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| likes
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FreedomIntelligence/ACVA-Arabic-Cultural-Value-Alignment
|
FreedomIntelligence
| 2023-09-21T12:39:18Z | 55 | 0 |
[
"size_categories:1K<n<10K",
"language:ar",
"license:apache-2.0",
"region:us"
] | null | 2023-09-15T10:18:04Z |
---
language:
- ar
viewer: true
license: apache-2.0
size_categories:
- 1K<n<10K
---
# About ArabicCulture
The ArabicCulture dataset was generated by gpt3.5 and contains 8000+ True and False questions.
The dataset contains questions from 58 different areas.
In the answers, "True" accounted for 59.62%, and "False" accounted for 40.38%
# data-all
It contains 8000+ data, and we took 5 data from each area as few-shot data.
# data-select
We asked two Arabs to judge 4000 of all the data for us, and we left data that two Arabs both thought were good. Finally, we got 2.4k data covering 9 areas.
We divided them into test sets and validation sets as above.
|
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kunishou/amenokaku-code-instruct
|
kunishou
| 2023-10-03T11:38:43Z | 55 | 4 |
[
"license:other",
"region:us"
] | null | 2023-10-01T01:04:50Z |
---
license: other
license_name: mixed-liscence
license_link: LICENSE
---
# AmenokakuCode-instruct
## 概要
- コードに特化した5.2KのInstructionデータセットです。
- データセットに含まれるデータは商用利用できるラインセンスが付与されたプログラミング学習コンテンツから収集、加工し作成しました（英語のコンテンツは日本語に自動翻訳し、翻訳の不自然な箇所を手動で修正）。
- また、ライセンスが明記されていない学習コンテンツについては権利者に個別に連絡を取り、本データセットへの掲載の許諾を得ております。
## データセット詳細
指示タスクの内訳としてはコード生成（code_generation）が1050レコード、コードの挙動確認（check_code_behavor）が150レコード、コードのバグ修正（code_fix）が4000レコードになります。
詳細な内訳は以下の通りになります。
|source name|num record|liscence|url|
|:----|:----|:----|:----|
|データサイエンス100本ノック（構造化データ加工編）（Python解答）|100|[MIT](https://github.com/The-Japan-DataScientist-Society/100knocks-preprocess/blob/master/LICENSE)|https://github.com/The-Japan-DataScientist-Society/100knocks-preprocess|
|データサイエンス100本ノック（構造化データ加工編）（SQL解答）|100|[MIT](https://github.com/rootassist/100knocks-preprocess-inSQLandPython-withColab/blob/master/LICENSE)|https://github.com/rootassist/100knocks-preprocess-inSQLandPython-withColab|
|画像処理100本ノック|100|[MIT](https://github.com/ryoppippi/Gasyori100knock/blob/master/LICENSE)|https://github.com/ryoppippi/Gasyori100knock|
|言語処理100本ノック2020|100|[MIT](https://github.com/nlp100/nlp100.github.io/blob/develop/LICENSE)<br>[MIT](https://github.com/upura/nlp100v2020/blob/master/LICENSE)|(問題) https://github.com/nlp100/nlp100.github.io<br>(解答) https://github.com/upura/nlp100v2020|
|Python初学者のためのpandas100本ノック※|100|AmenokakuCode Liscence|https://qiita.com/kunishou/items/bd5fad9a334f4f5be51c|
|Python初学者のためのPolars100本ノック※|100|AmenokakuCode Liscence|https://qiita.com/kunishou/items/1386d14a136f585e504e|
|100 Numpy Execieses|100|[MIT](https://github.com/rougier/numpy-100/blob/master/LICENSE.txt)|https://github.com/rougier/numpy-100|
|100 Julia Exercises|100|The Unliscence|https://github.com/RoyiAvital/Julia100Exercises|
|自作Python100本ノック|100|AmenokakuCode Liscence|https://qiita.com/ahpjop/items/373f807d68044cda1c9b|
|Python-for-Beginners-Solve-50-Exercises-Live|50|[MIT](https://github.com/garg10may/Python-for-Beginners-Solve-50-Exercises-Live/blob/master/LICENSE)|https://github.com/garg10may/Python-for-Beginners-Solve-50-Exercises-Live|
|R初学者のためのtidyverse100本ノック|100|AmenokakuCode Liscence|https://qiita.com/nekobo/items/cbf32a13637273f229da|
|JavaScript Questions|155|[MIT](https://github.com/lydiahallie/javascript-questions/blob/master/LICENSE)|https://github.com/lydiahallie/javascript-questions|
|Break-It-Fix-It|4,000|[MIT](https://github.com/michiyasunaga/BIFI/blob/main/LICENSE)|https://github.com/michiyasunaga/BIFI|
※ 私が過去に作成した学習コンテンツです。
## ライセンス
個々のデータのライセンスは収集元のライセンスに従うため、データセット全体では混合ライセンスになります。
また、データ自体にライセンスが明記されておらず個別に権利者に言語モデル学習用途でデータセットへの掲載許諾を取ったデータに関しては [AmenokakuCode Liscence](https://github.com/kunishou/amenokaku-code-instruct/blob/main/AmenokakuCode%20Liscence) というライセンスを付与しています。このライセンスは、言語モデルでの学習用途に限り自由にデータを利用することを許可するものになります（そのため、データ自体を販売したり、配布することは認めていません）。
## データセットの更新
データセットについては、商用利用可能なプログラミング学習コンテンツを見つけたら今後随時追加していきたいと思います。
**もし、有益なコンテンツを見つけたり、自身で作成した学習コンテンツを提供しても良いという方がおりましたら是非ご連絡下さい。**
## データセット名
Amenokaku は古事記に登場する[天迦久神](http://kojiki.kokugakuin.ac.jp/shinmei/amenokakunokami/)（あめのかくのかみ）という鹿の神様の名前を参考にしました。
## Github
https://github.com/kunishou/amenokaku-code-instruct
|
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lhallee/uniref_small
|
lhallee
| 2023-10-04T03:12:15Z | 55 | 0 |
[
"region:us"
] | null | 2023-10-04T03:12:13Z |
---
dataset_info:
features:
- name: uniref
dtype: string
splits:
- name: train
num_bytes: 20739509
num_examples: 100000
download_size: 20824692
dataset_size: 20739509
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "uniref_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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layoric/tiny-codes-alpaca
|
layoric
| 2023-10-08T02:28:04Z | 55 | 0 |
[
"region:us"
] | null | 2023-10-08T02:25:40Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: main_topic
dtype: string
- name: subtopic
dtype: string
- name: adjective
dtype: string
- name: action_verb
dtype: string
- name: scenario
dtype: string
- name: target_audience
dtype: string
- name: programming_language
dtype: string
- name: common_sense_topic
dtype: string
- name: idx
dtype: int64
- name: output
dtype: string
splits:
- name: train
num_bytes: 3795436393
num_examples: 1632309
download_size: 1642754203
dataset_size: 3795436393
---
# Dataset Card for "tiny-codes-alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
pythainlp/thai_usembassy
|
pythainlp
| 2023-10-20T14:34:38Z | 55 | 0 |
[
"task_categories:text-generation",
"task_categories:translation",
"language:th",
"language:en",
"license:cc0-1.0",
"region:us"
] |
[
"text-generation",
"translation"
] | 2023-10-14T14:14:38Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: url
dtype: string
- name: th
dtype: string
- name: en
dtype: string
- name: title_en
dtype: string
- name: title_th
dtype: string
splits:
- name: train
num_bytes: 5060813
num_examples: 615
download_size: 2048306
dataset_size: 5060813
license: cc0-1.0
task_categories:
- text-generation
- translation
language:
- th
- en
---
# Dataset Card for "thai_usembassy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
This dataset collect all Thai & English news from [U.S. Embassy Bangkok](https://th.usembassy.gov/news-events/).
|
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zkdeng/combinedSpidersBalanced
|
zkdeng
| 2023-10-19T01:35:11Z | 55 | 0 |
[
"region:us"
] | null | 2023-10-19T00:46:30Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Acantholycosa_lignaria
'1': Aculepeira_ceropegia
'2': Agalenatea_redii
'3': Agelena_labyrinthica
'4': Aglaoctenus_castaneus
'5': Aglaoctenus_lagotis
'6': Allocosa_funerea
'7': Allotrochosina_schauinslandi
'8': Alopecosa_albofasciata
'9': Alopecosa_barbipes
'10': Alopecosa_cuneata
'11': Alopecosa_inquilina
'12': Alopecosa_kochi
'13': Alopecosa_pulverulenta
'14': Anahita_punctulata
'15': Anasaitis_canosa
'16': Ancylometes_bogotensis
'17': Ancylometes_concolor
'18': Ancylometes_rufus
'19': Anoteropsis_hilaris
'20': Anoteropsis_litoralis
'21': Anyphaena_accentuata
'22': Aphonopelma_hentzi
'23': Araneus_diadematus
'24': Araneus_marmoreus
'25': Araneus_quadratus
'26': Araneus_trifolium
'27': Araniella_displicata
'28': Arctosa_cinerea
'29': Arctosa_leopardus
'30': Arctosa_littoralis
'31': Arctosa_perita
'32': Arctosa_personata
'33': Argiope_argentata
'34': Argiope_aurantia
'35': Argiope_bruennichi
'36': Argiope_keyserlingi
'37': Argiope_lobata
'38': Argiope_trifasciata
'39': Asthenoctenus_borellii
'40': Attulus_fasciger
'41': Aulonia_albimana
'42': Austracantha_minax
'43': Badumna_longinqua
'44': Carrhotus_xanthogramma
'45': Centroctenus_brevipes
'46': Cheiracanthium_erraticum
'47': Cheiracanthium_gracile
'48': Cheiracanthium_inclusum
'49': Cheiracanthium_mildei
'50': Cheiracanthium_punctorium
'51': Colonus_hesperus
'52': Colonus_sylvanus
'53': Ctenus_amphora
'54': Ctenus_hibernalis
'55': Ctenus_medius
'56': Ctenus_ornatus
'57': Cupiennius_coccineus
'58': Cupiennius_getazi
'59': Cupiennius_salei
'60': Cyclosa_conica
'61': Cyrtophora_citricola
'62': Diapontia_uruguayensis
'63': Dolomedes_albineus
'64': Dolomedes_minor
'65': Dolomedes_scriptus
'66': Dolomedes_tenebrosus
'67': Dolomedes_triton
'68': Dysdera_crocata
'69': Ebrechtella_tricuspidata
'70': Enoplognatha_ovata
'71': Eratigena_agrestis
'72': Eratigena_duellica
'73': Eriophora_ravilla
'74': Eris_militaris
'75': Evarcha_arcuata
'76': Gasteracantha_cancriformis
'77': Geolycosa_vultuosa
'78': Gladicosa_gulosa
'79': Gladicosa_pulchra
'80': Habronattus_pyrrithrix
'81': Hasarius_adansoni
'82': Helpis_minitabunda
'83': Hentzia_mitrata
'84': Hentzia_palmarum
'85': Herpyllus_ecclesiasticus
'86': Heteropoda_venatoria
'87': Hippasa_holmerae
'88': Hogna_antelucana
'89': Hogna_baltimoriana
'90': Hogna_bivittata
'91': Hogna_carolinensis
'92': Hogna_crispipes
'93': Hogna_frondicola
'94': Hogna_gumia
'95': Hogna_radiata
'96': Holocnemus_pluchei
'97': Kukulcania_hibernalis
'98': Lampona_cylindrata
'99': Larinioides_cornutus
'100': Larinioides_sclopetarius
'101': Latrodectus_bishopi
'102': Latrodectus_curacaviensis
'103': Latrodectus_geometricus
'104': Latrodectus_hasselti
'105': Latrodectus_hesperus
'106': Latrodectus_katipo
'107': Latrodectus_mactans
'108': Latrodectus_mirabilis
'109': Latrodectus_renivulvatus
'110': Latrodectus_tredecimguttatus
'111': Latrodectus_variolus
'112': Leucauge_argyra
'113': Leucauge_argyrobapta
'114': Leucauge_dromedaria
'115': Leucauge_venusta
'116': Loxosceles_amazonica
'117': Loxosceles_deserta
'118': Loxosceles_laeta
'119': Loxosceles_reclusa
'120': Loxosceles_rufescens
'121': Loxosceles_tenochtitlan
'122': Loxosceles_yucatana
'123': Lycosa_erythrognatha
'124': Lycosa_hispanica
'125': Lycosa_pampeana
'126': Lycosa_praegrandis
'127': Lycosa_singoriensis
