 844 |
| cy_en | 1241 | 690 | 690 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## 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
[CC BY-NC 4.0](https://github.com/facebookresearch/covost/blob/main/LICENSE)
### Citation Information
```
@misc{wang2020covost,
title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus},
author={Changhan Wang and Anne Wu and Juan Pino},
year={2020},
eprint={2007.10310},
archivePrefix={arXiv},
primaryClass={cs.CL}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | 24,399 | [
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allenai/mslr2022 | 2022-11-18T21:16:10.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"language:en",
"license:apache-2.0",
"region:us"
] | allenai | The Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical
evidence from different clinical studies are summarized in literature reviews. Reviews provide the
highest quality of evidence for clinical care, but are expensive to produce manually.
(Semi-)automation via NLP may facilitate faster evidence synthesis without sacrificing rigor.
The MSLR shared task uses two datasets to assess the current state of multidocument summarization
for this task, and to encourage the development of modeling contributions, scaffolding tasks, methods
for model interpretability, and improved automated evaluation methods in this domain. | @inproceedings{DeYoung2021MS2MS,
title = {MSˆ2: Multi-Document Summarization of Medical Studies},
author = {Jay DeYoung and Iz Beltagy and Madeleine van Zuylen and Bailey Kuehl and Lucy Lu Wang},
booktitle = {EMNLP},
year = {2021}
}
@article{Wallace2020GeneratingN,
title = {Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization},
author = {Byron C. Wallace and Sayantani Saha and Frank Soboczenski and Iain James Marshall},
year = 2020,
journal = {AMIA Annual Symposium},
volume = {abs/2008.11293}
} | 5 | 1,674 | 2022-07-18T16:24:24 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
# Dataset Card for MSLR2022
## Table of Contents
- [Dataset Card for MSLR2022](#dataset-card-for-mslr2022)
- [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)
## Dataset Description
- **Homepage:** https://github.com/allenai/mslr-shared-task
- **Repository:** https://github.com/allenai/mslr-shared-task
- **Paper:** https://aclanthology.org/2021.emnlp-main.594
- **Leaderboard:** https://github.com/allenai/mslr-shared-task#leaderboard
- **Point of Contact:** https://github.com/allenai/mslr-shared-task#contact-us
### Dataset Summary
The Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical evidence from different clinical studies are summarized in literature reviews. Reviews provide the highest quality of evidence for clinical care, but are expensive to produce manually. (Semi-)automation via NLP may facilitate faster evidence synthesis without sacrificing rigor. The MSLR shared task uses two datasets to assess the current state of multidocument summarization for this task, and to encourage the development of modeling contributions, scaffolding tasks, methods for model interpretability, and improved automated evaluation methods in this domain.
### Supported Tasks and Leaderboards
This dataset is used for the MSLR2022 Shared Task. For information on the shared task leaderboard, please refer [here](https://github.com/allenai/mslr-shared-task#leaderboard).
### Languages
English
## Dataset Structure
More information on dataset structure [here](https://github.com/allenai/mslr-shared-task#data-structure).
### Data Instances
__MS^2__
```json
{
"review_id": "30760312",
"pmid": [
"22776744",
"25271670",
"3493740",
"1863023",
"16291984",
"23984728",
"23996433",
"18466198",
"12151469",
"27400308",
"16053970",
"22922316",
"11897647",
"11597664",
"4230647"
],
"title": [
"Improved Cell Survival and Paracrine Capacity of Human Embryonic Stem Cell-Derived Mesenchymal Stem Cells Promote Therapeutic Potential for Pulmonary Arterial Hypertension",
"Adipose-derived stem cells attenuate pulmonary arterial hypertension and ameliorate pulmonary arterial remodeling in monocrotaline-induced pulmonary hypertensive rats",
"Effect of bone marrow mesenchymal stem cells on experimental pulmonary arterial hypertension",
"Survival in patients with primary pulmonary hypertension. Results from a national prospective registry.",
"Sildenafil citrate therapy for pulmonary arterial hypertension.",
"Macitentan and morbidity and mortality in pulmonary arterial hypertension.",
"Long-term research of stem cells in monocrotaline-induced pulmonary arterial hypertension",
"Safety and efficacy of autologous endothelial progenitor cells transplantation in children with idiopathic pulmonary arterial hypertension: open-label pilot study.",
"Inhaled iloprost for severe pulmonary hypertension.",
"Sildenafil reduces pulmonary vascular resistance in single ventricular physiology.",
"Ambrisentan therapy for pulmonary arterial hypertension.",
"Mesenchymal stem cell prevention of vascular remodeling in high flow-induced pulmonary hypertension through a paracrine mechanism.",
"Continuous subcutaneous infusion of treprostinil, a prostacyclin analogue, in patients with pulmonary arterial hypertension: a double-blind, randomized, placebo-controlled trial.",
"Effects of the dual endothelin-receptor antagonist bosentan in patients with pulmonary hypertension: a randomised placebocontrolled study",
"SYRCLE\\u2019s risk of bias tool for animal studies"
],
"abstract": [
"Although transplantation of adult bone marrow mesenchymal stem cells ( BM-MSCs ) holds promise in the treatment for pulmonary arterial hypertension ( PAH ) , the poor survival and differentiation potential of adult BM-MSCs have limited their therapeutic efficiency . Here , we compared the therapeutic efficacy of human embryonic stem cell-derived MSCs ( hESC-MSCs ) with adult BM-MSCs for the treatment of PAH in an animal model . One week following monocrotaline (MCT)-induced PAH , mice were r and omly assigned to receive phosphate-buffered saline ( MCT group ) ; 3.0 \\u00d7 106 human BM-derived MSCs ( BM-MSCs group ) or 3.0 \\u00d7 106 hESC-derived MSCs ( hESC-MSCs group ) via tail vein injection . At 3 weeks posttransplantation , the right ventricular systolic pressure ( RVSP ) , degree of RV hypertrophy , and medial wall thickening of pulmonary arteries were lower= , and pulmonary capillary density was higher in the hESC-MSC group as compared with BM-MSC and MCT groups ( all p < 0.05 ) . At 1 week posttransplantation , the number of engrafted MSCs in the lungs was found significantly higher in the hESC-MSC group than in the BM-MSC group ( all p < 0.01 ) . At 3 weeks posttransplantation , implanted BM-MSCs were undetectable whereas hESC-MSCs were not only engrafted in injured pulmonary arteries but had also undergone endothelial differentiation . In addition , protein profiling of hESC-MSC- and BM-MSC-conditioned medium revealed a differential paracrine capacity . Classification of these factors into bioprocesses revealed that secreted factors from hESC-MSCs were preferentially involved in early embryonic development and tissue differentiation , especially blood vessel morphogenesis . We concluded that improved cell survival and paracrine capacity of hESC-MSCs provide better therapeutic efficacy than BM-MSCs in the treatment for PAH",
"Abstract We investigated the effect of adipose-derived stem cells ( ADSCs ) transplantation effects on structural remodeling and pulmonary artery pressure in monocrotaline (MCT)-induced pulmonary hypertensive rats . In the first experiment , 32 male Sprague-Dawley ( SD ) rats were r and omly divided into four groups ( n = 8/group ) : 3 ADSCs treated groups and normal control ( Ctrl ) . ADSCs were administered through the left jugular vein at 105 , 106 and 107 cells , respectively , and a cell density of 106cells/ml was shown to be optimal . The GFP-tagged ADSCs were identified in the lungs and differentiated into endothelial-like cells . In the second experiment , 96 male SD rats were r and omly divided into three groups ( n = 32/group ) : Ctrl , MCT-induced pulmonary arterial hypertension ( PAH ) , and PAH treated with ADSCs ( ADSCs ) . Two weeks post-MCT administration , the ADSCs group received 1 \\u00d7 106 ADSCs via the external jugular vein . Compared to PAH rats , mean pulmonary arterial pressure was decreased in rats at 1 , 2 , and 3 weeks after ADSCs-treatment ( 18.63 \\u00b1 2.15 mmHg versus 24.53 \\u00b1 2.90 mmHg ; 23.07 \\u00b1 2.84 mmHg versus 33.18 \\u00b1 2.30 mmHg ; 22.98 \\u00b1 2.34 mmHg versus 36.38 \\u00b1 3.28 mmHg , p < 0.05 ) . Meanwhile , the right heart hypertrophy index ( 36.2 1 \\u00b1 4.27 % versus 41.01 \\u00b1 1.29 % ; 39.47 \\u00b1 4.02 % versus 48.75 \\u00b1 2 .13 % ; 41.02 \\u00b1 0.9 % versus 50.52 \\u00b1 1.49 % , p < 0.05 , respectively ) , ratio of wall/lumen thickness , as well as the wall/lumen area were significantly reduced in PAH rats at these time points following ADSCs-treatment , as compared with untreated PAH rats . In summary , ADSCs may colonize the pulmonary arteries , attenuate pulmonary arterial hypertension and ameliorate pulmonary arterial remodeling",
"The aim of the present study was to investigate the effect of bone marrow mesenchymal stem cell ( BMSC ) transp1antation on lung and heart damage in a rat model of monocrotaline (MCT)-induced pulmonary arterial hypertension ( PAH ) . The animals were r and omly divided into 3 groups : control , PAH and BMSC implantation groups . Structural changes in the pulmonary vascular wall , such as the pulmonary artery lumen area ( VA ) and vascular area ( TAA ) were measured by hematoxylin and eosin ( H&E ) staining , and the hemodynamics were detected by echocardiography . Two weeks post-operation , our results demonstrated that sublingual vein injection of BMSCs significantly attenuated the pulmonary vascular structural and hemodynamic changes caused by pulmonary arterial hypertension . The mechanism may be executed via paracrine effects",
"OBJECTIVE To characterize mortality in persons diagnosed with primary pulmonary hypertension and to investigate factors associated with survival . DESIGN Registry with prospect i ve follow-up . SETTING Thirty-two clinical centers in the United States participating in the Patient Registry for the Characterization of Primary Pulmonary Hypertension supported by the National Heart , Lung , and Blood Institute . PATIENTS Patients ( 194 ) diagnosed at clinical centers between 1 July 1981 and 31 December 1985 and followed through 8 August 1988 . MEASUREMENTS At diagnosis , measurements of hemodynamic variables , pulmonary function , and gas exchange variables were taken in addition to information on demographic variables , medical history , and life-style . Patients were followed for survival at 6-month intervals . MAIN RESULTS The estimated median survival of these patients was 2.8 years ( 95 % Cl , 1.9 to 3.7 years ) . Estimated single-year survival rates were as follows : at 1 year , 68 % ( Cl , 61 % to 75 % ) ; at 3 years , 48 % ( Cl , 41 % to 55 % ) ; and at 5 years , 34 % ( Cl , 24 % to 44 % ) . Variables associated with poor survival included a New York Heart Association ( NYHA ) functional class of III or IV , presence of Raynaud phenomenon , elevated mean right atrial pressure , elevated mean pulmonary artery pressure , decreased cardiac index , and decreased diffusing capacity for carbon monoxide ( DLCO ) . Drug therapy at entry or discharge was not associated with survival duration . CONCLUSIONS Mortality was most closely associated with right ventricular hemodynamic function and can be characterized by means of an equation using three variables : mean pulmonary artery pressure , mean right atrial pressure , and cardiac index . Such an equation , once vali date d prospect ively , could be used as an adjunct in planning treatment strategies and allocating medical re sources",