'128': Lycosa_tarantula
'129': Lyssomanes_viridis
'130': Maevia_inclemens
'131': Mangora_acalypha
'132': Maratus_griseus
'133': Marpissa_muscosa
'134': Mecynogea_lemniscata
'135': Menemerus_bivittatus
'136': Menemerus_semilimbatus
'137': Micrathena_gracilis
'138': Micrathena_sagittata
'139': Micrommata_virescens
'140': Missulena_bradleyi
'141': Missulena_occatoria
'142': Misumena_vatia
'143': Misumenoides_formosipes
'144': Misumessus_oblongus
'145': Naphrys_pulex
'146': Neoscona_arabesca
'147': Neoscona_crucifera
'148': Neoscona_oaxacensis
'149': Nephila_pilipes
'150': Neriene_radiata
'151': Nesticodes_rufipes
'152': Nuctenea_umbratica
'153': Oxyopes_salticus
'154': Oxyopes_scalaris
'155': Paraphidippus_aurantius
'156': Parasteatoda_tepidariorum
'157': Paratrochosina_amica
'158': Pardosa_amentata
'159': Pardosa_lapidicina
'160': Pardosa_mercurialis
'161': Pardosa_moesta
'162': Pardosa_wagleri
'163': Peucetia_viridans
'164': Phidippus_audax
'165': Phidippus_clarus
'166': Phidippus_johnsoni
'167': Phidippus_putnami
'168': Philaeus_chrysops
'169': Philodromus_dispar
'170': Pholcus_phalangioides
'171': Phoneutria_boliviensis
'172': Phoneutria_depilata
'173': Phoneutria_fera
'174': Phoneutria_nigriventer
'175': Phoneutria_pertyi
'176': Phoneutria_reidyi
'177': Pirata_piraticus
'178': Pisaura_mirabilis
'179': Pisaurina_mira
'180': Platycryptus_californicus
'181': Platycryptus_undatus
'182': Plebs_eburnus
'183': Plexippus_paykulli
'184': Portacosa_cinerea
'185': Rabidosa_hentzi
'186': Rabidosa_punctulata
'187': Rabidosa_rabida
'188': Salticus_scenicus
'189': Sassacus_vitis
'190': Schizocosa_avida
'191': Schizocosa_malitiosa
'192': Schizocosa_mccooki
'193': Scytodes_thoracica
'194': Sicarius_thomisoides
'195': Socca_pustulosa
'196': Sosippus_californicus
'197': Steatoda_grossa
'198': Steatoda_nobilis
'199': Steatoda_triangulosa
'200': Synema_globosum
'201': Thomisus_onustus
'202': Tigrosa_annexa
'203': Tigrosa_aspersa
'204': Tigrosa_georgicola
'205': Tigrosa_helluo
'206': Trichonephila_clavata
'207': Trichonephila_clavipes
'208': Trichonephila_edulis
'209': Trichonephila_plumipes
'210': Trochosa_ruricola
'211': Trochosa_sepulchralis
'212': Trochosa_terricola
'213': Tropicosa_moesta
'214': Venator_immansuetus
'215': Venator_spenceri
'216': Venatrix_furcillata
'217': Verrucosa_arenata
'218': Wadicosa_fidelis
'219': Xerolycosa_miniata
'220': Xerolycosa_nemoralis
'221': Zoropsis_spinimana
'222': Zygiella_x-notata
splits:
- name: train
num_bytes: 6290926802.784
num_examples: 269172
download_size: 5634209308
dataset_size: 6290926802.784
---
# Dataset Card for "combinedSpidersBalanced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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BrunoGR/emotional_response_spanish_dataset
|
BrunoGR
| 2023-11-21T06:47:22Z | 55 | 0 |
[
"region:us"
] | null | 2023-10-20T00:30:25Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: index
dtype: float64
- name: input
dtype: string
- name: output
dtype: string
- name: Prompt_sp
dtype: string
- name: Prompt_mix
dtype: string
- name: Prompt_en
dtype: string
splits:
- name: train
num_bytes: 139130014
num_examples: 41910
- name: test
num_bytes: 5047940
num_examples: 1320
- name: validation
num_bytes: 8297080
num_examples: 2220
download_size: 43129906
dataset_size: 152475034
---
# Dataset Card for "emotional_response_spanish_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jihyoung/ConversationChronicles
|
jihyoung
| 2023-10-23T01:33:39Z | 55 | 1 |
[
"task_categories:conversational",
"language:en",
"license:cc-by-4.0",
"arxiv:2310.13420",
"region:us"
] |
[
"conversational"
] | 2023-10-22T15:59:38Z |
---
license: cc-by-4.0
task_categories:
- conversational
language:
- en
pretty_name: CC
---
# 🕰️ Conversation Chronicles
We introduce Conversation Chronicles, a new high-quality 1M multi-session dataset that includes more various time intervals and fine-grained speaker relationships!
## Load with Datasets
To load our dataset with Hugging Face Datasets, please use the following code:
```python
from datasets import load_dataset
cc = load_dataset("jihyoung/ConversationChronicles")
```
## Languages
The language of Conversation Chronicles is ***English***.
## Dataset Size
| Feature | Conut |
| ---------------------- | ----- |
| # of Sessions | 1M |
| # of Episodes | 200K |
| # of Turns | 11.7M |
| Avg. Turns per session | 11.7 |
| Avg. Words per Turn | 18.03 |
### Dataset Splits
| Split | Number of Sessions | Number of Episodes |
| ------------- | ------------------ | ------------------ |
| Train | 800,000 | 160,000 |
| Validation | 100,000 | 20,000 |
| Test | 100,000 | 20,000 |
## Dataset Structure
| Fields | Type | Description |
| ------------------------- | --------------- | ---------------------------------------------------- |
| `dataID` | string | unique ID of an episode |
| `relationship` | string | relationships between the speakers in the episode |
| `time_interval` | sequence (list) | time intervals between sessions (total of 5) |
| `summary` | sequence (list) | chronological summaries of each session (total of 5) |
| `first_session_dialogue` | sequence (list) | utterance in the first session |
| `first_session_speakers` | sequence (list) | speaker matching for the first session utterance |
| `second_session_dialogue` | sequence (list) | utterance in the second session |
| `second_session_speakers` | sequence (list) | speaker matching for the second session utterance |
| `third_session_dialogue` | sequence (list) | utterance in the third session |
| `third_session_speakers` | sequence (list) | speaker matching for the third session utterance |
| `fourth_session_dialogue` | sequence (list) | utterance in the fourth session |
| `fourth_session_speakers` | sequence (list) | speaker matching for the fourth session utterance |
| `fifth_session_dialogue` | sequence (list) | utterance in the fifth session |
| `fifth_session_speakers` | sequence (list) | speaker matching for the fifth session utterance |
## Chronological Dynamics
our Conversation Chronicles implements chronological dynamics by integrating time interval and speaker relationship.
| Time Interval | Count |
| ------------------- | ------------------ |
| `A few hours` | 159,975 |
| `A few days` | 159,928 |
| `A few weeks` | 160,670 |
| `A few months` | 160,050 |
| `A couple of years` | 159,377 |
| Relationship | Count | Ratio |
| ------------------- | ------- | ----- |
| Classmates | 66,090 | 33.05% |
| Neighbors | 49,521 | 24.76% |
| Co-workers | 28,856 | 14.43% |
| Mentee and Mentor | 16,035 | 8.02% |
| Husband and Wife | 13,486 | 6.74% |
| Patient and Doctor | 6,980 | 3.49% |
| Parent and Child | 6,514 | 3.26% |
| Student and Teacher | 5,018 | 2.51% |
| Employee and Boss | 4,811 | 2.41% |
| Athlete and Coach | 2,689 | 1.34% |
| Total | 200,000 | |
## Citation Information
```
@inproceedings{jang2023conversation,
title={Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations},
author={Jihyoung Jang, MinSeong Boo, Hyounghun Kim},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://arxiv.org/abs/2310.13420}
}
```
|
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] |
jxm/dbpedia
|
jxm
| 2023-10-25T17:44:20Z | 55 | 0 |
[
"region:us"
] | null | 2023-10-25T17:44:13Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 14782633
num_examples: 49999
- name: test
num_bytes: 20641120
num_examples: 70000
- name: dev
num_bytes: 74007
num_examples: 256
download_size: 21721890
dataset_size: 35497760
---
# Dataset Card for "dbpedia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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flytech/python-codes-25k
|
flytech
| 2023-10-30T18:34:12Z | 55 | 1 |
[
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:token-classification",
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"code",
"python",
"flytech",
"cleaned",
"instructional",
"dataset 25k",
"text2code",
"code2text",
"behavioral",
"codegeneration",
"region:us"
] |
[
"text-classification",
"question-answering",
"token-classification",
"summarization",
"text2text-generation",
"text-generation"
] | 2023-10-30T17:03:27Z |
---
task_categories:
- text-classification
- question-answering
- token-classification
- summarization
- text2text-generation
- text-generation
tags:
- code
- python
- flytech
- cleaned
- instructional
- dataset 25k
- text2code
- code2text
- behavioral
- codegeneration
size_categories:
- 1M<n<10M
---
### <span style="color:#307090">License</span>
<span style="color:darkorange">MIT</span>
<hr style="height:1px;border:none;color:#333;background-color:#444;" />
## <span style="color:darkcyan">This is a Cleaned Python Dataset Covering 25,000 Instructional Tasks</span>
### <span style="color:#307090">Overview</span>
The dataset has 4 key features (fields): <b><span style="color:#205070">instruction</span></b>, <b><span style="color:#205070">input</span></b>, <b><span style="color:#205070">output</span></b>, and <b><span style="color:#205070">text</span></b>.
<span style="color:darkcyan">It's a rich source for Python codes, tasks, and extends into behavioral aspects.</span>
<hr style="height:1px;border:none;color:#333;background-color:#444;" />
### <span style="color:#307090">Dataset Statistics</span>
- **Total Entries**: <span style="color:darkmagenta">24,813</span>
- **Unique Instructions**: <span style="color:darkmagenta">24,580</span>
- **Unique Inputs**: <span style="color:darkmagenta">3,666</span>
- **Unique Outputs**: <span style="color:darkmagenta">24,581</span>
- **Unique Texts**: <span style="color:darkmagenta">24,813</span>
- **Average Tokens per example**: <span style="color:darkmagenta">508</span>
### <span style="color:#307090">Features</span>
- `instruction`: The instructional task to be performed / User input.
- `input`: Very short, introductive part of AI response or empty.
- `output`: Python code that accomplishes the task.
- `text`: All fields combined together.
<hr style="height:1px;border:none;color:#333;background-color:#444;" />
### <span style="color:#307090">Usage</span>
<span style="color:darkcyan">This dataset can be useful for:</span>
- <span style="color:#607090">Code generation tasks</span>
- <span style="color:#607090">Natural Language Understanding models specialized in coding languages</span>
- <span style="color:#607090">Behavioral analysis based on the given tasks and codes</span>
- <span style="color:#607090">Educational purposes to understand coding styles and task variations</span>
<span style="color:darkcyan">To load the dataset, one can use the following snippet:</span>
```python
from datasets import load_dataset
dataset = load_dataset('flytech/python-codes-25k', split='train')
# One can map the dataset in any way, for the sake of example:
dataset = dataset.map(lambda example: {'text': example['instruction'] + ' ' + example['input'] + ' ' + example['output']})['text']
```
### <span style="color:#307090">Access & Contributions</span>
<span style="color:cyan">Feel free to use this dataset as per the MIT license. Contributions to enhance or expand the dataset are welcome.</span>
|
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] |
npk7264/AutoBanner
|
npk7264
| 2023-11-13T08:02:50Z | 55 | 0 |
[
"region:us"
] | null | 2023-11-13T08:02:12Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 86538555.13
num_examples: 1362
download_size: 83996790
dataset_size: 86538555.13
---
# Dataset Card for "AutoBanner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
hodgesz/llama2-sql-create-context4
|
hodgesz
| 2023-11-18T01:13:21Z | 55 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-18T01:12:52Z |
---
license: apache-2.0
---
|
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] |
HgThinker/Music_Gen
|
HgThinker
| 2023-11-20T16:14:43Z | 55 | 0 |
[
"region:us"
] | null | 2023-11-20T13:23:54Z |
---
dataset_info:
features:
- name: ytid
dtype: string
- name: start_s
dtype: int64
- name: end_s
dtype: int64
- name: audioset_positive_labels
dtype: string
- name: aspect_list
dtype: string
- name: caption
dtype: string
- name: author_id
dtype: int64
- name: is_balanced_subset
dtype: bool
- name: is_audioset_eval
dtype: bool
- name: audio
struct:
- name: bytes
dtype: 'null'
- name: path
dtype: string
- name: download_status
dtype: bool
splits:
- name: train
num_bytes: 3161607
num_examples: 5520
download_size: 0
dataset_size: 3161607
---
# Dataset Card for "Music_Gen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
godivyam/business-companies-news-dataset
|
godivyam
| 2023-11-26T10:46:21Z | 55 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-26T10:45:33Z |
---
license: mit
---
|
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] |
nlewins/lsk_ceb_nonsynthetic
|
nlewins
| 2023-11-29T15:08:07Z | 55 | 0 |
[
"region:us"
] | null | 2023-11-29T15:07:24Z |
---
dataset_info:
features:
- name: ceb
dtype: string
- name: en
dtype: string
- name: en_audio
dtype: audio
- name: ceb_audio
dtype: audio
splits:
- name: train
num_bytes: 414635330.8170967
num_examples: 2134
- name: test
num_bytes: 56085596.75831154
num_examples: 267
- name: valid
num_bytes: 53523984.75831154
num_examples: 267
download_size: 435031554
dataset_size: 524244912.33371973
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
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] |
batterydata/pos_tagging
|
batterydata
| 2022-09-05T16:05:33Z | 54 | 0 |
[
"task_categories:token-classification",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"token-classification"
] | 2022-09-05T15:44:21Z |
---
language:
- en
license:
- apache-2.0
task_categories:
- token-classification
pretty_name: 'Part-of-speech(POS) Tagging Dataset for BatteryDataExtractor'
---
# POS Tagging Dataset
## Original Data Source
#### Conll2003
E. F. Tjong Kim Sang and F. De Meulder, Proceedings of the
Seventh Conference on Natural Language Learning at HLT-
NAACL 2003, 2003, pp. 142–147.
#### The Peen Treebank
M. P. Marcus, B. Santorini and M. A. Marcinkiewicz, Comput.
Linguist., 1993, 19, 313–330.
## Citation
BatteryDataExtractor: battery-aware text-mining software embedded with BERT models
|
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chenghao/cuad_qa
|
chenghao
| 2022-09-14T16:15:12Z | 54 | 0 |
[
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2103.06268",
"region:us"
] |
[
"question-answering"
] | 2022-09-14T00:01:15Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: cuad
pretty_name: CUAD
train-eval-index:
- config: default
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: test
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: cuad
name: CUAD
---
# Dataset Card for CUAD
This is a modified version of original [CUAD](https://huggingface.co/datasets/cuad/blob/main/README.md) which trims the question to its label form.
## 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:** [Contract Understanding Atticus Dataset](https://www.atticusprojectai.org/cuad)
- **Repository:** [Contract Understanding Atticus Dataset](https://github.com/TheAtticusProject/cuad/)
- **Paper:** [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://arxiv.org/abs/2103.06268)
- **Point of Contact:** [Atticus Project Team]([email protected])
### Dataset Summary
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
CUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at https://arxiv.org/abs/2103.06268. Code for replicating the results and the trained model can be found at https://github.com/TheAtticusProject/cuad.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains samples in English only.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [44],
"text": ['DISTRIBUTOR AGREEMENT']
},
"context": 'EXHIBIT 10.6\n\n DISTRIBUTOR AGREEMENT\n\n THIS DISTRIBUTOR AGREEMENT (the "Agreement") is made by and between Electric City Corp., a Delaware corporation ("Company") and Electric City of Illinois LLC ("Distributor") this 7th day of September, 1999...',
"id": "LIMEENERGYCO_09_09_1999-EX-10-DISTRIBUTOR AGREEMENT__Document Name_0",
"question": "Highlight the parts (if any) of this contract related to "Document Name" that should be reviewed by a lawyer. Details: The name of the contract",
"title": "LIMEENERGYCO_09_09_1999-EX-10-DISTRIBUTOR AGREEMENT"
}
```
### Data Fields
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
This dataset is split into train/test set. Number of samples in each set is given below:
| | Train | Test |
| ----- | ------ | ---- |
| CUAD | 22450 | 4182 |
## Dataset Creation
### Curation Rationale
A highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.
Contract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.
To reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.
### Source Data
#### Initial Data Collection and Normalization
The CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.
Type of Contracts: # of Docs
Affiliate Agreement: 10
Agency Agreement: 13
Collaboration/Cooperation Agreement: 26
Co-Branding Agreement: 22
Consulting Agreement: 11
Development Agreement: 29
Distributor Agreement: 32
Endorsement Agreement: 24
Franchise Agreement: 15
Hosting Agreement: 20
IP Agreement: 17
Joint Venture Agreemen: 23
License Agreement: 33
Maintenance Agreement: 34
Manufacturing Agreement: 17
Marketing Agreement: 17
Non-Compete/No-Solicit/Non-Disparagement Agreement: 3
Outsourcing Agreement: 18
Promotion Agreement: 12
Reseller Agreement: 12
Service Agreement: 28
Sponsorship Agreement: 31
Supply Agreement: 18
Strategic Alliance Agreement: 32
Transportation Agreement: 13
TOTAL: 510
#### Who are the source language producers?
The contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at https://www.sec.gov/edgar. Please read the Datasheet at https://www.atticusprojectai.org/ for information on the intended use and limitations of the CUAD.
### Annotations
#### Annotation process
The labeling process included multiple steps to ensure accuracy:
1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.
2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.
3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.
4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.
5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.
6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.
7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.
#### Who are the annotators?
Answered in above section.
### Personal and Sensitive Information
Some clauses in the files are redacted because the party submitting these contracts redacted them to protect confidentiality. Such redaction may show up as asterisks (\*\*\*) or underscores (\_\_\_) or blank spaces. The dataset and the answers reflect such redactions. For example, the answer for “January \_\_ 2020” would be “1/[]/2020”).
For any categories that require an answer of “Yes/No”, annotators include full sentences as text context in a contract. To maintain consistency and minimize inter-annotator disagreement, annotators select text for the full sentence, under the instruction of “from period to period”.
For the other categories, annotators selected segments of the text in the contract that are responsive to each such category. One category in a contract may include multiple labels. For example, “Parties” may include 4-10 separate text strings that are not continuous in a contract. The answer is presented in the unified format separated by semicolons of “Party A Inc. (“Party A”); Party B Corp. (“Party B”)”.
Some sentences in the files include confidential legends that are not part of the contracts. An example of such confidential legend is as follows:
THIS EXHIBIT HAS BEEN REDACTED AND IS THE SUBJECT OF A CONFIDENTIAL TREATMENT REQUEST. REDACTED MATERIAL IS MARKED WITH [* * *] AND HAS BEEN FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION.
Some sentences in the files contain irrelevant information such as footers or page numbers. Some sentences may not be relevant to the corresponding category. Some sentences may correspond to a different category. Because many legal clauses are very long and contain various sub-parts, sometimes only a sub-part of a sentence is responsive to a category.
To address the foregoing limitations, annotators manually deleted the portion that is not responsive, replacing it with the symbol "<omitted>" to indicate that the two text segments do not appear immediately next to each other in the contracts. For example, if a “Termination for Convenience” clause starts with “Each Party may terminate this Agreement if” followed by three subparts “(a), (b) and (c)”, but only subpart (c) is responsive to this category, the authors manually deleted subparts (a) and (b) and replaced them with the symbol "<omitted>”. Another example is for “Effective Date”, the contract includes a sentence “This Agreement is effective as of the date written above” that appears after the date “January 1, 2010”. The annotation is as follows: “January 1, 2010 <omitted> This Agreement is effective as of the date written above.”
Because the contracts were converted from PDF into TXT files, the converted TXT files may not stay true to the format of the original PDF files. For example, some contracts contain inconsistent spacing between words, sentences and paragraphs. Table format is not maintained in the TXT files.
## 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
Attorney Advisors
Wei Chen, John Brockland, Kevin Chen, Jacky Fink, Spencer P. Goodson, Justin Haan, Alex Haskell, Kari Krusmark, Jenny Lin, Jonas Marson, Benjamin Petersen, Alexander Kwonji Rosenberg, William R. Sawyers, Brittany Schmeltz, Max Scott, Zhu Zhu
Law Student Leaders
John Batoha, Daisy Beckner, Lovina Consunji, Gina Diaz, Chris Gronseth, Calvin Hannagan, Joseph Kroon, Sheetal Sharma Saran
Law Student Contributors
Scott Aronin, Bryan Burgoon, Jigar Desai, Imani Haynes, Jeongsoo Kim, Margaret Lynch, Allison Melville, Felix Mendez-Burgos, Nicole Mirkazemi, David Myers, Emily Rissberger, Behrang Seraj, Sarahginy Valcin
Technical Advisors & Contributors
Dan Hendrycks, Collin Burns, Spencer Ball, Anya Chen
### Licensing Information
CUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license and free to the public for commercial and non-commercial use.
The creators make no representations or warranties regarding the license status of the underlying contracts, which are publicly available and downloadable from EDGAR.
Privacy Policy & Disclaimers
The categories or the contracts included in the dataset are not comprehensive or representative. The authors encourage the public to help improve them by sending them your comments and suggestions to [email protected]. Comments and suggestions will be reviewed by The Atticus Project at its discretion and will be included in future versions of Atticus categories once approved.
The use of CUAD is subject to their privacy policy https://www.atticusprojectai.org/privacy-policy and disclaimer https://www.atticusprojectai.org/disclaimer.
### Citation Information
```
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding the original CUAD dataset.
|
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] |
vidhikatkoria/SGD_Flights
|
vidhikatkoria
| 2023-03-21T20:53:30Z | 54 | 0 |
[
"region:us"
] | null | 2022-11-11T21:17:00Z |
---
dataset_info:
features:
- name: domain
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: act
dtype: int64
- name: speaker
dtype: int64
splits:
- name: train
num_bytes: 6377556.63733501
num_examples: 20682
- name: test
num_bytes: 282
num_examples: 1
download_size: 2501341
dataset_size: 6377838.63733501
---
# Dataset Card for "SGD_Flights"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
bigbio/pico_extraction
|
bigbio
| 2022-12-22T15:46:16Z | 54 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:11:27Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: PICO Annotation
homepage: https://github.com/Markus-Zlabinger/pico-annotation
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for PICO Annotation
## Dataset Description
- **Homepage:** https://github.com/Markus-Zlabinger/pico-annotation
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
This dataset contains annotations for Participants, Interventions, and Outcomes (referred to as PICO task).
For 423 sentences, annotations collected by 3 medical experts are available.
To get the final annotations, we perform the majority voting.
## Citation Information
```
@inproceedings{zlabinger-etal-2020-effective,
title = "Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports",
author = {Zlabinger, Markus and
Sabou, Marta and
Hofst{"a}tter, Sebastian and
Hanbury, Allan},
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.274",
doi = "10.18653/v1/2020.findings-emnlp.274",
pages = "3064--3074",
}
```
|
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] |
bigbio/scicite
|
bigbio
| 2022-12-22T15:46:37Z | 54 | 0 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:12:03Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: SciCite
homepage: https://allenai.org/data/scicite
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for SciCite
## Dataset Description
- **Homepage:** https://allenai.org/data/scicite
- **Pubmed:** False
- **Public:** True
- **Tasks:** TXTCLASS
SciCite is a dataset of 11K manually annotated citation intents based on
citation context in the computer science and biomedical domains.