"BACKGROUND Sildenafil inhibits phosphodiesterase type 5 , an enzyme that metabolizes cyclic guanosine monophosphate , thereby enhancing the cyclic guanosine monophosphate-mediated relaxation and growth inhibition of vascular smooth-muscle cells , including those in the lung . METHODS In this double-blind , placebo-controlled study , we r and omly assigned 278 patients with symptomatic pulmonary arterial hypertension ( either idiopathic or associated with connective-tissue disease or with repaired congenital systemic-to-pulmonary shunts ) to placebo or sildenafil ( 20 , 40 , or 80 mg ) orally three times daily for 12 weeks . The primary end point was the change from baseline to week 12 in the distance walked in six minutes . The change in mean pulmonary-artery pressure and World Health Organization ( WHO ) functional class and the incidence of clinical worsening were also assessed , but the study was not powered to assess mortality . Patients completing the 12-week r and omized study could enter a long-term extension study . RESULTS The distance walked in six minutes increased from baseline in all sildenafil groups ; the mean placebo-corrected treatment effects were 45 m ( + 13.0 percent ) , 46 m ( + 13.3 percent ) , and 50 m ( + 14.7 percent ) for 20 , 40 , and 80 mg of sildenafil , respectively ( P<0.001 for all comparisons ) . All sildenafil doses reduced the mean pulmonary-artery pressure ( P=0.04 , P=0.01 , and P<0.001 , respectively ) , improved the WHO functional class ( P=0.003 , P<0.001 , and P<0.001 , respectively ) , and were associated with side effects such as flushing , dyspepsia , and diarrhea . The incidence of clinical worsening did not differ significantly between the patients treated with sildenafil and those treated with placebo . Among the 222 patients completing one year of treatment with sildenafil monotherapy , the improvement from baseline at one year in the distance walked in six minutes was 51 m. CONCLUSIONS Sildenafil improves exercise capacity , WHO functional class , and hemodynamics in patients with symptomatic pulmonary arterial hypertension",
"BACKGROUND Current therapies for pulmonary arterial hypertension have been adopted on the basis of short-term trials with exercise capacity as the primary end point . We assessed the efficacy of macitentan , a new dual endothelin-receptor antagonist , using a primary end point of morbidity and mortality in a long-term trial . METHODS We r and omly assigned patients with symptomatic pulmonary arterial hypertension to receive placebo once daily , macitentan at a once-daily dose of 3 mg , or macitentan at a once-daily dose of 10 mg . Stable use of oral or inhaled therapy for pulmonary arterial hypertension , other than endothelin-receptor antagonists , was allowed at study entry . The primary end point was the time from the initiation of treatment to the first occurrence of a composite end point of death , atrial septostomy , lung transplantation , initiation of treatment with intravenous or subcutaneous prostanoids , or worsening of pulmonary arterial hypertension . RESULTS A total of 250 patients were r and omly assigned to placebo , 250 to the 3-mg macitentan dose , and 242 to the 10-mg macitentan dose . The primary end point occurred in 46.4 % , 38.0 % , and 31.4 % of the patients in these groups , respectively . The hazard ratio for the 3-mg macitentan dose as compared with placebo was 0.70 ( 97.5 % confidence interval [ CI ] , 0.52 to 0.96 ; P=0.01 ) , and the hazard ratio for the 10-mg macitentan dose as compared with placebo was 0.55 ( 97.5 % CI , 0.39 to 0.76 ; P<0.001 ) . Worsening of pulmonary arterial hypertension was the most frequent primary end-point event . The effect of macitentan on this end point was observed regardless of whether the patient was receiving therapy for pulmonary arterial hypertension at baseline . Adverse events more frequently associated with macitentan than with placebo were headache , nasopharyngitis , and anemia . CONCLUSIONS Macitentan significantly reduced morbidity and mortality among patients with pulmonary arterial hypertension in this event-driven study . ( Funded by Actelion Pharmaceuticals ; SERAPHIN Clinical Trials.gov number , NCT00660179 . )",
"Our previous studies have shown that bone marrow mesenchymal stem cells ( BMSCs ) can inhibit the progression of pulmonary artery hypertension ( PAH ) in the monocrotaline ( MCT ) model in the short term . The aim of this study was to further investigate the long-term effect of BMSCs on PAH and to explore the mechanism of the protective effect including the pulmonary vascular remodeling and cell differentiation . PAH model was established by subcutaneous injection of 50 mg/kg MCT as previously study . Postoperatively , the animals were r and omly divided into three groups ( n = 10 in each group ) : control , PAH group , and BMSCs implantation group . Six months after injection , immunology and immunohistochemistry analysis indicated the MCT-induced intima-media thickness in muscular arteries was reduced ( P < 0.05 ) ; the area of collagen fibers in lung tissue was lower ( P < 0.05 ) , and the proliferating cell nuclear antigen level in pulmonary artery smooth muscle cells was decreased ( P < 0.05 ) . Immunofluorescence showed that the cells have the ability to differentiate between von Willebr and factor and vascular endothelial growth factor . Six months after intravenous injection , BMSCs could significantly improve pulmonary function by inhibiting the ventricular remodeling and the effect of cell differentiation",
"Experimental data suggest that transplantation of EPCs attenuates monocrotaline-induced pulmonary hypertension in rats and dogs . In addition , our previous studies suggested that autologous EPC transplantation was feasible , safe , and might have beneficial effects on exercise capacity and pulmonary hemodynamics in adults with IPAH . Thus , we hypothesized that transplantation of EPCs would improve exercise capacity and pulmonary hemodynamics in children with IPAH . Thirteen children with IPAH received intravenous infusion of autologous EPCs . The right-sided heart catheterization and 6-MWD test were performed at baseline and at the time of 12 wk after cell infusion . At the time of 12 wk , mPAP decreased by 6.4 mmHg from 70.3 + /- 19.0 to 63.9 + /- 19.3 mmHg ( p = 0.015 ) . PVR decreased by approximately 19 % from 1118 + /- 537 to 906 + /- 377 dyn s/cm(5 ) ( p = 0.047 ) . CO increased from 3.39 + /- 0.79 to 3.85 + /- 0.42 L/min ( p = 0.048 ) . The 6-MWD increased by 39 m from 359 + /- 82 to 399 + /- 74 m ( p = 0.012 ) . NYHA functional class also improved . There were no severe adverse events with cell infusion . The small pilot study suggested that intravenous infusion of autologous EPCs was feasible , safe , and associated with significant improvements in exercise capacity , NYHA functional class , and pulmonary hemodynamics in children with IPAH . Confirmation of these results in a r and omized controlled trial are essential",
"BACKGROUND Uncontrolled studies suggested that aerosolized iloprost , a stable analogue of prostacyclin , causes selective pulmonary vasodilatation and improves hemodynamics and exercise capacity in patients with pulmonary hypertension . METHODS We compared repeated daily inhalations of 2.5 or 5.0 microg of iloprost ( six or nine times per day ; median inhaled dose , 30 microg per day ) with inhalation of placebo . A total of 203 patients with selected forms of severe pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension ( New York Heart Association [ NYHA ] functional class III or IV ) were included . The primary end point was met if , after week 12 , the NYHA class and distance walked in six minutes were improved by at least one class and at least 10 percent , respectively , in the absence of clinical deterioration according to predefined criteria and death . RESULTS The combined clinical end point was met by 16.8 percent of the patients receiving iloprost , as compared with 4.9 percent of the patients receiving placebo ( P=0.007 ) . There were increases in the distance walked in six minutes of 36.4 m in the iloprost group as a whole ( P=0.004 ) and of 58.8 m in the subgroup of patients with primary pulmonary hypertension . Overall , 4.0 percent of patients in the iloprost group ( including one who died ) and 13.7 percent of those in the placebo group ( including four who died ) did not complete the study ( P=0.024 ) ; the most common reason for withdrawal was clinical deterioration . As compared with base-line values , hemodynamic values were significantly improved at 12 weeks when measured after iloprost inhalation ( P<0.001 ) , were largely unchanged when measured before iloprost inhalation , and were significantly worse in the placebo group . Further significant beneficial effects of iloprost treatment included an improvement in the NYHA class ( P=0.03 ) , dyspnea ( P=0.015 ) , and quality of life ( P=0.026 ) . Syncope occurred with similar frequency in the two groups but was more frequently rated as serious in the iloprost group , although this adverse effect was not associated with clinical deterioration . CONCLUSIONS Inhaled iloprost is an effective therapy for patients with severe pulmonary hypertension",
"BACKGROUND High pulmonary vascular resistance ( PVR ) may be a risk factor for early and late mortality in both Glen shunt and Fontan operation patients . Furthermore , PVR may increase long after the Fontan operation . Whether pulmonary vasodilators such as phosphodiesterase 5 inhibitors can decrease PVR in patients with single ventricular physiology remains undetermined . METHODS AND RESULTS This was a prospect i ve , multicenter study . Patients with single ventricular physiology who have a PVR index higher than 2.5 Wood units \\u00b7 \\u33a1 ( WU ) were enrolled . Cardiac catheterization was performed before and after administration of sildenafil in all patients . After the Fontan operation , a six minute walk test ( 6MWT ) was also performed . A total of 42 patients were enrolled . PVR was significantly decreased in each stage of single ventricular physiology after sildenafil administration : from 4.3\\u00b11.5WU to 2.1\\u00b10.6WU ( p<0.01 ) in patients before a Glenn shunt , from 3.2\\u00b10.5WU to 1.6\\u00b10.6WU ( p<0.001 ) in patients after a Glenn shunt , and from 3.9\\u00b11.7WU to 2.3\\u00b10.8WU ( p<0.001 ) in patients after Fontan . In patients after Fontan , the 6MWT increased from 416\\u00b174 m to 485\\u00b172 m ( p<0.01 ) , and NYHA functional class improved significantly ( p<0.05 ) after sildenafil administration . No major side effects were observed in any patients . CONCLUSIONS Sildenafil reduced PVR in patients with single ventricle physiology . Sildenafil increased exercise capacity and improved NYHA functional class in patients after a Fontan operation . This implies that pulmonary vasodilation is a potential therapeutic target in selected patients with elevated PVR with single ventricle physiology . Long-term clinical significance warrants further study",
"OBJECTIVES The purpose of this study was to examine the efficacy and safety of four doses of ambrisentan , an oral endothelin type A receptor-selective antagonist , in patients with pulmonary arterial hypertension ( PAH ) . BACKGROUND Pulmonary arterial hypertension is a life-threatening and progressive disease with limited treatment options . Endothelin is a vasoconstrictor and smooth muscle cell mitogen that plays a critical role in the pathogenesis and progression of PAH . METHODS In this double-blind , dose-ranging study , 64 patients with idiopathic PAH or PAH associated with collagen vascular disease , anorexigen use , or human immunodeficiency virus infection were r and omized to receive 1 , 2.5 , 5 , or 10 mg of ambrisentan once daily for 12 weeks followed by 12 weeks of open-label ambrisentan . The primary end point was an improvement from baseline in 6-min walk distance ( 6MWD ) ; secondary end points included Borg dyspnea index , World Health Organization ( WHO ) functional class , a subject global assessment , and cardiopulmonary hemodynamics . RESULTS At 12 weeks , ambrisentan increased 6MWD ( + 36.1 m , p < 0.0001 ) with similar and statistically significant increases for each dose group ( range , + 33.9 to + 38.1 m ) . Improvements were also observed in Borg dyspnea index , WHO functional class , subject global assessment , mean pulmonary arterial pressure ( -5.2 mm Hg , p < 0.0001 ) , and cardiac index ( + 0.33 l/min/m2 , p < 0.0008 ) . Adverse events were mild and unrelated to dose , including the incidence of elevated serum aminotransferase concentrations > 3 times the upper limit of normal ( 3.1 % ) . CONCLUSIONS Ambrisentan appears to improve exercise capacity , symptoms , and hemodynamics in patients with PAH . The incidence and severity of liver enzyme abnormalities appear to be low",
"UNLABELLED Pulmonary arterial hypertension ( PAH ) is characterized by functional and structural changes in the pulmonary vasculature , and despite the drug treatment that made significant progress , the prognosis of patients with advanced PH remains extremely poor . In the present study , we investigated the early effect of bone marrow mesenchymal stem cells ( BMSCs ) on experimental high blood flow-induced PAH model rats and discussed the mechanism . BMSCs were isolated , cultured from bone marrow of Sprague-Dawley ( SD ) rat . The animal model of PAH was created by surgical methods to produce a left-to-right shunt . Following the successful establishment of the PAH model , rats were r and omly assigned to three groups ( n=20 in each group ) : sham group ( control ) , PAH group , and BMSC group ( received a sublingual vein injection of 1 - 5 \\u00d7 10(6 ) BMSCs ) . Two weeks after the administration , BMSCs significantly reduced the vascular remodeling , improved the hemodynamic data , and deceased the right ventricle weight ratio to left ventricular plus septal weight ( RV/LV+S ) ( P<0.05 ) . Real-time reverse transcription-polymerase chain reaction ( RT-PCR ) and immunohistochemistry analysis results indicated that the inflammation factors such as interleukin-1\\u03b2 ( IL-1\\u03b2 ) , IL-6 , and tumor necrosis factor-\\u03b1 ( TNF-\\u03b1 ) were reduced ( P<0.05 ) ; the expression of matrix metallo proteinase-9 ( MMP-9 ) was lower ( P<0.05 ) ; vascular endothelial growth factor ( VEGF ) was higher in BMSC group than those in PAH group ( P<0.05 ) . CONCLUSION Sublingual vein injection of BMSCs for 2 weeks , significantly improved the lung and heart injury caused by left-to-right shunt-induced PAH ; decreased pulmonary vascular remodeling and inflammation ; and enhanced angiogenesis",
"Pulmonary arterial hypertension is a life-threatening disease for which continuous intravenous prostacyclin has proven to be effective . However , this treatment requires a permanent central venous catheter with the associated risk of serious complications such as sepsis , thromboembolism , or syncope . Treprostinil , a stable prostacyclin analogue , can be administered by a continuous subcutaneous infusion , avoiding these risks . We conducted a 12-week , double-blind , placebo-controlled multicenter trial in 470 patients with pulmonary arterial hypertension , either primary or associated with connective tissue disease or congenital systemic-to-pulmonary shunts . Exercise capacity improved with treprostinil and was unchanged with placebo ; the between treatment group difference in median six-minute walking distance was 16 m ( p = 0.006 ) . Improvement in exercise capacity was greater in the sicker patients and was dose-related , but independent of disease etiology . Concomitantly , treprostinil significantly improved indices of dyspnea , signs and symptoms of pulmonary hypertension , and hemodynamics . The most common side effect attributed to treprostinil was infusion site pain ( 85 % ) leading to premature discontinuation from the study in 8 % of patients . Three patients in the treprostinil treatment group presented with an episode of gastrointestinal hemorrhage . We conclude that chronic subcutaneous infusion of treprostinil is an effective treatment with an acceptable safety profile in patients with pulmonary arterial hypertension",
"BACKGROUND Endothelin 1 , a powerful endogenous vasoconstrictor and mitogen , might be a cause of pulmonary hypertension . We describe the efficacy and safety of bosentan , a dual endothelin-receptor antagonist that can be taken orally , in patients with severe pulmonary hypertension . METHODS In this double-blind , placebo-controlled study , 32 patients with pulmonary hypertension ( primary or associated with scleroderma ) were r and omly assigned to bosentan ( 62.5 mg taken twice daily for 4 weeks then 125 mg twice daily ) or placebo for a minimum of 12 weeks . The primary endpoint was change in exercise capacity . Secondary endpoints included changes in cardiopulmonary haemodynamics , Borg dyspnoea index , WHO functional class , and withdrawal due to clinical worsening . Analysis was by intention to treat . FINDINGS In patients given bosentan , the distance walked in 6 min improved by 70 m at 12 weeks compared with baseline , whereas it worsened by 6 m in those on placebo ( difference 76 m [ 95 % CI 12 - 139 ] , p=0.021 ) . The improvement was maintained for at least 20 weeks . The cardiac index was 1.0 L min(-1 ) m(-2 ) ( 95 % CI 0.6 - 1.4 , p<0.0001 ) greater in patients given bosentan than in those given placebo . Pulmonary vascular resistance decreased by 223 dyn s cm(-)(5 ) with bosentan , but increased by 191 dyn s cm(-5 ) with placebo ( difference -415 [ -608 to -221 ] , p=0.0002 ) . Patients given bosentan had a reduced Borg dyspnoea index and an improved WHO functional class . All three withdrawals from clinical worsening were in the placebo group ( p=0.033 ) . The number and nature of adverse events did not differ between the two groups . INTERPRETATION Bosentan increases exercise capacity and improves haemodynamics in patients with pulmonary hypertension , suggesting that endothelin has an important role in pulmonary hypertension",
"Background Systematic Review s ( SRs ) of experimental animal studies are not yet common practice , but awareness of the merits of conducting such SRs is steadily increasing . As animal intervention studies differ from r and omized clinical trials ( RCT ) in many aspects , the methodology for SRs of clinical trials needs to be adapted and optimized for animal intervention studies . The Cochrane Collaboration developed a Risk of Bias ( RoB ) tool to establish consistency and avoid discrepancies in assessing the method ological quality of RCTs . A similar initiative is warranted in the field of animal experimentation . Methods We provide an RoB tool for animal intervention studies ( SYRCLE \\u2019s RoB tool ) . This tool is based on the Cochrane RoB tool and has been adjusted for aspects of bias that play a specific role in animal intervention studies . To enhance transparency and applicability , we formulated signalling questions to facilitate judgment . Results The result ing RoB tool for animal studies contains 10 entries . These entries are related to selection bias , performance bias , detection bias , attrition bias , reporting bias and other biases . Half these items are in agreement with the items in the Cochrane RoB tool . Most of the variations between the two tools are due to differences in design between RCTs and animal studies . Shortcomings in , or unfamiliarity with , specific aspects of experimental design of animal studies compared to clinical studies also play a role . Conclusions SYRCLE \\u2019s RoB tool is an adapted version of the Cochrane RoB tool . Widespread adoption and implementation of this tool will facilitate and improve critical appraisal of evidence from animal studies . This may subsequently enhance the efficiency of translating animal research into clinical practice and increase awareness of the necessity of improving the method ological quality of animal studies"
],
"target": "Conclusions SC therapy is effective for PAH in pre clinical studies .\\nThese results may help to st and ardise pre clinical animal studies and provide a theoretical basis for clinical trial design in the future .",
"background": "Background Despite significant progress in drug treatment , the prognosis of patients with advanced pulmonary arterial hypertension ( PAH ) remains extremely poor .\\nMany pre clinical studies have reported the efficacy of stem cell ( SC ) therapy for PAH ; however , this approach remains controversial .\\nThe aim of this systematic review and meta- analysis is to assess the potential efficacy of SC therapy for PAH .",
"reviews_info": "Background Despite significant progress in drug treatment , the prognosis of patients with advanced pulmonary arterial hypertension ( PAH ) remains extremely poor .\\nMany pre clinical studies have reported the efficacy of stem cell ( SC ) therapy for PAH ; however , this approach remains controversial .\\nThe aim of this systematic review and meta- analysis is to assess the potential efficacy of SC therapy for PAH ."
}
```
__Cochrane__
```json
{
"review_id": "CD007697",
"pmid": [
"16394043"
],
"title": [
"Aggressive surgical effort and improved survival in advanced-stage ovarian cancer."