## Citation Information
```
@inproceedings{cohan:naacl19,
author = {Arman Cohan and Waleed Ammar and Madeleine van Zuylen and Field Cady},
title = {Structural Scaffolds for Citation Intent Classification in Scientific Publications},
booktitle = {Conference of the North American Chapter of the Association for Computational Linguistics},
year = {2019},
url = {https://aclanthology.org/N19-1361/},
doi = {10.18653/v1/N19-1361},
}
```
|
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irds/beir_fever
|
irds
| 2023-01-05T02:44:51Z | 54 | 0 |
[
"task_categories:text-retrieval",
"arxiv:2104.08663",
"region:us"
] |
[
"text-retrieval"
] | 2023-01-05T02:44:45Z |
---
pretty_name: '`beir/fever`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `beir/fever`
The `beir/fever` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/fever).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=5,416,568
- `queries` (i.e., topics); count=123,142
This dataset is used by: [`beir_fever_dev`](https://huggingface.co/datasets/irds/beir_fever_dev), [`beir_fever_test`](https://huggingface.co/datasets/irds/beir_fever_test), [`beir_fever_train`](https://huggingface.co/datasets/irds/beir_fever_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/beir_fever', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ..., 'title': ...}
queries = load_dataset('irds/beir_fever', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Thorne2018Fever,
title = "{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification",
author = "Thorne, James and
Vlachos, Andreas and
Christodoulopoulos, Christos and
Mittal, Arpit",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N18-1074",
doi = "10.18653/v1/N18-1074",
pages = "809--819"
}
@article{Thakur2021Beir,
title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna",
journal= "arXiv preprint arXiv:2104.08663",
month = "4",
year = "2021",
url = "https://arxiv.org/abs/2104.08663",
}
```
|
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] |
Ozziey/poems_dataset
|
Ozziey
| 2023-01-09T16:28:56Z | 54 | 3 |
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:afl-3.0",
"region:us"
] |
[
"tabular-classification"
] | 2023-01-09T16:25:22Z |
---
license: afl-3.0
task_categories:
- tabular-classification
language:
- en
pretty_name: Detected emotions and information for poetry dataset
size_categories:
- n<1K
---
|
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0.26214760541915894,
0.04487833380699158,
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] |
Multimodal-Fatima/FGVC_Aircraft_test
|
Multimodal-Fatima
| 2023-06-02T02:15:19Z | 54 | 0 |
[
"region:us"
] | null | 2023-01-28T02:49:32Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: family
dtype:
class_label:
names:
'0': A300
'1': A310
'2': A320
'3': A330
'4': A340
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'6': ATR-42
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- name: manufacturer
dtype:
class_label:
names:
'0': ATR
'1': Airbus
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'3': Beechcraft
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- name: label
dtype:
class_label:
names:
'0': 707-320
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- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: LLM_Description_opt175b_downstream_tasks_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: clip_tag_ViT_L_14_specific
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_full
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_fgvc
sequence: string
- name: clip_tags_ViT_L_14_with_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_wo_openai_classes
sequence: string
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dtype: string
- name: clip_tags_ViT_L_14_ensemble_specific
dtype: string
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dtype: string
- name: clip_tags_ViT_B_16_ensemble_specific
dtype: string
- name: clip_tags_ViT_B_32_simple_specific
dtype: string
- name: clip_tags_ViT_B_32_ensemble_specific
dtype: string
- name: Attributes_ViT_B_16_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_simple_specific
dtype: string
- name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific
dtype: string
splits:
- name: test
num_bytes: 929803718.0
num_examples: 3333
download_size: 923279914
dataset_size: 929803718.0
---
# Dataset Card for "FGVC_Aircraft_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jonathan-roberts1/RS_C11
|
jonathan-roberts1
| 2023-03-31T17:07:50Z | 54 | 0 |
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
[
"image-classification",
"zero-shot-image-classification"
] | 2023-02-14T18:12:02Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': dense forest
'1': grassland
'2': harbor
'3': high buildings
'4': low buildings
'5': overpass
'6': railway
'7': residential area
'8': roads
'9': sparse forest
'10': storage tanks
splits:
- name: train
num_bytes: 969136595.28
num_examples: 1232
download_size: 916398984
dataset_size: 969136595.28
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "RS_C11"
## Dataset Description
- **Paper** [Feature significance-based multibag-of-visual-words model for remote sensing image scene classification](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-3/035004/Feature-significance-based-multibag-of-visual-words-model-for-remote/10.1117/1.JRS.10.035004.pdf)
### Licensing Information
Free usage without license.
## Citation Information
[Feature significance-based multibag-of-visual-words model for remote sensing image scene classification](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-3/035004/Feature-significance-based-multibag-of-visual-words-model-for-remote/10.1117/1.JRS.10.035004.pdf)
```
@article{zhao2016feature,
title = {Feature significance-based multibag-of-visual-words model for remote sensing image scene classification},
author = {Zhao, Lijun and Tang, Ping and Huo, Lianzhi},
year = 2016,
journal = {Journal of Applied Remote Sensing},
publisher = {Society of Photo-Optical Instrumentation Engineers},
volume = 10,
number = 3,
pages = {035004--035004}
}
```
|
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ELiRF/dacsa
|
ELiRF
| 2023-03-25T09:58:52Z | 54 | 1 |
[
"task_categories:text2text-generation",
"task_ids:news-articles-summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:es",
"license:odbl",
"region:us"
] |
[
"text2text-generation"
] | 2023-03-03T10:16:33Z |
---
task_categories:
- text2text-generation
task_ids:
- news-articles-summarization
language:
- ca
- es
size_categories:
- 1M<n<10M
license:
- odbl
multilinguality:
- multilingual
source_datasets:
- original
paperswithcode_id: dacsa
annotations_creators:
- found
language_creators:
- found
pretty_name: DACSA
---
# Dataset Card for "DACSA"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles](https://aclanthology.org/2022.naacl-main.434/)
- **Point of Contact:** [Vicent Ahuir](mailto:[email protected])
### Dataset Summary
The Dataset for Automatic summarization of Catalan and Spanish newspaper
Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be
used to train summarization models for Catalan and Spanish. The data provides
pairs of news article and its summary from different newspapers for both, the
Catalan and the Spanish languages. Regarding the Catalan set, there are 725,184
sample pairs from 9 newspapers, regarding the Spanish set, the corpus provides
2,120,649 sample pairs from 21 newspapers.
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/xl-sum)
### Languages
- `catalan`
- `spanish`
## Dataset Structure
### Data Fields
- 'id': A string representing the article ID.
- 'summary': A string containing the article summary.
- 'article' : A string containing the article text.
### Data Splits
Four splits are provided for each language set
- **train**: samples for training models
- **validation**: samples for adjusting and validating models
- **test.i**: test samples from newspapers present in _train_ and _validation_ splits
- **test.ni**: test samples from newspapers not present in training and validation splits
The _validation_ and _test-i_ splits contain a uniform distribution of samples
from each newspaper source.
Languages | ISO 639-1 Code | Train | Val | Test.i | Test.ni | Total |
--------------|----------------|---------|-------|--------|---------|---------|
Catalan | ca | 636596 | 35376 | 35376 | 17836 | 725184 |
Spanish | es | 1802919 | 104052 | 104052 | 109626 | 2120649 |
## Dataset Creation
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/xl-sum)
### Source Data
Newspapers from Spain that publish news in Catalan or Spanish
#### Initial Data Collection and Normalization
[Detailed in the paper](https://aclanthology.org/2022.naacl-main.434/)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://aclanthology.org/2022.naacl-main.434/)
### Discussion of Biases
[More information needed](https://aclanthology.org/2022.naacl-main.434/)
### Other Known Limitations
[More information needed](https://aclanthology.org/2022.naacl-main.434/)
## Additional Information
### Dataset Curators
[More information needed](https://aclanthology.org/2022.naacl-main.434/)
### Licensing Information
These data are released under this licensing scheme.
We do not own any of the text from which these data has been extracted.
This DACSA dataset package is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
Should you consider that our data contains material that is owned by you
and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address,
telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and
information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources
from the next release of the corpus.
### Citation Information
If you use any of the datasets, models or code modules, please cite the following paper:
```
@inproceedings{segarra-soriano-etal-2022-dacsa,
title = "{DACSA}: A large-scale Dataset for Automatic summarization of {C}atalan and {S}panish newspaper Articles",
author = "Segarra Soriano, Encarnaci{\'o}n and
Ahuir, Vicent and
Hurtado, Llu{\'\i}s-F. and
Gonz{\'a}lez, Jos{\'e}",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.434",
doi = "10.18653/v1/2022.naacl-main.434",
pages = "5931--5943",
abstract = "The application of supervised methods to automatic summarization requires the availability of adequate corpora consisting of a set of document-summary pairs. As in most Natural Language Processing tasks, the great majority of available datasets for summarization are in English, making it difficult to develop automatic summarization models for other languages. Although Spanish is gradually forming part of some recent summarization corpora, it is not the same for minority languages such as Catalan.In this work, we describe the construction of a corpus of Catalan and Spanish newspapers, the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be used to train summarization models for Catalan and Spanish.We have carried out an analysis of the corpus, both in terms of the style of the summaries and the difficulty of the summarization task. In particular, we have used a set of well-known metrics in the summarization field in order to characterize the corpus. Additionally, for benchmarking purposes, we have evaluated the performances of some extractive and abstractive summarization systems on the DACSA corpus.",
}
```
|
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Dahoas/hh_human_eval
|
Dahoas
| 2023-03-06T00:10:19Z | 54 | 0 |
[
"region:us"
] | null | 2023-03-05T18:19:08Z |
---
dataset_info:
features:
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 50908
num_examples: 100
download_size: 30961
dataset_size: 50908
---
# Dataset Card for "hh_human_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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CasperLD/Pizza_Dataset_Extra_Detailed
|
CasperLD
| 2023-04-08T14:33:00Z | 54 | 1 |
[
"region:us"
] | null | 2023-04-08T14:32:32Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3791807.0
num_examples: 80
download_size: 3782195
dataset_size: 3791807.0
---
# Dataset Card for "Pizza_Dataset_Extra_Detailed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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rcds/swiss_rulings
|
rcds
| 2023-07-20T07:35:08Z | 54 | 1 |
[
"size_categories:100K<n<1M",
"language:it",
"language:de",
"language:fr",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
] | null | 2023-04-28T08:49:32Z |
---
license: cc-by-sa-4.0
language:
- it
- de
- fr
pretty_name: Swiss Rulings
size_categories:
- 100K<n<1M
---
# Dataset Card for Swiss Rulings
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
SwissRulings is a multilingual, diachronic dataset of 637K Swiss Federal Supreme Court (FSCS) cases. This dataset can be used to pretrain language models on Swiss legal data.
### Supported Tasks and Leaderboards
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
| Language | Subset | Number of Documents Full |
|------------|------------|--------------------------|
| German | **de** | 319K |
| French | **fr** | 246K |
| Italian | **it** | 71K |
## Dataset Structure
### Data Fields
```
decision_id (string)
facts (string)
considerations (string)
origin_facts (string)
origin_considerations (string)
law_area (string)
language (string)
year (int32)
court (string)
chamber (string)
canton (string)
region (string)
```
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
|
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] |
abokbot/wikipedia-first-paragraph
|
abokbot
| 2023-06-04T10:58:32Z | 54 | 0 |
[
"language:en",
"wikipedia",
"region:us"
] | null | 2023-06-04T10:06:17Z |
---
language:
- en
tags:
- wikipedia
---
# Dataset Description
This dataset contains the first paragraph of cleaned Wikipedia articles in English.