],
"abstract": [
"Residual disease after initial surgery for ovarian cancer is the strongest prognostic factor for survival. However, the extent of surgical resection required to achieve optimal cytoreduction is controversial. Our goal was to estimate the effect of aggressive surgical resection on ovarian cancer patient survival.\\n A retrospective cohort study of consecutive patients with International Federation of Gynecology and Obstetrics stage IIIC ovarian cancer undergoing primary surgery was conducted between January 1, 1994, and December 31, 1998. The main outcome measures were residual disease after cytoreduction, frequency of radical surgical resection, and 5-year disease-specific survival.\\n The study comprised 194 patients, including 144 with carcinomatosis. The mean patient age and follow-up time were 64.4 and 3.5 years, respectively. After surgery, 131 (67.5%) of the 194 patients had less than 1 cm of residual disease (definition of optimal cytoreduction). Considering all patients, residual disease was the only independent predictor of survival; the need to perform radical procedures to achieve optimal cytoreduction was not associated with a decrease in survival. For the subgroup of patients with carcinomatosis, residual disease and the performance of radical surgical procedures were the only independent predictors. Disease-specific survival was markedly improved for patients with carcinomatosis operated on by surgeons who most frequently used radical procedures compared with those least likely to use radical procedures (44% versus 17%, P < .001).\\n Overall, residual disease was the only independent predictor of survival. Minimizing residual disease through aggressive surgical resection was beneficial, especially in patients with carcinomatosis.\\n II-2."
],
"target": "We found only low quality evidence comparing ultra-radical and standard surgery in women with advanced ovarian cancer and carcinomatosis. The evidence suggested that ultra-radical surgery may result in better survival.\\u00a0 It was unclear whether there were any differences in progression-free survival, QoL and morbidity between the two groups. The cost-effectiveness of this intervention has not been investigated. We are, therefore, unable to reach definite conclusions about the relative benefits and adverse effects of the two types of surgery.\\nIn order to determine the role of ultra-radical surgery in the management of advanced stage ovarian cancer, a sufficiently powered randomised controlled trial comparing ultra-radical and standard surgery or well-designed non-randomised studies would be required."
}
```
### Data Fields
__MS^2__
- `"review_id"`: The PubMed ID of the review.
- `"pmid"`: The PubMed IDs of the included studies.
- `"title"`: The titles of the included studies.
- `"abstract"`: The abstracts of the included studies.
- `"target"`: The conclusions, taken from the abstract of the review, that serve as the summarization target.
- `"background"`: A description of the reviews objective.
__Cochrane__
- `"review_id"`: The PubMed ID of the review.
- `"pmid"`: The PubMed IDs of the included studies.
- `"title"`: The titles of the included studies.
- `"abstract"`: The abstracts of the included studies.
- `"target"`: The conclusions, taken from the abstract of the review, that serve as the summarization target.
### Data Splits
Each dataset is split into training, validation and test partitions
__MS^2__
| train | validation | test |
|------:|-----------:|-----:|
| 14188 | 2021 | 1667 |
__Cochrane__
| train | validation | test |
|------:|-----------:|-----:|
| 3752 | 470 | 470 |
## Dataset Creation
Please refer to the following papers for details about dataset curation:
[MSˆ2: A Dataset for Multi-Document Summarization of Medical Studies](https://aclanthology.org/2021.emnlp-main.594.pdf)
[Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378607/)
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Licensing information can be found [here](https://github.com/allenai/mslr-shared-task/blob/main/LICENSE).
### Citation Information
**DeYoung, Jay, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl and Lucy Lu Wang. "MS2: A Dataset for Multi-Document Summarization of Medical Studies." EMNLP (2021).**
```bibtex
@inproceedings{DeYoung2021MS2MS,
title={MSˆ2: Multi-Document Summarization of Medical Studies},
author={Jay DeYoung and Iz Beltagy and Madeleine van Zuylen and Bailey Kuehl and Lucy Lu Wang},
booktitle={EMNLP},
year={2021}
}
```
**Byron C. Wallace, Sayantani Saha, Frank Soboczenski, and Iain James Marshall. (2020). "Generating (factual?) narrative summaries of RCTs: Experiments with neural multi-document summarization." AMIA Annual Symposium.**
```bibtex
@article{Wallace2020GeneratingN,
title={Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization},
author={Byron C. Wallace and Sayantani Saha and Frank Soboczenski and Iain James Marshall},
journal={AMIA Annual Symposium},
year={2020},
volume={abs/2008.11293}
}
``` | 39,290 | [
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yahoo_answers_topics | 2023-01-25T15:03:25.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|other-yahoo-answers-corpus",
"language:en",
"license:unknown",
"region:us"
] | null | Yahoo! Answers Topic Classification is text classification dataset. The dataset is the Yahoo! Answers corpus as of 10/25/2007. The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. From all the answers and other meta-information, this dataset only used the best answer content and the main category information. | null | 26 | 1,673 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- extended|other-yahoo-answers-corpus
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: YahooAnswersTopics
dataset_info:
features:
- name: id
dtype: int32
- name: topic
dtype:
class_label:
names:
'0': Society & Culture
'1': Science & Mathematics
'2': Health
'3': Education & Reference
'4': Computers & Internet
'5': Sports
'6': Business & Finance
'7': Entertainment & Music
'8': Family & Relationships
'9': Politics & Government
- name: question_title
dtype: string
- name: question_content
dtype: string
- name: best_answer
dtype: string
config_name: yahoo_answers_topics
splits:
- name: train
num_bytes: 760460695
num_examples: 1400000
- name: test
num_bytes: 32661362
num_examples: 60000
download_size: 319476345
dataset_size: 793122057
train-eval-index:
- config: yahoo_answers_topics
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
question_content: text
topic: 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
---
# Dataset Card for "Yahoo Answers Topics"
## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### 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
[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 [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | 5,013 | [
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enriched_web_nlg | 2023-06-01T14:59:50.000Z | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-web-nlg",
"language:de",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | null | WebNLG is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the development and evaluation of popular tasks in the NLG pipeline architecture (Reiter and Dale, 2000), such as Discourse Ordering, Lexicalization, Aggregation and Referring Expression Generation. | @InProceedings{ferreiraetal2018,
author = "Castro Ferreira, Thiago and Moussallem, Diego and Wubben, Sander and Krahmer, Emiel",
title = "Enriching the WebNLG corpus",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
year = "2018",
series = {INLG'18},
publisher = "Association for Computational Linguistics",
address = "Tilburg, The Netherlands",
} | 1 | 1,668 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
- de
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-web-nlg
task_categories:
- tabular-to-text
task_ids:
- rdf-to-text
paperswithcode_id: null
pretty_name: Enriched WebNLG
dataset_info:
- config_name: en
features:
- name: category
dtype: string
- name: size
dtype: int32
- name: eid
dtype: string
- name: original_triple_sets
sequence:
- name: otriple_set
sequence: string
- name: modified_triple_sets
sequence:
- name: mtriple_set
sequence: string
- name: shape
dtype: string
- name: shape_type
dtype: string
- name: lex
sequence:
- name: comment
dtype: string
- name: lid
dtype: string
- name: text
dtype: string
- name: template
dtype: string
- name: sorted_triple_sets
sequence: string
- name: lexicalization
dtype: string
splits:
- name: train
num_bytes: 14665155
num_examples: 6940
- name: dev
num_bytes: 1843787
num_examples: 872
- name: test
num_bytes: 3931381
num_examples: 1862
download_size: 44284508
dataset_size: 20440323
- config_name: de
features:
- name: category
dtype: string
- name: size
dtype: int32
- name: eid
dtype: string
- name: original_triple_sets
sequence:
- name: otriple_set
sequence: string
- name: modified_triple_sets
sequence:
- name: mtriple_set
sequence: string
- name: shape
dtype: string
- name: shape_type
dtype: string
- name: lex
sequence:
- name: comment
dtype: string
- name: lid
dtype: string
- name: text
dtype: string
- name: template
dtype: string
- name: sorted_triple_sets
sequence: string
splits:
- name: train
num_bytes: 9748193
num_examples: 6940
- name: dev
num_bytes: 1238609
num_examples: 872
download_size: 44284508
dataset_size: 10986802
config_names:
- de
- en
---
# Dataset Card for WebNLG
## 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:** [WebNLG challenge website](https://webnlg-challenge.loria.fr/)
- **Repository:** [Enriched WebNLG Github repository](https://github.com/ThiagoCF05/webnlg)
- **Paper:** [Enriching the WebNLG corpus](https://www.aclweb.org/anthology/W18-6521/)
### Dataset Summary
The WebNLG challenge consists in mapping data to text. The training data consists of Data/Text pairs where the data is a
set of triples extracted from DBpedia and the text is a verbalisation of these triples. For instance, given the 3
DBpedia triples shown in (a), the aim is to generate a text such as (b). It is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the development and evaluation of popular tasks in the NLG pipeline architecture, such as Discourse Ordering, Lexicalization, Aggregation and Referring Expression Generation.
### Supported Tasks and Leaderboards
The dataset supports a `other-rdf-to-text` task which requires a model takes a set of RDF (Resource Description
Format) triples from a database (DBpedia) of the form (subject, property, object) as input and write out a natural
language sentence expressing the information contained in the triples.
### Languages
The dataset is presented in two versions: English (config `en`) and German (config `de`)
## Dataset Structure
### Data Instances
A typical example contains the original RDF triples in the set, a modified version which presented to crowd workers, and
a set of possible verbalizations for this set of triples:
```
{ 'category': 'Politician',
'eid': 'Id10',
'lex': {'comment': ['good', 'good', 'good'],
'lid': ['Id1', 'Id2', 'Id3'],
'text': ['World War II had Chiang Kai-shek as a commander and United States Army soldier Abner W. Sibal.',
'Abner W. Sibal served in the United States Army during the Second World War and during that war Chiang Kai-shek was one of the commanders.',
'Abner W. Sibal, served in the United States Army and fought in World War II, one of the commanders of which, was Chiang Kai-shek.']},
'modified_triple_sets': {'mtriple_set': [['Abner_W._Sibal | battle | World_War_II',
'World_War_II | commander | Chiang_Kai-shek',
'Abner_W._Sibal | militaryBranch | United_States_Army']]},
'original_triple_sets': {'otriple_set': [['Abner_W._Sibal | battles | World_War_II', 'World_War_II | commander | Chiang_Kai-shek', 'Abner_W._Sibal | branch | United_States_Army'],
['Abner_W._Sibal | militaryBranch | United_States_Army',
'Abner_W._Sibal | battles | World_War_II',
'World_War_II | commander | Chiang_Kai-shek']]},
'shape': '(X (X) (X (X)))',
'shape_type': 'mixed',
'size': 3}
```
### Data Fields
The following fields can be found in the instances:
- `category`: the category of the DBpedia entites present in the RDF triples.
- `eid`: an example ID, only unique per split per category.
- `size`: number of RDF triples in the set.
- `shape`: (for v3 only) Each set of RDF-triples is a tree, which is characterised by its shape and shape type. `shape`
is a string representation of the tree with nested parentheses where X is a node (
see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format))
- `shape_type`: (for v3 only) is a type of the tree shape, which can be: `chain` (the object of one triple is the
subject of the other); `sibling` (triples with a shared subject); `mixed` (both chain and sibling types present).
- `2017_test_category`: (for `webnlg_challenge_2017`) tells whether the set of RDF triples was present in the training
set or not.
- `lex`: the lexicalizations, with:
- `text`: the text to be predicted.
- `lid`: a lexicalizayion ID, unique per example.
- `comment`: the lexicalizations were rated by crowd workers are either `good` or `bad`
### Data Splits
The `en` version has `train`, `test` and `dev` splits; the `de` version, only `train` and `dev`.
## Dataset Creation
### Curation Rationale
Natural Language Generation (NLG) is the process of automatically converting non-linguistic data into a linguistic output format (Reiter andDale, 2000; Gatt and Krahmer, 2018). Recently, the field has seen an increase in the number of available focused data resources as E2E (Novikova et al., 2017), ROTOWIRE(Wise-man et al., 2017) and WebNLG (Gardent et al.,2017a,b) corpora. Although theses recent releases are highly valuable resources for the NLG community in general,nall of them were designed to work with end-to-end NLG models. Hence, they consist of a collection of parallel raw representations and their corresponding textual realizations. No intermediate representations are available so researchersncan straight-forwardly use them to develop or evaluate popular tasks in NLG pipelines (Reiter and Dale, 2000), such as Discourse Ordering, Lexicalization, Aggregation, Referring Expression Generation, among others. Moreover, these new corpora, like many other resources in Computational Linguistics more in general, are only available in English, limiting the development of NLG-applications to other languages.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset uses the `cc-by-nc-sa-4.0` license. The source DBpedia project uses the `cc-by-sa-3.0` and `gfdl-1.1`
licenses.
### Citation Information
- If you use the Enriched WebNLG corpus, cite:
```
@InProceedings{ferreiraetal2018,
author = "Castro Ferreira, Thiago
and Moussallem, Diego
and Wubben, Sander
and Krahmer, Emiel",
title = "Enriching the WebNLG corpus",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
year = "2018",
series = {INLG'18},
publisher = "Association for Computational Linguistics",
address = "Tilburg, The Netherlands",
}
@inproceedings{web_nlg,
author = {Claire Gardent and
Anastasia Shimorina and
Shashi Narayan and
Laura Perez{-}Beltrachini},
editor = {Regina Barzilay and
Min{-}Yen Kan},
title = {Creating Training Corpora for {NLG} Micro-Planners},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2017, Vancouver, Canada, July 30 - August 4, Volume
1: Long Papers},
pages = {179--188},
publisher = {Association for Computational Linguistics},
year = {2017},
url = {https://doi.org/10.18653/v1/P17-1017},
doi = {10.18653/v1/P17-1017}
}
```
### Contributions
Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset. | 10,900 | [
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yuvalkirstain/pickapic_v2 | 2023-09-25T11:14:43.000Z | [
"region:us"
] | yuvalkirstain | null | null | 4 | 1,661 | 2023-09-24T20:54:31 | ---
dataset_info:
features:
- name: are_different
dtype: bool
- name: best_image_uid
dtype: string
- name: caption
dtype: string
- name: created_at
dtype: timestamp[ns]
- name: has_label
dtype: bool
- name: image_0_uid
dtype: string
- name: image_0_url
dtype: string
- name: image_1_uid
dtype: string
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dtype: string
- name: jpg_0
dtype: binary
- name: jpg_1
dtype: binary
- name: label_0
dtype: float64
- name: label_1
dtype: float64
- name: model_0
dtype: string
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dtype: string
- name: ranking_id
dtype: int64
- name: user_id
dtype: int64
- name: num_example_per_prompt
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 322022952127
num_examples: 959040
- name: validation
num_bytes: 6339087542
num_examples: 20596
- name: test
num_bytes: 6618429346
num_examples: 20716
- name: validation_unique
num_bytes: 170578993
num_examples: 500
- name: test_unique
num_bytes: 175368751
num_examples: 500
download_size: 15603769274
dataset_size: 335326416759
---
# Dataset Card for "pickapic_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,359 | [
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] |
C-MTEB/CovidRetrieval | 2023-07-28T09:44:36.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,660 | 2023-07-28T09:43:30 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 91531256
num_examples: 100001
- name: queries
num_bytes: 111094
num_examples: 949
download_size: 65093081
dataset_size: 91642350
---
# Dataset Card for "CovidRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 587 | [
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] |
C-MTEB/T2Reranking | 2023-07-28T07:29:52.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,658 | 2023-07-28T07:28:07 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: dev
num_bytes: 206865573
num_examples: 6129
download_size: 120293598
dataset_size: 206865573
---
# Dataset Card for "T2Reranking"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 520 | [
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] |
C-MTEB/CmedqaRetrieval | 2023-07-28T09:40:17.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,657 | 2023-07-28T09:39:17 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 84962605
num_examples: 100001
- name: queries
num_bytes: 728106
num_examples: 3999
download_size: 61319407
dataset_size: 85690711
---
# Dataset Card for "CmedqaRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 589 | [
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] |
C-MTEB/MMarcoRetrieval | 2023-07-28T09:59:36.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,653 | 2023-07-28T09:59:09 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 32552468
num_examples: 106813
- name: queries
num_bytes: 303316
num_examples: 6980
download_size: 20422289
dataset_size: 32855784
---
# Dataset Card for "MMarcoRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 589 | [
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WizardLM/WizardLM_evol_instruct_70k | 2023-08-24T03:59:32.000Z | [
"arxiv:2308.09583",
"arxiv:2304.12244",
"arxiv:2306.08568",
"region:us"
] | WizardLM | null | null | 116 | 1,650 | 2023-04-25T09:57:27 | This is the training data of WizardLM.
## News
- 🔥 🔥 🔥 [08/11/2023] We release **WizardMath** Models.
- 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
- 🔥 Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM.
- 🔥 Our **WizardMath-70B-V1.0** model achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM.
| Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License|
| ----- |------| ---- |------|-------| ----- | ----- |
| WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>|
<font size=4>
| <sup>Model</sup> | <sup>Checkpoint</sup> | <sup>Paper</sup> |<sup>MT-Bench</sup> | <sup>AlpacaEval</sup> | <sup>WizardEval</sup> | <sup>HumanEval</sup> | <sup>License</sup>|
| ----- |------| ---- |------|-------| ----- | ----- | ----- |
| <sup>WizardLM-13B-V1.2</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>| | <sup>7.06</sup> | <sup>89.17%</sup> | <sup>101.4% </sup>|<sup>36.6 pass@1</sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> |
| <sup>WizardLM-13B-V1.1</sup> |<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> | | <sup>6.76</sup> |<sup>86.32%</sup> | <sup>99.3% </sup> |<sup>25.0 pass@1</sup>| <sup>Non-commercial</sup>|
| <sup>WizardLM-30B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> | | <sup>7.01</sup> | | <sup>97.8% </sup> | <sup>37.8 pass@1</sup>| <sup>Non-commercial</sup> |
| <sup>WizardLM-13B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> | | <sup>6.35</sup> | <sup>75.31%</sup> | <sup>89.1% </sup> |<sup> 24.0 pass@1 </sup> | <sup>Non-commercial</sup>|
| <sup>WizardLM-7B-V1.0 </sup>| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> |<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>| | | <sup>78.0% </sup> |<sup>19.1 pass@1 </sup>|<sup> Non-commercial</sup>|
| <sup>WizardCoder-15B-V1.0</sup> | <sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a></sup> | <sup>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a></sup> | || |<sup> 57.3 pass@1 </sup> | <sup> <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a></sup> |
</font> | 3,937 | [
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] |
kumapo/JAQKET | 2023-10-09T06:44:28.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:ja",
"license:cc-by-sa-4.0",
"region:us"
] | kumapo | JAQKET: JApanese Questions on Knowledge of EnTitie | @InProceedings{Kurihara_nlp2020,
author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也",
title = "JAQKET: クイズを題材にした日本語 QA データセットの構築",
booktitle = "言語処理学会第26回年次大会",
year = "2020",
url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf"
note= "in Japanese" | 0 | 1,648 | 2023-06-21T13:04:38 | ---
license: cc-by-sa-4.0
task_categories:
- multiple-choice
- question-answering
language:
- ja
---
# Dataset Card for JAQKET
This dataset loading script is developed on [GitHub](https://github.com/kumapo/JAQKET-dataset).
Please feel free to open an [issue](https://github.com/kumapo/JAQKET-dataset/issues) or [pull request](https://github.com/kumapo/JAQKET-dataset/pulls).
## Dataset Description
- **Homepage:** https://sites.google.com/view/project-aio/dataset
- **Repository:** https://github.com/kumapo/JAQKET-dataset
### Dataset Summary
From [the original paper](https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf):
> 本研究では，日本における質問応答/機械読解研究の促進を目的として，研究者が容易に利用可能な日本語のオープンドメイン QA タスクのデータセット「JAQKET」1を構築する.
> 作成するデータセットは，既存研究 [7] に倣い，Wikipedia2 の記事名を答えとした，日本語のオープンドメイン QA タスクのデータセットである.
### Supported Tasks
#### JAQKET v1.0
From [the original paper](https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf):
> 本研究で扱う日本語オープンドメイン QA タスクを定義する.本研究では，クイズの問題文に対して複数(数個から数十個程度)の解答の選択肢が与られ，その選択肢から正解を一つ選択するという択一問題を取り扱う.
#### JAQKET v2.0
From [the homepage](https://sites.google.com/view/project-aio/competition2):
> 問題として与えられるのはクイズの問題文のみです．その問題文から解答となる文字列を解答として返すシステムを構築してもらいます．
### Languages
The language data in JAQKET is in Japanese.