It was obtained by transorming the [Wikipedia](https://huggingface.co/datasets/wikipedia) "20220301.en" dataset as follows:
```python
from datasets import load_dataset
dataset = load_dataset("wikipedia", "20220301.en")["train"]
def get_first_paragraph(example):
example["text"] = example['text'].split('\n\n')[0]
return example
dataset = dataset.map(get_first_paragraph)
```
# Why use this dataset?
The size of the original English Wikipedia dataset is over 20GB. It takes 20min to load it on a Google Colab notebook and running computations on that dataset can be costly.
If you want to create a use case that mostly needs the information in the first paragraph of a Wikipedia article (which is the paragraph with the most important information), this 'wikipedia-first-paragraph' dataset is for you.
Its size is 1.39GB and it takes 5 min to load it on a Google colab notebook.
# How to load dataset
You can load it by runnning:
```python
from datasets import load_dataset
load_dataset("abokbot/wikipedia-first-paragraph")
```
# Dataset Structure
An example looks as follows:
```
{
'id': '12',
'url': 'https://en.wikipedia.org/wiki/Anarchism',
'title': 'Anarchism',
'text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects \
all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, \
which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, \
placed on the farthest left of the political spectrum, it is usually described alongside communalism \
and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement, and \
has a strong historical association with anti-capitalism and socialism.'
}
```
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] |
Patt/HellaSwag_TH_drop
|
Patt
| 2023-11-16T16:39:54Z | 54 | 0 |
[
"language:th",
"language:en",
"arxiv:1907.04307",
"region:us"
] | null | 2023-06-22T09:10:40Z |
---
language:
- th
- en
dataset_info:
features:
- name: ind
dtype: int64
- name: activity_label
dtype: string
- name: activity_label_th
dtype: string
- name: ctx_a
dtype: string
- name: ctx_a_th
dtype: string
- name: ctx_b
dtype: string
- name: ctx_b_th
dtype: string
- name: ctx
dtype: string
- name: ctx_th
dtype: string
- name: endings
sequence: string
- name: endings_th
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: int64
- name: score_ctx_a
dtype: float64
- name: score_ctx
dtype: float64
- name: score_endings
dtype: float64
splits:
- name: train
num_bytes: 66295463
num_examples: 20027
- name: validation
num_bytes: 17133944
num_examples: 5034
- name: test
num_bytes: 16871175
num_examples: 5093
download_size: 44164434
dataset_size: 100300582
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for HellaSwag_TH_drop
### Dataset Description
This dataset is Thai translated version of [hellaswag](https://huggingface.co/datasets/hellaswag) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
The score was penalized by the length of original text compare to translated text. The row that any score < 0.5 was dropped.
### Languages
- EN
- TH
|
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DynamicSuperb/SpeakerCounting_LibriTTS-TestClean
|
DynamicSuperb
| 2023-07-31T07:47:14Z | 54 | 0 |
[
"region:us"
] | null | 2023-07-13T18:22:13Z |
---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: instruction
dtype: string
- name: label
dtype: string
- name: utterance 1
dtype: string
- name: utterance 2
dtype: string
- name: utterance 3
dtype: string
- name: utterance 4
dtype: string
- name: utterance 5
dtype: string
splits:
- name: test
num_bytes: 391751299.0
num_examples: 2000
download_size: 444578671
dataset_size: 391751299.0
---
# Dataset Card for "SpeakerCounting_LibriTTSTestClean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
fujiki/llm-japanese-dataset_wikinews
|
fujiki
| 2023-07-24T08:13:28Z | 54 | 2 |
[
"license:cc-by-2.5",
"region:us"
] | null | 2023-07-24T07:42:30Z |
---
license: cc-by-2.5
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 6934579
num_examples: 4265
download_size: 3599861
dataset_size: 6934579
---
- This dataset is a subset of [izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) only including news-title generation tasks from `Wikinews`.
- Please also refer to the original dataset: [izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset)
|
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] |
jason-lee08/TinyStoriesWithExclamationsSmall
|
jason-lee08
| 2023-08-20T03:52:44Z | 54 | 0 |
[
"region:us"
] | null | 2023-08-03T01:20:07Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 23826331
num_examples: 21197
- name: validation
num_bytes: 236180
num_examples: 220
download_size: 8127925
dataset_size: 24062511
---
# Dataset Card for "TinyStoriesWithExclamationsSmall"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
owkin/medical_knowledge_from_extracts
|
owkin
| 2023-09-22T09:37:42Z | 54 | 4 |
[
"task_categories:summarization",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"summarization"
] | 2023-08-10T09:18:11Z |
---
license: apache-2.0
task_categories:
- summarization
language:
- en
---
This dataset is used to train LLMs for medical knowledge extraction tasks
|
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EgilKarlsen/PKDD_DistilRoBERTa_Baseline
|
EgilKarlsen
| 2023-08-17T23:39:58Z | 54 | 0 |
[
"region:us"
] | null | 2023-08-17T23:34:22Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115608907.5
num_examples: 37500
- name: test
num_bytes: 38536305.0
num_examples: 12500
download_size: 211880087
dataset_size: 154145212.5
---
# Dataset Card for "PKDD_DistilRoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
loremipsum3658/pet
|
loremipsum3658
| 2023-08-24T21:28:06Z | 54 | 0 |
[
"region:us"
] | null | 2023-08-24T21:27:59Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: fname
dtype: string
- name: raw_text
dtype: string
- name: aviso_previo
dtype: bool
- name: saldo_de_salario
dtype: bool
- name: ferias
dtype: bool
- name: decimo_terceiro
dtype: bool
- name: fgts
dtype: bool
- name: multa_do_477
dtype: bool
- name: multa_do_467
dtype: bool
- name: horas_extras
dtype: bool
- name: intervalo_intrajornada
dtype: bool
- name: intervalo_interjornada
dtype: bool
- name: adicional_noturno
dtype: bool
- name: adicional_de_insalubridade
dtype: bool
- name: adicional_de_periculosidade
dtype: bool
- name: diferencas_salariais_ou_equiparacao_salarial
dtype: bool
- name: dano_moral
dtype: bool
- name: contribuicao_assistencial
dtype: bool
- name: indenizacao_por_lucros_cessantes
dtype: bool
- name: indenizacao_por_dano_emergente
dtype: bool
- name: multa_normativa
dtype: bool
- name: honorarios_advocaticios
dtype: bool
- name: justica_gratuita
dtype: bool
- name: reconhecimento_de_vinculo
dtype: bool
- name: reflexos_das_parcelas_salariais
dtype: bool
- name: reflexos_de_salarios_oficiosos_e_informais
dtype: bool
- name: outros
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1654516
num_examples: 1705
- name: test
num_bytes: 351964
num_examples: 366
- name: validation
num_bytes: 332831
num_examples: 366
download_size: 1391885
dataset_size: 2339311
---
# Dataset Card for "pet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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axiong/pmc_llama_instructions
|
axiong
| 2023-11-23T08:47:30Z | 54 | 12 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:openrail",
"biology",
"med",
"region:us"
] |
[
"question-answering",
"text-generation"
] | 2023-09-01T00:56:32Z |
---
license: openrail
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- biology
- med
---
This repo provides part of the dataset used for PMC-LLaMA-13B's instruction tuning.
| Data | Size | Link |
| --- | --- | --- |
| ChatDoctor | 100K | https://www.yunxiangli.top/ChatDoctor/ |
| MedQA | 10.2K | https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options |
| MedMCQA | 183K | https://huggingface.co/datasets/medmcqa |
| PubmedQA | 211K | https://huggingface.co/datasets/pubmed_qa |
| LiveQA | 635 | https://huggingface.co/datasets/truehealth/liveqa |
| MedicationQA | 690 | https://huggingface.co/datasets/truehealth/medicationqa |
| UMLS | 99K | https://www.nlm.nih.gov/research/umls/index.html |
The whole instruction dataset is composed of 7 parts. We have covered them in this dataset repo except for *ChatDoctor*.
You should consider merge ChatDoctor's data for complete dataset.
|
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PAug/lisa-formal-v1
|
PAug
| 2023-12-01T17:15:17Z | 54 | 0 |
[
"region:us"
] | null | 2023-11-15T14:49:03Z |
---
dataset_info:
features:
- name: directory
dtype: string
- name: id
dtype: string
- name: file_path
dtype: string
- name: file_type
dtype: string
- name: raw_content
dtype: string
- name: parsed_content
list:
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dtype: string
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dtype:
class_label:
names:
'0': Axiom
'1': Lemma
'2': PredicateDefinition
'3': FunctionDefinition
'4': Theorem
- name: line
dtype: int64
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sequence: string
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list:
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class_label:
names:
'0': Axiom
'1': Lemma
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'3': FunctionDefinition
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sequence: string
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splits:
- name: train
num_bytes: 4662436
num_examples: 92
- name: val
num_bytes: 255599
num_examples: 4
download_size: 878593
dataset_size: 4918035
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
# Dataset Card for "lisa-formal-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
amlan107/para
|
amlan107
| 2023-11-16T11:50:35Z | 54 | 0 |
[
"region:us"
] | null | 2023-11-16T11:50:33Z |
---
dataset_info:
features:
- name: bn
dtype: string
- name: en
dtype: string
splits:
- name: parallel
num_bytes: 12827861
num_examples: 50000
download_size: 6965146
dataset_size: 12827861
configs:
- config_name: default
data_files:
- split: parallel
path: data/parallel-*
---
|
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jlbaker361/small_multiplication_whole
|
jlbaker361
| 2023-11-17T05:53:40Z | 54 | 0 |
[
"region:us"
] | null | 2023-11-17T04:47:33Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1343.111111111111
num_examples: 40
- name: test
num_bytes: 167.88888888888889
num_examples: 5
download_size: 4215
dataset_size: 1511.0
---
# Dataset Card for "small_multiplication_whole"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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thanaphatt1/samsum-th-ggtrans
|
thanaphatt1
| 2023-11-20T09:48:02Z | 54 | 0 |
[
"region:us"
] | null | 2023-11-20T09:48:00Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: dialogue
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 22585353
num_examples: 14731
- name: validation
num_bytes: 1231483
num_examples: 818
- name: test
num_bytes: 1271673
num_examples: 819
download_size: 9523753
dataset_size: 25088509
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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gilkeyio/inclusive_voice
|
gilkeyio
| 2023-11-23T03:34:27Z | 54 | 0 |
[
"task_categories:audio-classification",
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] |
[
"audio-classification",
"automatic-speech-recognition"
] | 2023-11-23T00:39:48Z |
---
dataset_info:
features:
- name: client_id
dtype: string
- name: audio
dtype: audio
- name: sentence
dtype: string
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
splits:
- name: train
num_bytes: 254499181.875
num_examples: 6075
- name: test
num_bytes: 76654946.864
num_examples: 1812
download_size: 327803775
dataset_size: 331154128.73899996
license:
- cc0-1.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- audio-classification
- automatic-speech-recognition
language:
- en
pretty_name: Inclusive Voice
size_categories:
- 1K<n<10K
---
# Dataset Card for Inclusive Voice
A dataset curated from Mozilla Common Voice containing equal representation from male, female, and other
## Dataset Details
### Dataset Description
- **Curated by:** Kim Gilkey
- **Language(s) (NLP):** English
- **License:** Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Dataset Sources
Mozilla Common Voice
- **Homepage:** https://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
## Uses
### Direct Use
This dataset is intended for use in training and evaluating voice recognition models, particularly for applications in gender classification.
### Out-of-Scope Use
The dataset is not suitable for applications requiring a wide range of dialects or non-English languages. It is also not designed for identifying fine-grained demographic characteristics beyond gender.
You agree not to attempt to identify the individuals in the dataset.
## Dataset Structure
### Data Fields
- **client_id:** A unique identifier for each participant.
- **audio:** Audio recording of a spoken sentence.
- **sentence:** The text content of the audio recording.
- **age:** Categorical age group of the participant.
- **gender:** Gender of the participant (balanced representation).
- **accent:** Accent information of the participant, if available.
### Splits
- **Train:** 6,075 examples (2,025 of each gender).
- **Test:** 1,812 examples (604 of each gender).