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
#### JAQKET v1.0
```python
from datasets import load_dataset
dataset = load_dataset("kumapo/JAQKET", name="v1.0")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['qid', 'question', 'answer_entity', 'label', 'answer_candidates', 'contexts'],
# num_rows: 13061
# })
# validation: Dataset({
# features: ['qid', 'question', 'answer_entity', 'label', 'answer_candidates', 'contexts'],
# num_rows: 271
# })
# })
```
An example of the JAQKET v1.0 dataset looks as follows:
```json
{
"qid": "QA20QBIK-0002",
"question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉?",
"answer_entity": "ササ",
"answer_candidates": [
"ササ",
"チシマザサ",
"クマザサ",
"アダン",
"チガヤ",
"アセビ",
"ススキ",
"ホオノキ",
"マテバシイ",
"ヤマフジ",
"ウツギ",
"タムシバ",
"ミズキ",
"アキタブキ",
"トベラ",
"クヌギ",
"ネズミモチ",
"ヒシ",
"コブシ",
"オオウバユリ"
],
"qtype": "なに〜"
}
```
```json
{
"qid": "QA20QBIK-0026",
"question": "北海道の中心に位置することから「北海道のへそ」と名乗る、ラベンダーで有名な都市はどこ?",
"answer_entity": "富良野市",
"answer_candidates": [
"富良野市",
"滝川市",
"北見市",
"芦別市",
"中富良野町",
"名寄市",
"網走市",
"美瑛町",
"南富良野町",
"岩見沢市",
"美唄市",
"上富良野町",
"倶知安町",
"小樽市",
"歌志内市",
"旭川市",
"ニセコ町",
"北斗市",
"稚内市",
"帯広市"
],
"qtype": "どこ"
}
```
#### JAQKET v2.0
```python
from datasets import load_dataset
dataset = load_dataset("kumapo/JAQKET", name="v2.0")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['qid', 'question', 'answers', 'ctxs'],
# num_rows: 2154
# })
# validation: Dataset({
# features: ['qid', 'question', 'answers', 'ctxs'],
# num_rows: 1164
# })
# })
```
An example of the JAQKET v2.0 dataset looks as follows:
```json
{
"qid": "QA20QBIK-0002",
"competition": "第1回AI王",
"timestamp": "2020/01/27",
"section": "開発データ問題 (dev1)",
"number": "2",
"original_question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉？",
"original_answer": "ササ",
"original_additional_info": "",
"question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉?",
"answers" :["ササ"]
}
```
## Additional Information
### Citation Information
```bibtex
@InProceedings{Kurihara_nlp2020,
author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也",
title = "JAQKET: クイズを題材にした日本語 QA データセットの構築",
booktitle = "言語処理学会第26回年次大会",
year = "2020",
url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf"
note= "in Japanese"}
```
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fka/awesome-chatgpt-prompts | 2023-03-07T10:04:18.000Z | [
"license:cc0-1.0",
"ChatGPT",
"region:us"
] | fka | null | null | 3,661 | 1,647 | 2022-12-13T23:47:45 | ---
license: cc0-1.0
tags:
- ChatGPT
---
<p align="center"><h1>🧠 Awesome ChatGPT Prompts [CSV dataset]</h1></p>
This is a Dataset Repository of **Awesome ChatGPT Prompts**
**[View All Prompts on GitHub](https://github.com/f/awesome-chatgpt-prompts)**
# License
CC-0
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jojo0217/korean_rlhf_dataset | 2023-09-25T08:36:04.000Z | [
"task_categories:text-generation",
"language:ko",
"license:apache-2.0",
"region:us"
] | jojo0217 | null | null | 12 | 1,644 | 2023-08-08T07:37:14 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- ko
---
성균관대학교 산학협력프로젝트 과정에서 한국어 llm 모델 SFT 학습을 위해 구축한 데이터셋 입니다.
2023-09-25
오픈 어시스턴트 data에서 오픈 어시스턴트를 포함하는 데이터 삭제
-> 답변에 오픈 어시스턴트라고 하는 경우가 나오기 때문
또한 스탠포드 대학 번역 데이터에서 번역 과정 오류로 input에 입력없음 과 같이 추가된 부분 삭제
그리고 \<unk\> 등으로 gpt 상에서 번역 오류가 난 것들을 삭제
***
자연스러움을 위해 stanford alpaca data, oig_chip2를 ChatGPT3.5 turbo 16k를 이용하여 새롭게 전처리 과정을 거쳤습니다.
https://github.com/JoJo0217/rlhf_korean_dataset/tree/main
여기에서 자세한 설명을 볼 수 있으며
데이터의 구성은 다음과 같습니다.
***
데이터 구성
|데이터 종류|개수|url|
|:---|---:|---:|
|koalpaca v1.1|21155|https://github.com/Beomi/KoAlpaca|
|stanford alpaca|51374|https://huggingface.co/datasets/tatsu-lab/alpaca|
|dolly|15009|https://huggingface.co/datasets/nlpai-lab/databricks-dolly-15k-ko|
|openassistant|9651|https://huggingface.co/datasets/nlpai-lab/openassistant-guanaco-ko|
|oig_chip2|10000|https://huggingface.co/datasets/0-hero/OIG-small-chip2|
|총합|107189||
| 965 | [
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] |
conllpp | 2023-04-05T10:02:29.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|conll2003",
"language:en",
"license:unknown",
"region:us"
] | null | CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set
have been manually corrected. The training set and development set are included for completeness.
For more details see https://www.aclweb.org/anthology/D19-1519/ and https://github.com/ZihanWangKi/CrossWeigh | @inproceedings{wang2019crossweigh,
title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={5157--5166},
year={2019}
} | 5 | 1,631 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: conll
pretty_name: CoNLL++
train-eval-index:
- config: conllpp
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
0: '"'
1: ''''''
2: '#'
3: $
4: (
5: )
6: ','
7: .
8: ':'
9: '``'
10: CC
11: CD
12: DT
13: EX
14: FW
15: IN
16: JJ
17: JJR
18: JJS
19: LS
20: MD
21: NN
22: NNP
23: NNPS
24: NNS
25: NN|SYM
26: PDT
27: POS
28: PRP
29: PRP$
30: RB
31: RBR
32: RBS
33: RP
34: SYM
35: TO
36: UH
37: VB
38: VBD
39: VBG
40: VBN
41: VBP
42: VBZ
43: WDT
44: WP
45: WP$
46: WRB
- name: chunk_tags
sequence:
class_label:
names:
0: O
1: B-ADJP
2: I-ADJP
3: B-ADVP
4: I-ADVP
5: B-CONJP
6: I-CONJP
7: B-INTJ
8: I-INTJ
9: B-LST
10: I-LST
11: B-NP
12: I-NP
13: B-PP
14: I-PP
15: B-PRT
16: I-PRT
17: B-SBAR
18: I-SBAR
19: B-UCP
20: I-UCP
21: B-VP
22: I-VP
- name: ner_tags
sequence:
class_label:
names:
0: O
1: B-PER
2: I-PER
3: B-ORG
4: I-ORG
5: B-LOC
6: I-LOC
7: B-MISC
8: I-MISC
config_name: conllpp
splits:
- name: train
num_bytes: 6931393
num_examples: 14041
- name: validation
num_bytes: 1739247
num_examples: 3250
- name: test
num_bytes: 1582078
num_examples: 3453
download_size: 4859600
dataset_size: 10252718
---
# Dataset Card for "conllpp"
## 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/ZihanWangKi/CrossWeigh)
- **Repository:** [Github](https://github.com/ZihanWangKi/CrossWeigh)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/D19-1519)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set
have been manually corrected. The training set and development set from CoNLL2003 is included for completeness. One
correction on the test set for example, is:
```
{
"tokens": ["SOCCER", "-", "JAPAN", "GET", "LUCKY", "WIN", ",", "CHINA", "IN", "SURPRISE", "DEFEAT", "."],
"original_ner_tags_in_conll2003": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-PER", "O", "O", "O", "O"],
"corrected_ner_tags_in_conllpp": ["O", "O", "B-LOC", "O", "O", "O", "O", "B-LOC", "O", "O", "O", "O"],
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
#### conllpp
- **Size of downloaded dataset files:** 4.85 MB
- **Size of the generated dataset:** 10.26 MB
- **Total amount of disk used:** 15.11 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
```
### Data Fields
The data fields are the same among all splits.
#### conllpp
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
- `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4).
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|conll2003|14041| 3250|3453|
## 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
```
@inproceedings{wang2019crossweigh,
title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={5157--5166},
year={2019}
}
```
### Contributions
Thanks to [@ZihanWangKi](https://github.com/ZihanWangKi) for adding this dataset. | 7,704 | [
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] |
C-MTEB/EcomRetrieval | 2023-07-28T09:37:55.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,627 | 2023-07-28T09:37:40 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 9930587
num_examples: 100902
- name: queries
num_bytes: 32376
num_examples: 1000
download_size: 8448455
dataset_size: 9962963
---
# Dataset Card for "EcomRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 583 | [
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alkzar90/CC6204-Hackaton-Cub-Dataset | 2023-01-12T12:14:32.000Z | [
"task_categories:image-classification",
"task_categories:text-classification",
"task_ids:multi-class-image-classification",
"size_categories:10K<n<15K",
"source_datasets:extended|other",
"language:en",
"license:apache-2.0",
"region:us"
] | alkzar90 | null | null | 5 | 1,625 | 2022-11-24T13:29:55 | ---
language:
- en
license:
- apache-2.0
pretty_name: CC6204-Hackaton-CUB200
size_categories:
- 10K<n<15K
source_datasets:
- extended|other
paperswithcode_id: cub-200-2011
task_categories:
- image-classification
- text-classification
task_ids:
- multi-class-image-classification
---
## Dataset Description
- **Homepage:** [CUB 200 2011](http://www.vision.caltech.edu/datasets/cub_200_2011/)
- **Repository:** [Caltech Vision Lab](http://www.vision.caltech.edu/datasets/cub_200_2011/)
- **Paper:** [The Caltech-UCSD Birds-200-2011 Dataset](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf)
- **Leaderboard:** [Paperswithcode](https://paperswithcode.com/dataset/cub-200-2011)
- **Point of Contact:** [Catherine Wah](https://scholar.google.com/citations?user=rCDdLUsAAAAJ&hl=en)
# CC6204: Hackaton Deep Learning 2022
**Nota:** esta fue un actividad del curso CC6204: Deep Learning, Universidad de Chile, año 2022. Dictado por el profesor Iván Sipiran, material del curso [aquí](https://github.com/ivansipiran/CC6204-Deep-Learning).
En esta actividad intentaremos resolver un problema de clasificación multimodal. En un problema de clasificación multimodal, cada pieza de información viene en diferentes representaciones (imágenes, texto, audios, etc) y la idea es determinar cómo usar esos datos para un problema de clasificación.
En este caso trabajaremos con un dataset que contiene datos sobre especies de pájaros.