## Dataset Creation
### Curation Rationale
The dataset was specifically curated to address gender imbalance in voice recognition datasets, aiming to provide a balanced representation of male, female, and non-binary voices.
### Source Data
#### Data Collection and Processing
Derived from Mozilla's Common Voice project, the dataset underwent additional processing to balance gender representation and limit the dominance of any single voice.
## Bias, Risks, and Limitations
Despite efforts to balance gender representation, the dataset may still have biases, such as over-representation of certain accents or age groups. It's not comprehensive in representing all possible variations in English speech.
Since the original dataset contains relatively few female and even fewer non-binary voices, the dataset becomes quite small when filtered down.
## Citation
The original Common Voice citation:
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
## Dataset Card Authors
Kim Gilkey
## Dataset Card Contact
[email protected]
|
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adamjweintraut/eli5_lfqa_slice
|
adamjweintraut
| 2023-11-27T06:28:50Z | 54 | 0 |
[
"region:us"
] | null | 2023-11-27T06:28:35Z |
---
dataset_info:
features:
- name: index
dtype: int64
- name: q_id
dtype: string
- name: question
dtype: string
- name: best_answer
dtype: string
- name: all_answers
sequence: string
- name: num_answers
dtype: int64
- name: context
dtype: string
- name: orig
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 275385111.5562014
num_examples: 20000
- name: test
num_bytes: 34423138.944525175
num_examples: 2500
- name: validation
num_bytes: 34423138.944525175
num_examples: 2500
download_size: 207239662
dataset_size: 344231389.44525176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
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] |
DynamicSuperb/Text2Speech_LibriTTS-TestClean
|
DynamicSuperb
| 2023-11-30T04:00:14Z | 54 | 0 |
[
"region:us"
] | null | 2023-11-30T03:58:18Z |
---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: file
dtype: string
- name: text
dtype: string
- name: reference_speech_id
dtype: string
- name: reference_speech
dtype:
audio:
sampling_rate: 24000
- name: reference_speech_transcription
dtype: string
- name: label
dtype:
audio:
sampling_rate: 24000
- name: instruction
dtype: string
splits:
- name: test
num_bytes: 2639185705.032
num_examples: 4916
download_size: 2199786738
dataset_size: 2639185705.032
---
# Dataset Card for "Text2Speech_LibriTTS-TestClean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
SocialGrep/the-reddit-dataset-dataset
|
SocialGrep
| 2022-07-01T17:55:48Z | 53 | 1 |
[
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-04T20:47:35Z |
---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-reddit-dataset-dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/the-reddit-dataset-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=theredditdatasetdataset)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=theredditdatasetdataset)
### Dataset Summary
A meta dataset of Reddit's own /r/datasets community.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Additional Information
### Licensing Information
CC-BY v4.0
|
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Paul/hatecheck-german
|
Paul
| 2022-07-05T10:38:52Z | 53 | 1 |
[
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"arxiv:2206.09917",
"region:us"
] |
[
"text-classification"
] | 2022-07-05T10:36:48Z |
---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: German HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** [email protected]
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC.
|
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yhavinga/cnn_dailymail_dutch
|
yhavinga
| 2022-08-20T12:39:20Z | 53 | 1 |
[
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:nl",
"license:apache-2.0",
"region:us"
] |
[
"summarization"
] | 2022-08-16T18:25:06Z |
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- nl
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: cnn-daily-mail-1
pretty_name: CNN / Daily Mail
train-eval-index:
- config: 3.0.0
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
article: text
highlights: target
---
# Dataset Card for CNN Dailymail Dutch 🇳🇱🇧🇪 Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
Note: the data below is from the English version at [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail).
- **Homepage:**
- **Repository:** [CNN / DailyMail Dataset repository](https://github.com/abisee/cnn-dailymail)
- **Paper:** [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), [Get To The Point: Summarization with Pointer-Generator Networks](https://www.aclweb.org/anthology/K16-1028.pdf)
- **Leaderboard:** [Papers with Code leaderboard for CNN / Dailymail Dataset](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail)
- **Point of Contact:** [Abigail See](mailto:[email protected])
### Dataset Summary
The CNN / DailyMail Dutch 🇳🇱🇧🇪 Dataset is an English-language dataset translated to Dutch containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.
*This dataset currently (Aug '22) has a single config, which is
config `3.0.0` of [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail) translated to Dutch
with [yhavinga/t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi).*
### Supported Tasks and Leaderboards
- 'summarization': [Version 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) can be used to train a model for abstractive and extractive summarization ([Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given article is when compared to the highlight as written by the original article author. [Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models.
### Languages
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
## Dataset Structure
### Data Instances
For each instance, there is a string for the article, a string for the highlights, and a string for the id. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=cnn_dailymail&config=3.0.0) to explore more examples.
```
{'id': '0054d6d30dbcad772e20b22771153a2a9cbeaf62',
'article': '(CNN) -- An American woman died aboard a cruise ship that docked at Rio de Janeiro on Tuesday, the same ship on which 86 passengers previously fell ill, according to the state-run Brazilian news agency, Agencia Brasil. The American tourist died aboard the MS Veendam, owned by cruise operator Holland America. Federal Police told Agencia Brasil that forensic doctors were investigating her death. The ship's doctors told police that the woman was elderly and suffered from diabetes and hypertension, according the agency. The other passengers came down with diarrhea prior to her death during an earlier part of the trip, the ship's doctors said. The Veendam left New York 36 days ago for a South America tour.'
'highlights': 'The elderly woman suffered from diabetes and hypertension, ship's doctors say .\nPreviously, 86 passengers had fallen ill on the ship, Agencia Brasil says .'}
```
The average token count for the articles and the highlights are provided below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Article | 781 |
| Highlights | 56 |
### Data Fields
- `id`: a string containing the heximal formated SHA1 hash of the url where the story was retrieved from
- `article`: a string containing the body of the news article
- `highlights`: a string containing the highlight of the article as written by the article author
### Data Splits
The CNN/DailyMail dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 3.0.0 of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 287,113 |
| Validation | 13,368 |
| Test | 11,490 |
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015.
The code for the original data collection is available at <https://github.com/deepmind/rc-data>. The articles were downloaded using archives of <www.cnn.com> and <www.dailymail.co.uk> on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <https://cs.nyu.edu/~kcho/DMQA/>. An updated version of the code that does not anonymize the data is available at <https://github.com/abisee/cnn-dailymail>.
Hermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.
#### Who are the source language producers?
The text was written by journalists at CNN and the Daily Mail.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Version 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences.
This task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.
### Discussion of Biases
[Bordia and Bowman (2019)](https://www.aclweb.org/anthology/N19-3002.pdf) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.
Because the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published.
### Other Known Limitations
News articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article [(Kryściński et al, 2019)](https://www.aclweb.org/anthology/D19-1051.pdf). [Chen et al (2016)](https://www.aclweb.org/anthology/P16-1223.pdf) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors.
It should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles.
## Additional Information
### Dataset Curators
The data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format.
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.
The code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <https://github.com/abisee/cnn-dailymail>. The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.
### Licensing Information
The CNN / Daily Mail dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@inproceedings{see-etal-2017-get,
title = "Get To The Point: Summarization with Pointer-Generator Networks",
author = "See, Abigail and
Liu, Peter J. and
Manning, Christopher D.",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1099",
doi = "10.18653/v1/P17-1099",
pages = "1073--1083",
abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
}
```
```
@inproceedings{DBLP:conf/nips/HermannKGEKSB15,
author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
title={Teaching Machines to Read and Comprehend},
year={2015},
cdate={1420070400000},
pages={1693-1701},
url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},
booktitle={NIPS},
crossref={conf/nips/2015}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding the English version of this dataset.
The dataset was translated on Cloud TPU compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
|
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] |
leslyarun/c4_200m_gec_train100k_test25k
|
leslyarun
| 2022-10-26T07:59:31Z | 53 | 3 |
[
"task_categories:text-generation",
"source_datasets:allenai/c4",
"language:en",
"grammatical-error-correction",
"region:us"
] |
[
"text-generation"
] | 2022-10-26T07:21:21Z |
---
language:
- en
source_datasets:
- allenai/c4
task_categories:
- text-generation
pretty_name: C4 200M Grammatical Error Correction Dataset
tags:
- grammatical-error-correction
---
# C4 200M
# Dataset Summary
C4 200M Sample Dataset adopted from https://huggingface.co/datasets/liweili/c4_200m
C4_200m is a collection of 185 million sentence pairs generated from the cleaned English dataset from C4. This dataset can be used in grammatical error correction (GEC) tasks.
The corruption edits and scripts used to synthesize this dataset is referenced from: [C4_200M Synthetic Dataset](https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction)
# Description
As discussed before, this dataset contains 185 million sentence pairs. Each article has these two attributes: `input` and `output`. Here is a sample of dataset:
```
{
"input": "Bitcoin is for $7,094 this morning, which CoinDesk says."
"output": "Bitcoin goes for $7,094 this morning, according to CoinDesk."
}
```
|
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] |
bigbio/scai_disease
|
bigbio
| 2022-12-22T15:46:35Z | 53 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:12:00Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: SCAI Disease
homepage: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpus-for-disease-names-and-adverse-effects.html
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for SCAI Disease
## Dataset Description
- **Homepage:** https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpus-for-disease-names-and-adverse-effects.html
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
SCAI Disease is a dataset annotated in 2010 with mentions of diseases and
adverse effects. It is a corpus containing 400 randomly selected MEDLINE
abstracts generated using ‘Disease OR Adverse effect’ as a PubMed query. This
evaluation corpus was annotated by two individuals who hold a Master’s degree
in life sciences.
## Citation Information
```
@inproceedings{gurulingappa:lrec-ws10,
author = {Harsha Gurulingappa and Roman Klinger and Martin Hofmann-Apitius and Juliane Fluck},
title = {An Empirical Evaluation of Resources for the Identification of Diseases and Adverse Effects in Biomedical Literature},
booktitle = {LREC Workshop on Building and Evaluating Resources for Biomedical Text Mining},
year = {2010},
}
```
|
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irds/msmarco-document-v2
|
irds
| 2023-01-05T03:41:13Z | 53 | 0 |
[
"task_categories:text-retrieval",
"region:us"
] |
[
"text-retrieval"
] | 2023-01-05T03:41:07Z |
---
pretty_name: '`msmarco-document-v2`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `msmarco-document-v2`
The `msmarco-document-v2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-document-v2#msmarco-document-v2).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=11,959,635
This dataset is used by: [`msmarco-document-v2_trec-dl-2019`](https://huggingface.co/datasets/irds/msmarco-document-v2_trec-dl-2019), [`msmarco-document-v2_trec-dl-2019_judged`](https://huggingface.co/datasets/irds/msmarco-document-v2_trec-dl-2019_judged), [`msmarco-document-v2_trec-dl-2020`](https://huggingface.co/datasets/irds/msmarco-document-v2_trec-dl-2020), [`msmarco-document-v2_trec-dl-2020_judged`](https://huggingface.co/datasets/irds/msmarco-document-v2_trec-dl-2020_judged)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/msmarco-document-v2', 'docs')
for record in docs:
record # {'doc_id': ..., 'url': ..., 'title': ..., 'headings': ..., 'body': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
booktitle={InCoCo@NIPS},
year={2016}
}
```
|
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tobiolatunji/afrispeech-200
|
tobiolatunji
| 2023-11-20T09:20:34Z | 53 | 9 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2023-01-30T22:34:30Z |
---
pretty_name: AfriSpeech-200
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
dataset_info:
features:
- name: user_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 44100
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 1722002133
num_examples: 58000
- name: dev
num_bytes: 86120227
num_examples: 3231
download_size: 1475540500
dataset_size: 1808122360
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not attempt to determine the
identity of speakers in the Common Voice dataset.