## Dataset
### Data Instances
Una muestra del _dataset_ se encuentra a continuación:
```
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=334x500 at 0x7F59DE348AF0>,
'description': 'this bird has a short orange bill, white breast and body and white eyes.\na medium sized bird with a orange bill and a black crown and white eyes\nthis white-breasted bird has a short, squat, orange bill, a black head and wings, and small white eyes above a white stripe.\nthis bird has a white breast, a black head, a short red beak, and webbed feet.\nthis bird is white with black on its neck and has a long, pointy beak.\nthis bird has wings that are black and has a white belly\nthis bird has wings that are black and has a long bill\nthis is a medium sized bird, with a white belly, and a grey head and wings, with a short yellow bill.\nthis bird is white and gray in color, and has a bright orange beak.\nthis bird has a blunt orange beak with mostly black above the neck, the belly is solid white.\n',
'label': 6,
'file_name': 'Parakeet_Auklet_0048_795980.jpg'}
```
### Data Fields
Cada instancia de datos tiene los siguientes campos:
- `image`: imagen RGB de un pájaro
- `description`: texto con 10 descripciones del pájaro en la foto, cada descripción esta separado por un salto de linea (i.e. `\n`)
- `label`: un número entero que representa el id de la especie a la que pertenece el pájaro
<details>
<summary>Id2String</summary>
```bash
1 001.Black_footed_Albatross
2 002.Laysan_Albatross
3 003.Sooty_Albatross
4 004.Groove_billed_Ani
5 005.Crested_Auklet
6 006.Least_Auklet
7 007.Parakeet_Auklet
8 008.Rhinoceros_Auklet
9 009.Brewer_Blackbird
10 010.Red_winged_Blackbird
11 011.Rusty_Blackbird
12 012.Yellow_headed_Blackbird
13 013.Bobolink
14 014.Indigo_Bunting
15 015.Lazuli_Bunting
16 016.Painted_Bunting
17 017.Cardinal
18 018.Spotted_Catbird
19 019.Gray_Catbird
20 020.Yellow_breasted_Chat
21 021.Eastern_Towhee
22 022.Chuck_will_Widow
23 023.Brandt_Cormorant
24 024.Red_faced_Cormorant
25 025.Pelagic_Cormorant
26 026.Bronzed_Cowbird
27 027.Shiny_Cowbird
28 028.Brown_Creeper
29 029.American_Crow
30 030.Fish_Crow
31 031.Black_billed_Cuckoo
32 032.Mangrove_Cuckoo
33 033.Yellow_billed_Cuckoo
34 034.Gray_crowned_Rosy_Finch
35 035.Purple_Finch
36 036.Northern_Flicker
37 037.Acadian_Flycatcher
38 038.Great_Crested_Flycatcher
39 039.Least_Flycatcher
40 040.Olive_sided_Flycatcher
41 041.Scissor_tailed_Flycatcher
42 042.Vermilion_Flycatcher
43 043.Yellow_bellied_Flycatcher
44 044.Frigatebird
45 045.Northern_Fulmar
46 046.Gadwall
47 047.American_Goldfinch
48 048.European_Goldfinch
49 049.Boat_tailed_Grackle
50 050.Eared_Grebe
51 051.Horned_Grebe
52 052.Pied_billed_Grebe
53 053.Western_Grebe
54 054.Blue_Grosbeak
55 055.Evening_Grosbeak
56 056.Pine_Grosbeak
57 057.Rose_breasted_Grosbeak
58 058.Pigeon_Guillemot
59 059.California_Gull
60 060.Glaucous_winged_Gull
61 061.Heermann_Gull
62 062.Herring_Gull
63 063.Ivory_Gull
64 064.Ring_billed_Gull
65 065.Slaty_backed_Gull
66 066.Western_Gull
67 067.Anna_Hummingbird
68 068.Ruby_throated_Hummingbird
69 069.Rufous_Hummingbird
70 070.Green_Violetear
71 071.Long_tailed_Jaeger
72 072.Pomarine_Jaeger
73 073.Blue_Jay
74 074.Florida_Jay
75 075.Green_Jay
76 076.Dark_eyed_Junco
77 077.Tropical_Kingbird
78 078.Gray_Kingbird
79 079.Belted_Kingfisher
80 080.Green_Kingfisher
81 081.Pied_Kingfisher
82 082.Ringed_Kingfisher
83 083.White_breasted_Kingfisher
84 084.Red_legged_Kittiwake
85 085.Horned_Lark
86 086.Pacific_Loon
87 087.Mallard
88 088.Western_Meadowlark
89 089.Hooded_Merganser
90 090.Red_breasted_Merganser
91 091.Mockingbird
92 092.Nighthawk
93 093.Clark_Nutcracker
94 094.White_breasted_Nuthatch
95 095.Baltimore_Oriole
96 096.Hooded_Oriole
97 097.Orchard_Oriole
98 098.Scott_Oriole
99 099.Ovenbird
100 100.Brown_Pelican
101 101.White_Pelican
102 102.Western_Wood_Pewee
103 103.Sayornis
104 104.American_Pipit
105 105.Whip_poor_Will
106 106.Horned_Puffin
107 107.Common_Raven
108 108.White_necked_Raven
109 109.American_Redstart
110 110.Geococcyx
111 111.Loggerhead_Shrike
112 112.Great_Grey_Shrike
113 113.Baird_Sparrow
114 114.Black_throated_Sparrow
115 115.Brewer_Sparrow
116 116.Chipping_Sparrow
117 117.Clay_colored_Sparrow
118 118.House_Sparrow
119 119.Field_Sparrow
120 120.Fox_Sparrow
121 121.Grasshopper_Sparrow
122 122.Harris_Sparrow
123 123.Henslow_Sparrow
124 124.Le_Conte_Sparrow
125 125.Lincoln_Sparrow
126 126.Nelson_Sharp_tailed_Sparrow
127 127.Savannah_Sparrow
128 128.Seaside_Sparrow
129 129.Song_Sparrow
130 130.Tree_Sparrow
131 131.Vesper_Sparrow
132 132.White_crowned_Sparrow
133 133.White_throated_Sparrow
134 134.Cape_Glossy_Starling
135 135.Bank_Swallow
136 136.Barn_Swallow
137 137.Cliff_Swallow
138 138.Tree_Swallow
139 139.Scarlet_Tanager
140 140.Summer_Tanager
141 141.Artic_Tern
142 142.Black_Tern
143 143.Caspian_Tern
144 144.Common_Tern
145 145.Elegant_Tern
146 146.Forsters_Tern
147 147.Least_Tern
148 148.Green_tailed_Towhee
149 149.Brown_Thrasher
150 150.Sage_Thrasher
151 151.Black_capped_Vireo
152 152.Blue_headed_Vireo
153 153.Philadelphia_Vireo
154 154.Red_eyed_Vireo
155 155.Warbling_Vireo
156 156.White_eyed_Vireo
157 157.Yellow_throated_Vireo
158 158.Bay_breasted_Warbler
159 159.Black_and_white_Warbler
160 160.Black_throated_Blue_Warbler
161 161.Blue_winged_Warbler
162 162.Canada_Warbler
163 163.Cape_May_Warbler
164 164.Cerulean_Warbler
165 165.Chestnut_sided_Warbler
166 166.Golden_winged_Warbler
167 167.Hooded_Warbler
168 168.Kentucky_Warbler
169 169.Magnolia_Warbler
170 170.Mourning_Warbler
171 171.Myrtle_Warbler
172 172.Nashville_Warbler
173 173.Orange_crowned_Warbler
174 174.Palm_Warbler
175 175.Pine_Warbler
176 176.Prairie_Warbler
177 177.Prothonotary_Warbler
178 178.Swainson_Warbler
179 179.Tennessee_Warbler
180 180.Wilson_Warbler
181 181.Worm_eating_Warbler
182 182.Yellow_Warbler
183 183.Northern_Waterthrush
184 184.Louisiana_Waterthrush
185 185.Bohemian_Waxwing
186 186.Cedar_Waxwing
187 187.American_Three_toed_Woodpecker
188 188.Pileated_Woodpecker
189 189.Red_bellied_Woodpecker
190 190.Red_cockaded_Woodpecker
191 191.Red_headed_Woodpecker
192 192.Downy_Woodpecker
193 193.Bewick_Wren
194 194.Cactus_Wren
195 195.Carolina_Wren
196 196.House_Wren
197 197.Marsh_Wren
198 198.Rock_Wren
199 199.Winter_Wren
200 200.Common_Yellowthroat
```
</details>
- `file_name`: nombre del archivo que tiene la imagen
### Data Splits
| |train| test|
|------------------|----:|----:|
|# de observaciones|5994 |5794 |
## Problema
El problema consiste en entrenar un modelo que clasifique instancias del dataset CUB de la mejor manera posible. Algunas preguntas que podrían guiar nuestro desarrollo son:
* Se podrá obtener un buen _performance_ de clasificación solo usando las imágenes del dataset? Este tipo de problema sería el clásico problema de clasificar imágenes.
* Se podrá obtener un buen _performance_ de clasificación solo usando los textos del dataset? Este tipo de problema sería el clásico problema de clasificar texto.
* Se podrá obtener un mejor _performance_ si combino la información en un modelo multimodal? Cómo construyo un modelo multimodal que reciba una imagen y un texto y clasifique la instancia con su respectiva especie? Hint: piense en cómo una red neuronal (la que sea) es simplemente una función que recibe un dato y genera una representación de alto nivel (vector característico) de ese dato. Una red CNN podría hacerse cargo de calcular la representación de una imagen y una red RNN podría hacerse cargo de calcular la representación del texto. Finalmente concateno ambas representaciones y entreno un MLP final que hace la clasificación.
## Experimentación
Como el dataset es grande y los recursos de computación son muy limitados, una estrategia para hacer los experimentos es tomar una muestra más pequeña de datos para ir probando las ideas. Para esta estrategia, éstas son dos ideas válidas:
* Tomar menos instancias por cada clase para el desarrollo y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.
* Tomar menos clases para el desarrollo inicial y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.
Ambas estrategias nos permiten lidiar con los recursos limitados que tenemos, pero cuáles son sus ventajas o desventajas? Si usas alguna de estas estrategias, puedes comentar este punto en tu desarrollo final.
## Métrica de Evaluación
La métrica que se debe reportar es el accuracy en conjunto de test.
## Citation Information
Sitio web del [_dataset_ CUB200](http://www.vision.caltech.edu/datasets/cub_200_2011/), y reporte técnico [aquí](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf).