---
# Dataset Card for AfriSpeech-200
## Table of Contents
- [Dataset Card for AfriSpeech-200](#dataset-card-for-afrispeech-200)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [How to use](#how-to-use)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/intron-innovation/AfriSpeech-Dataset-Paper
- **Repository:** https://github.com/intron-innovation/AfriSpeech-Dataset-Paper
- **Paper:** [AfriSpeech-200: Pan-African accented speech dataset for clinical and general domain ASR](https://github.com/intron-innovation/AfriSpeech-Dataset-Paper)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Intron Innovation](mailto:[email protected])
### Dataset Summary
AFRISPEECH-200 is a 200hr Pan-African speech corpus for clinical and general domain English accented ASR; a dataset with 120 African accents from 13 countries and 2,463 unique African speakers.
Our goal is to raise awareness for and advance Pan-African English ASR research, especially for the clinical domain.
## 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.
```python
from datasets import load_dataset
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "all")
```
The entire dataset is ~120GB and may take about 2hrs to download depending on internet speed/bandwidth. If you have disk space or bandwidth limitations, you can use `streaming` mode described below to work with smaller subsets of the data.
Alterntively you are able to pass a config to the `load_dataset` function and download only a subset of the data corresponding to a specific accent of interest. The example provided below is `isizulu`.
For example, to download the isizulu config, simply specify the corresponding accent config name. The list of supported accents is provided in the `accent list` section below:
```python
from datasets import load_dataset
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", 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
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train", streaming=True)
print(next(iter(afrispeech)))
print(list(afrispeech.take(5)))
```
### Local
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train")
batch_sampler = BatchSampler(RandomSampler(afrispeech), batch_size=32, drop_last=False)
dataloader = DataLoader(afrispeech, batch_sampler=batch_sampler)
```
### Streaming
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train", streaming=True)
dataloader = DataLoader(afrispeech, batch_size=32)
```
### Caveats
Note that till the end of the ongoing [AfriSpeech ASR Challenge event](https://zindi.africa/competitions/intron-afrispeech-200-automatic-speech-recognition-challenge) (Feb - May 2023), the transcripts in the validation set are hidden and the test set will be unreleased till May 19, 2023.
### Fine-tuning Colab tutorial
To walk through a complete colab tutorial that finetunes a wav2vec2 model on the afrispeech-200 dataset with `transformers`, take a look at this colab notebook [afrispeech/wav2vec2-colab-tutorial](https://colab.research.google.com/drive/1uZYew6pcgN6UE6sFDLohxD_HKivvDXzD?usp=sharing).
### Supported Tasks and Leaderboards
- Automatic Speech Recognition
- Speech Synthesis (Text-to-Speech)
### Languages
English (Accented)
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called `path` and its transcription, called `transcript`. Some additional information about the speaker is provided.
```
{
'speaker_id': 'b545a4ca235a7b72688a1c0b3eb6bde6',
'path': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397.wav',
'audio_id': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397',
'audio': {
'path': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397.wav',
'array': array([0.00018311, 0.00061035, 0.00012207, ..., 0.00192261, 0.00195312, 0.00216675]),
'sampling_rate': 44100},
'transcript': 'His mother is in her 50 s and has hypertension .',
'age_group': '26-40',
'gender': 'Male',
'accent': 'yoruba',
'domain': 'clinical',
'country': 'US',
'duration': 3.241995464852608
}
```
### Data Fields
- speaker_id: An id for which speaker (voice) made the recording
- path: The path to the audio file
- 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]`.
- transcript: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train, dev, and test.
Speech was recorded in a quiet environment with high quality microphone, speakers were asked to read one sentence at a time.
- Total Number of Unique Speakers: 2,463
- Female/Male/Other Ratio: 57.11/42.41/0.48
- Data was first split on speakers. Speakers in Train/Dev/Test do not cross partitions
| | Train | Dev | Test |
| ----------- | ----------- | ----------- | ----------- |
| # Speakers | 1466 | 247 | 750 |
| # Seconds | 624228.83 | 31447.09 | 67559.10 |
| # Hours | 173.4 | 8.74 | 18.77 |
| # Accents | 71 | 45 | 108 |
| Avg secs/speaker | 425.81 | 127.32 | 90.08 |
| Avg num clips/speaker | 39.56 | 13.08 | 8.46 |
| Avg num speakers/accent | 20.65 | 5.49 | 6.94 |
| Avg secs/accent | 8791.96 | 698.82 | 625.55 |
| # clips general domain | 21682 | 1407 | 2723 |
| # clips clinical domain | 36318 | 1824 | 3623 |
## Dataset Creation
### Curation Rationale
Africa has a very low doctor-to-patient ratio.
At very busy clinics, doctors could see 30+ patients per day-- a heavy patient burden compared with
developed countries-- but productivity tools such as clinical automatic speech recognition
(ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous,
in developed nations, and clinician-reported performance of commercial clinical ASR systems
is generally satisfactory. Furthermore, the recent performance of general domain ASR is
approaching human accuracy. However, several gaps exist. Several publications have
highlighted racial bias with speech-to-text algorithms and performance on minority
accents lags significantly. To our knowledge, there is no publicly available research or
benchmark on accented African clinical ASR, and speech data is non-existent for the
majority of African accents. We release AfriSpeech, 200hrs of Pan-African speech,
67,577 clips from 2,463 unique speakers, across 120 indigenous accents from 13 countries for
clinical and general domain ASR, a benchmark test set, with publicly available pre-trained
models with SOTA performance on the AfriSpeech benchmark.
### Source Data
#### Country Stats
| Country | Clips | Speakers | Duration (seconds) | Duration (hrs) |
| ----------- | ----------- | ----------- | ----------- | ----------- |
| NG | 45875 | 1979 | 512646.88 | 142.40 |
| KE | 8304 | 137 | 75195.43 | 20.89 |
| ZA | 7870 | 223 | 81688.11 | 22.69 |
| GH | 2018 | 37 | 18581.13 | 5.16 |
| BW | 1391 | 38 | 14249.01 | 3.96 |
| UG | 1092 | 26 | 10420.42 | 2.89 |
| RW | 469 | 9 | 5300.99 | 1.47 |
| US | 219 | 5 | 1900.98 | 0.53 |
| TR | 66 | 1 | 664.01 | 0.18 |
| ZW | 63 | 3 | 635.11 | 0.18 |
| MW | 60 | 1 | 554.61 | 0.15 |
| TZ | 51 | 2 | 645.51 | 0.18 |
| LS | 7 | 1 | 78.40 | 0.02 |
#### Accent Stats
| Accent | Clips | Speakers | Duration (s) | Country | Splits |
| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
| yoruba | 15407 | 683 | 161587.55 | US,NG | train,test,dev |
| igbo | 8677 | 374 | 93035.79 | US,NG,ZA | train,test,dev |
| swahili | 6320 | 119 | 55932.82 | KE,TZ,ZA,UG | train,test,dev |
| hausa | 5765 | 248 | 70878.67 | NG | train,test,dev |
| ijaw | 2499 | 105 | 33178.9 | NG | train,test,dev |
| afrikaans | 2048 | 33 | 20586.49 | ZA | train,test,dev |
| idoma | 1877 | 72 | 20463.6 | NG | train,test,dev |
| zulu | 1794 | 52 | 18216.97 | ZA,TR,LS | dev,train,test |
| setswana | 1588 | 39 | 16553.22 | BW,ZA | dev,test,train |
| twi | 1566 | 22 | 14340.12 | GH | test,train,dev |
| isizulu | 1048 | 48 | 10376.09 | ZA | test,train,dev |
| igala | 919 | 31 | 9854.72 | NG | train,test |
| izon | 838 | 47 | 9602.53 | NG | train,dev,test |
| kiswahili | 827 | 6 | 8988.26 | KE | train,test |
| ebira | 757 | 42 | 7752.94 | NG | train,test,dev |
| luganda | 722 | 22 | 6768.19 | UG,BW,KE | test,dev,train |
| urhobo | 646 | 32 | 6685.12 | NG | train,dev,test |
| nembe | 578 | 16 | 6644.72 | NG | train,test,dev |
| ibibio | 570 | 39 | 6489.29 | NG | train,test,dev |
| pidgin | 514 | 20 | 5871.57 | NG | test,train,dev |
| luhya | 508 | 4 | 4497.02 | KE | train,test |
| kinyarwanda | 469 | 9 | 5300.99 | RW | train,test,dev |
| xhosa | 392 | 12 | 4604.84 | ZA | train,dev,test |
| tswana | 387 | 18 | 4148.58 | ZA,BW | train,test,dev |
| esan | 380 | 13 | 4162.63 | NG | train,test,dev |
| alago | 363 | 8 | 3902.09 | NG | train,test |
| tshivenda | 353 | 5 | 3264.77 | ZA | test,train |
| fulani | 312 | 18 | 5084.32 | NG | test,train |
| isoko | 298 | 16 | 4236.88 | NG | train,test,dev |
| akan (fante) | 295 | 9 | 2848.54 | GH | train,dev,test |
| ikwere | 293 | 14 | 3480.43 | NG | test,train,dev |
| sepedi | 275 | 10 | 2751.68 | ZA | dev,test,train |
| efik | 269 | 11 | 2559.32 | NG | test,train,dev |
| edo | 237 | 12 | 1842.32 | NG | train,test,dev |
| luo | 234 | 4 | 2052.25 | UG,KE | test,train,dev |
| kikuyu | 229 | 4 | 1949.62 | KE | train,test,dev |
| bekwarra | 218 | 3 | 2000.46 | NG | train,test |
| isixhosa | 210 | 9 | 2100.28 | ZA | train,dev,test |
| hausa/fulani | 202 | 3 | 2213.53 | NG | test,train |
| epie | 202 | 6 | 2320.21 | NG | train,test |
| isindebele | 198 | 2 | 1759.49 | ZA | train,test |
| venda and xitsonga | 188 | 2 | 2603.75 | ZA | train,test |
| sotho | 182 | 4 | 2082.21 | ZA | dev,test,train |
| akan | 157 | 6 | 1392.47 | GH | test,train |
| nupe | 156 | 9 | 1608.24 | NG | dev,train,test |
| anaang | 153 | 8 | 1532.56 | NG | test,dev |
| english | 151 | 11 | 2445.98 | NG | dev,test |
| afemai | 142 | 2 | 1877.04 | NG | train,test |
| shona | 138 | 8 | 1419.98 | ZA,ZW | test,train,dev |
| eggon | 137 | 5 | 1833.77 | NG | test |
| luganda and kiswahili | 134 | 1 | 1356.93 | UG | train |
| ukwuani | 133 | 7 | 1269.02 | NG | test |
| sesotho | 132 | 10 | 1397.16 | ZA | train,dev,test |
| benin | 124 | 4 | 1457.48 | NG | train,test |
| kagoma | 123 | 1 | 1781.04 | NG | train |
| nasarawa eggon | 120 | 1 | 1039.99 | NG | train |
| tiv | 120 | 14 | 1084.52 | NG | train,test,dev |
| south african english | 119 | 2 | 1643.82 | ZA | train,test |
| borana | 112 | 1 | 1090.71 | KE | train |
| swahili ,luganda ,arabic | 109 | 1 | 929.46 | UG | train |
| ogoni | 109 | 4 | 1629.7 | NG | train,test |
| mada | 109 | 2 | 1786.26 | NG | test |
| bette | 106 | 4 | 930.16 | NG | train,test |
| berom | 105 | 4 | 1272.99 | NG | dev,test |
| bini | 104 | 4 | 1499.75 | NG | test |
| ngas | 102 | 3 | 1234.16 | NG | train,test |
| etsako | 101 | 4 | 1074.53 | NG | train,test |
| okrika | 100 | 3 | 1887.47 | NG | train,test |
| venda | 99 | 2 | 938.14 | ZA | train,test |
| siswati | 96 | 5 | 1367.45 | ZA | dev,train,test |
| damara | 92 | 1 | 674.43 | NG | train |
| yoruba, hausa | 89 | 5 | 928.98 | NG | test |
| southern sotho | 89 | 1 | 889.73 | ZA | train |
| kanuri | 86 | 7 | 1936.78 | NG | test,dev |
| itsekiri | 82 | 3 | 778.47 | NG | test,dev |
| ekpeye | 80 | 2 | 922.88 | NG | test |
| mwaghavul | 78 | 2 | 738.02 | NG | test |
| bajju | 72 | 2 | 758.16 | NG | test |
| luo, swahili | 71 | 1 | 616.57 | KE | train |
| dholuo | 70 | 1 | 669.07 | KE | train |
| ekene | 68 | 1 | 839.31 | NG | test |
| jaba | 65 | 2 | 540.66 | NG | test |
| ika | 65 | 4 | 576.56 | NG | test,dev |
| angas | 65 | 1 | 589.99 | NG | test |
| ateso | 63 | 1 | 624.28 | UG | train |
| brass | 62 | 2 | 900.04 | NG | test |
| ikulu | 61 | 1 | 313.2 | NG | test |
| eleme | 60 | 2 | 1207.92 | NG | test |
| chichewa | 60 | 1 | 554.61 | MW | train |
| oklo | 58 | 1 | 871.37 | NG | test |
| meru | 58 | 2 | 865.07 | KE | train,test |
| agatu | 55 | 1 | 369.11 | NG | test |
| okirika | 54 | 1 | 792.65 | NG | test |
| igarra | 54 | 1 | 562.12 | NG | test |
| ijaw(nembe) | 54 | 2 | 537.56 | NG | test |
| khana | 51 | 2 | 497.42 | NG | test |
| ogbia | 51 | 4 | 461.15 | NG | test,dev |
| gbagyi | 51 | 4 | 693.43 | NG | test |
| portuguese | 50 | 1 | 525.02 | ZA | train |
| delta | 49 | 2 | 425.76 | NG | test |
| bassa | 49 | 1 | 646.13 | NG | test |
| etche | 49 | 1 | 637.48 | NG | test |
| kubi | 46 | 1 | 495.21 | NG | test |
| jukun | 44 | 2 | 362.12 | NG | test |
| igbo and yoruba | 43 | 2 | 466.98 | NG | test |
| urobo | 43 | 3 | 573.14 | NG | test |
| kalabari | 42 | 5 | 305.49 | NG | test |
| ibani | 42 | 1 | 322.34 | NG | test |
| obolo | 37 | 1 | 204.79 | NG | test |
| idah | 34 | 1 | 533.5 | NG | test |
| bassa-nge/nupe | 31 | 3 | 267.42 | NG | test,dev |
| yala mbembe | 29 | 1 | 237.27 | NG | test |
| eket | 28 | 1 | 238.85 | NG | test |
| afo | 26 | 1 | 171.15 | NG | test |
| ebiobo | 25 | 1 | 226.27 | NG | test |
| nyandang | 25 | 1 | 230.41 | NG | test |
| ishan | 23 | 1 | 194.12 | NG | test |
| bagi | 20 | 1 | 284.54 | NG | test |
| estako | 20 | 1 | 480.78 | NG | test |
| gerawa | 13 | 1 | 342.15 | NG | test |
#### 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
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
The dataset was initially prepared by Intron and refined for public release by CLAIR Lab.