```
@techreport{WahCUB_200_2011,
Title = The Caltech-UCSD Birds-200-2011 Dataset,
Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.},
Year = {2011}
Institution = {California Institute of Technology},
Number = {CNS-TR-2011-001}
}
```
## Contributions
Creación y adaptación del material de la actividad en un Hugging Face dataset por Cristóbal Alcázar.
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laion/laion1b-nolang-vit-l-14-embeddings | 2022-12-16T17:53:26.000Z | [
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] |
aeslc | 2023-04-05T08:32:58.000Z | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"aspect-based-summarization",
"conversations-summarization",
"multi-document-summarization",
"email-headline-generation",
"arxiv:1906.03497",
"region:us"
] | null | A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text. | @misc{zhang2019email,
title={This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation},
author={Rui Zhang and Joel Tetreault},
year={2019},
eprint={1906.03497},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 5 | 1,620 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: 'AESLC: Annotated Enron Subject Line Corpus'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: aeslc
tags:
- aspect-based-summarization
- conversations-summarization
- multi-document-summarization
- email-headline-generation
dataset_info:
features:
- name: email_body
dtype: string
- name: subject_line
dtype: string
splits:
- name: train
num_bytes: 11902668
num_examples: 14436
- name: validation
num_bytes: 1660730
num_examples: 1960
- name: test
num_bytes: 1384177
num_examples: 1906
download_size: 11643743
dataset_size: 14947575
---
# Dataset Card for "aeslc"
## 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:** https://github.com/ryanzhumich/AESLC
- **Paper:** [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://arxiv.org/abs/1906.03497)
- **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:** 11.64 MB
- **Size of the generated dataset:** 14.95 MB
- **Total amount of disk used:** 26.59 MB
### Dataset Summary
A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
Monolingual English (mainly en-US) with some exceptions.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 11.64 MB
- **Size of the generated dataset:** 14.95 MB
- **Total amount of disk used:** 26.59 MB
An example of 'train' looks as follows.
```
{
"email_body": "B/C\n<<some doc>>\n",
"subject_line": "Service Agreement"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `email_body`: a `string` feature.
- `subject_line`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|14436| 1960|1906|
## 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{zhang-tetreault-2019-email,
title = "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation",
author = "Zhang, Rui and
Tetreault, Joel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1043",
doi = "10.18653/v1/P19-1043",
pages = "446--456",
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset. | 6,163 | [
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] |
guardian_authorship | 2023-04-05T10:06:55.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013.
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).
3- The same-topic/genre scenario is created by grouping all the datasts as follows.
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
split='train[-40%:]+validation[-40%:]+test[-40%:]')
IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced
* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples | @article{article,
author = {Stamatatos, Efstathios},
year = {2013},
month = {01},
pages = {421-439},
title = {On the robustness of authorship attribution based on character n-gram features},
volume = {21},
journal = {Journal of Law and Policy}
}
@inproceedings{stamatatos2017authorship,
title={Authorship attribution using text distortion},
author={Stamatatos, Efstathios},
booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},
volume={1}
pages={1138--1149},
year={2017}
} | 3 | 1,617 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
pretty_name: GuardianAuthorship
dataset_info:
- config_name: cross_topic_1
features:
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names:
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'1': georgemonbiot
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'7': peterpreston
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dtype:
class_label:
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- config_name: cross_topic_2
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- config_name: cross_topic_3
features:
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- config_name: cross_topic_4
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- config_name: cross_topic_5
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features:
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download_size: 3100749
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- name: author
dtype:
class_label:
names:
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'1': georgemonbiot
'2': hugoyoung
'3': jonathanfreedland
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'5': maryriddell
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download_size: 3100749
dataset_size: 2740710
---
# Dataset Card for "guardian_authorship"
## 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.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf](http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf)
- **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:** 49.61 MB
- **Size of the generated dataset:** 38.98 MB
- **Total amount of disk used:** 88.59 MB
### Dataset Summary
A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013.
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).
3- The same-topic/genre scenario is created by grouping all the datasts as follows.
For ex., to use same_topic and split the data 60-40 use:
train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
split='train[:60%]+validation[:60%]+test[:60%]')
tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>",
split='train[-40%:]+validation[-40%:]+test[-40%:]')
IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced
* See https://huggingface.co/docs/datasets/splits.html for detailed/more examples
### 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
#### cross_genre_1
- **Size of downloaded dataset files:** 3.10 MB
- **Size of the generated dataset:** 2.74 MB
- **Total amount of disk used:** 5.84 MB
An example of 'train' looks as follows.
```
{
"article": "File 1a\n",
"author": 0,
"topic": 4
}
```
#### cross_genre_2
- **Size of downloaded dataset files:** 3.10 MB
- **Size of the generated dataset:** 2.74 MB
- **Total amount of disk used:** 5.84 MB
An example of 'validation' looks as follows.
```
{
"article": "File 1a\n",
"author": 0,
"topic": 1
}
```
#### cross_genre_3
- **Size of downloaded dataset files:** 3.10 MB
- **Size of the generated dataset:** 2.74 MB
- **Total amount of disk used:** 5.84 MB
An example of 'validation' looks as follows.
```
{
"article": "File 1a\n",
"author": 0,
"topic": 2
}
```
#### cross_genre_4
- **Size of downloaded dataset files:** 3.10 MB
- **Size of the generated dataset:** 2.74 MB
- **Total amount of disk used:** 5.84 MB
An example of 'validation' looks as follows.
```
{
"article": "File 1a\n",
"author": 0,
"topic": 3
}
```
#### cross_topic_1
- **Size of downloaded dataset files:** 3.10 MB
- **Size of the generated dataset:** 2.34 MB
- **Total amount of disk used:** 5.43 MB
An example of 'validation' looks as follows.
```
{
"article": "File 1a\n",
"author": 0,
"topic": 1
}
```
### Data Fields
The data fields are the same among all splits.
#### cross_genre_1
- `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4).
- `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4).
- `article`: a `string` feature.
#### cross_genre_2
- `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4).
- `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4).
- `article`: a `string` feature.
#### cross_genre_3
- `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4).
- `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4).
- `article`: a `string` feature.
#### cross_genre_4
- `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4).
- `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4).
- `article`: a `string` feature.
#### cross_topic_1
- `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4).
- `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4).
- `article`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------------|----:|---------:|---:|
|cross_genre_1| 63| 112| 269|
|cross_genre_2| 63| 62| 319|
|cross_genre_3| 63| 90| 291|
|cross_genre_4| 63| 117| 264|
|cross_topic_1| 112| 62| 207|
## 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{article,
author = {Stamatatos, Efstathios},
year = {2013},
month = {01},
pages = {421-439},
title = {On the robustness of authorship attribution based on character n-gram features},
volume = {21},
journal = {Journal of Law and Policy}
}
@inproceedings{stamatatos2017authorship,
title={Authorship attribution using text distortion},
author={Stamatatos, Efstathios},
booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},
volume={1}
pages={1138--1149},
year={2017}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@eltoto1219](https://github.com/eltoto1219), [@malikaltakrori](https://github.com/malikaltakrori) for adding this dataset. | 24,474 | [
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banghua/random_pre | 2023-10-28T02:55:26.000Z | [
"region:us"
] | banghua | null | null | 0 | 1,617 | 2023-10-28T02:48:38 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: answers
list:
- name: answer
dtype: string
- name: model
dtype: string
- name: rank
dtype: float64
- name: turns
dtype: int64
- name: num_responses
dtype: int64
- name: source
sequence: string
splits:
- name: train
num_bytes: 1206940856
num_examples: 182968
download_size: 551450326
dataset_size: 1206940856
---
# Dataset Card for "random_pre"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 705 | [
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huggingface/semantic-segmentation-test-sample | 2022-04-11T09:15:24.000Z | [
"region:us"
] | huggingface | null | null | 0 | 1,614 | 2022-04-11T09:12:00 | This dataset contains 10 examples of the [segments/sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic) dataset (i.e. 10 images with corresponding ground-truth segmentation maps). | 205 | [
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] |
C-MTEB/MedicalRetrieval | 2023-07-28T09:33:59.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,614 | 2023-07-28T09:33:27 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 37393271
num_examples: 100999
- name: queries
num_bytes: 63649
num_examples: 1000
download_size: 25077981
dataset_size: 37456920
---
# Dataset Card for "MedicalRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 589 | [
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YeungNLP/ultrachat | 2023-06-19T02:52:43.000Z | [
"region:us"
] | YeungNLP | null | null | 14 | 1,611 | 2023-06-18T16:58:11 | Entry not found | 15 | [
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] |
BeIR/scifact | 2022-10-23T06:01:22.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 1 | 1,601 | 2022-06-05T16:24:20 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
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google/MusicCaps | 2023-03-08T14:37:09.000Z | [
"task_categories:text-to-speech",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2301.11325",
"region:us"
] | google | null | null | 79 | 1,601 | 2023-01-27T16:26:11 | ---
license:
- cc-by-sa-4.0
converted_from: kaggle
kaggle_id: googleai/musiccaps
task_categories:
- text-to-speech
language:
- en
---
# Dataset Card for MusicCaps
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://kaggle.com/datasets/googleai/musiccaps
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The MusicCaps dataset contains **5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians**. An aspect list is for example *"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead"*, while the caption consists of multiple sentences about the music, e.g.,
*"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar."*
The text is solely focused on describing *how* the music sounds, not the metadata like the artist name.
The labeled examples are 10s music clips from the [**AudioSet**](https://research.google.com/audioset/) dataset (2,858 from the eval and 2,663 from the train split).
Please cite the corresponding paper, when using this dataset: http://arxiv.org/abs/2301.11325 (DOI: `10.48550/arXiv.2301.11325`)
### Dataset Usage
The published dataset takes the form of a `.csv` file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times.
The following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: https://github.com/nateraw/download-musiccaps-dataset
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
#### ytid
YT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening https://youtu.be/watch?v={ytid}&start={start_s}
#### start_s
Position in the YouTube video at which the music starts.
#### end_s
Position in the YouTube video at which the music end. All clips are 10s long.
#### audioset_positive_labels
Labels for this segment from the AudioSet (https://research.google.com/audioset/) dataset.
#### aspect_list
A list of aspects describing the music.
#### caption
A multi-sentence free text caption describing the music.
#### author_id
An integer for grouping samples by who wrote them.
#### is_balanced_subset
If this value is true, the row is a part of the 1k subset which is genre-balanced.
#### is_audioset_eval
If this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was shared by [@googleai](https://ai.google/research/)
### Licensing Information
The license for this dataset is cc-by-sa-4.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] | 5,062 | [
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] |
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