### Licensing Information
Public Domain, Creative Commons Attribution NonCommercial ShareAlike v4.0 ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode))
### Citation Information
@article{olatunji2023afrispeech,
title={AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR},
author={Olatunji, Tobi and Afonja, Tejumade and Yadavalli, Aditya and Emezue, Chris Chinenye and Singh, Sahib and Dossou, Bonaventure FP and Osuchukwu, Joanne and Osei, Salomey and Tonja, Atnafu Lambebo and Etori, Naome and others},
journal={arXiv preprint arXiv:2310.00274},
year={2023}
}
### Contributions
Thanks to [@tobiolatunji](https://github.com/tobiolatunji) for adding this dataset.
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] |
Multimodal-Fatima/OK-VQA_test
|
Multimodal-Fatima
| 2023-05-29T02:08:55Z | 53 | 0 |
[
"region:us"
] | null | 2023-03-07T14:45:38Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: question_type
dtype: string
- name: confidence
dtype: int32
- name: answers
sequence: string
- name: answers_original
list:
- name: answer
dtype: string
- name: raw_answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: id_image
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: id
dtype: int64
- name: clip_tags_LAION_ViT_H_14_2B
sequence: string
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption_beam_5
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float32
- name: size
dtype: string
- name: tag
dtype: string
- name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random
list:
- name: attribute
dtype: string
- name: box
sequence: float64
- name: captions_module
sequence: string
- name: captions_module_filter
sequence: string
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float64
- name: size
dtype: string
- name: tag
dtype: string
- name: clip_tags_ViT_B_16_with_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_with_openai
sequence: string
- name: clip_tags_ViT_L_14_with_openai
sequence: string
- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_ViT_B_16_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_
list:
- name: attribute
dtype: string
- name: box
sequence: float64
- name: captions_all_patches
sequence: string
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float64
- name: size
dtype: string
- name: tag
dtype: string
- name: blip_caption_topk_50_Salesforce_blip_image_captioning_large_multiple
sequence: string
splits:
- name: test
num_bytes: 1133674079.0
num_examples: 5046
download_size: 959321361
dataset_size: 1133674079.0
---
# Dataset Card for "OK-VQA_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
metaeval/boolq-natural-perturbations
|
metaeval
| 2023-04-09T14:14:18Z | 53 | 0 |
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
[
"text-classification"
] | 2023-04-07T09:05:20Z |
---
task_categories:
- text-classification
language:
- en
---
BoolQ questions with semantic alteration and human verifications
```bib
@article{khashabi2020naturalperturbations,
title={Natural Perturbation for Robust Question Answering},
author={D. Khashabi and T. Khot and A. Sabhwaral},
journal={arXiv preprint},
year={2020}
}
```
|
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gaussalgo/Canard_Wiki-augmented
|
gaussalgo
| 2023-04-12T13:35:37Z | 53 | 0 |
[
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"question-answering",
"conversational",
"text2text-generation"
] | 2023-04-11T12:49:22Z |
---
dataset_info:
features:
- name: History
sequence: string
- name: QuAC_dialog_id
dtype: string
- name: Question
dtype: string
- name: Question_no
dtype: int64
- name: Rewrite
dtype: string
- name: true_page_title
dtype: string
- name: true_contexts
dtype: string
- name: answer
dtype: string
- name: true_contexts_wiki
dtype: string
- name: extractive
dtype: bool
- name: retrieved_contexts
sequence: string
splits:
- name: train
num_bytes: 1353765609
num_examples: 31526
- name: test
num_bytes: 252071528
num_examples: 5571
download_size: 231554886
dataset_size: 1605837137
license: cc-by-sa-4.0
task_categories:
- question-answering
- conversational
- text2text-generation
language:
- en
pretty_name: Canard Wikipedia-augmented
size_categories:
- 10K<n<100K
---
# Dataset Card for Canard_Wiki-augmented
### Summary
This is a dataset of fact-retrieving conversations about Wikipedia articles, with all responses grounded in a specific segment of text in the referenced Wikipedia article.
It is an extended version of [Canard](https://sites.google.com/view/qanta/projects/canard)
and [QuAC](https://huggingface.co/datasets/quac) datasets,
augmented with the contexts of [English Wikipedia](https://huggingface.co/datasets/wikipedia).
### Supported Tasks
The dataset is intended to train a factually-consistent conversational model able to ground all its responses to the corresponding source(s).
However, the data can also be used to evaluate the information retrieval (IR) system for given queries, for contextual disambiguation of the queries from a conversation, etc.
## Dataset Structure
The dataset can be loaded by simply choosing a split (`train` or `test`) and calling:
```python
import datasets
canard_augm_test = datasets.load_dataset("gaussalgo/Canard_Wiki-augmented", split="test")
print(canard_augm_test[0]) # print the first sample
```
### Data Instances
The samples of Canard_Wiki-augmented have this format:
```python
{'History': ['Anna Politkovskaya', 'The murder remains unsolved, 2016'],
'QuAC_dialog_id': 'C_0aaa843df0bd467b96e5a496fc0b033d_1',
'Question': 'Did they have any clues?',
'Question_no': 1,
'answer': 'Her colleagues at Novaya gazeta protested that until the instigator or sponsor of the crime was identified, arrested and prosecuted the case was not closed.'
'Rewrite': 'Did investigators have any clues in the unresolved murder of Anna Politkovskaya?',
'true_page_title': 'Anna Politkovskaya',
'true_contexts': 'In September 2016 Vladimir Markin, official spokesman for (...)',
'true_contexts_wiki': 'Anna Stepanovna Politkovskaya was a US-born Russian journalist (...)',
'extractive': True
'retrieved_contexts': ['Clues was an indie rock band from Montreal, Canada formed by Alden Penner (...)',
'High Stakes is a British game show series hosted by Jeremy Kyle, in which (...)']
```
### Data Fields
* **History**: History of the conversation from Canard. The first two entries of the conversation are always synthetic.
* **QuAC_dialog_id**: Dialogue ID mapping the conversation to the original QuAC dataset (*dialogue_id* in QuAC).
* **Question**: Current question of the user from Canard.
* **Question_no**: Ordering of the user's question from the conversation, originally from Canard.
* **answer**: Correctly extracted answer to a given question from a relevant Wikipedia article (*true_contexts*). Note that some of the questions are open, thus the listed answer is not the only correct possibility.
* **Rewrite**: A rephrased version of *Question*, manually disambiguated from the context of *History* by the annotators of Canard.
* **true_page_title**: Title of the Wikipedia article containing *answer*. *wikipedia_page_title* from QuAC.
* **true_contexts**: An excerpt of the paragraph with an answer from the Wikipedia article titled *true_page_title*.
* **true_contexts_wiki**: A full contents of Wikipedia article (*text* from Wikipedia dataset), where *true_page_title* matches Wikipedia *title*. Note that the Wikipedia dataset was retrieved on 2nd of April, 2023.
* **extractive**: A flag whether the *answer* in this sample can be found as an exact-match in *true_contexts_wiki*.
* **retrieved_contexts**: "Distractor" contexts retrieved from the full Wikipedia dataset using the okapi-BM25 IR system on a **Rewrite** question.
### Data Splits
* **train** split is aligned with the training splits of Canard and QuAC.
* **test** split matches the validation split of QuAC and the test split of Canard (where the conversation ids match).
## Licensing
This dataset is composed of [QuAC](https://huggingface.co/datasets/quac) (MIT),
[Canard](https://sites.google.com/view/qanta/projects/canard) (CC BY-SA 4.0)
and [Wikipedia](https://huggingface.co/datasets/wikipedia) (CC BY SA 3.0).
Canard_Wiki-augmented is therefore licensed under CC BY-SA 4.0 as well, allowing it to be also commercially used.
## Cite
If you use this dataset in a research, do not forget to cite the authors of original datasets, that Canard_Wiki-augmented is derived from:
[QuAC](https://huggingface.co/datasets/quac), [Canard](https://sites.google.com/view/qanta/projects/canard).
|
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climatebert/tcfd_recommendations
|
climatebert
| 2023-04-19T07:08:06Z | 53 | 3 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-classification"
] | 2023-04-11T13:10:27Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: TCFDRecommendations
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': metrics
'2': strategy
'3': risk
'4': governance
splits:
- name: train
num_bytes: 638487
num_examples: 1300
- name: test
num_bytes: 222330
num_examples: 400
download_size: 492631
dataset_size: 860817
---
# Dataset Card for tcfd_recommendations
## Dataset Description
- **Homepage:** [climatebert.ai](https://climatebert.ai)
- **Repository:**
- **Paper:** [papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435)
- **Leaderboard:**
- **Point of Contact:** [Nicolas Webersinke](mailto:[email protected])
### Dataset Summary
We introduce an expert-annotated dataset for classifying the TCFD recommendation categories ([fsb-tcfd.org](https://www.fsb-tcfd.org)) of paragraphs in corporate disclosures.
### Supported Tasks and Leaderboards
The dataset supports a multiclass classification task of paragraphs into the four TCFD recommendation categories (governance, strategy, risk management, metrics and targets) and the non-climate-related class.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
```
{
'text': '− Scope 3: Optional scope that includes indirect emissions associated with the goods and services supply chain produced outside the organization. Included are emissions from the transport of products from our logistics centres to stores (downstream) performed by external logistics operators (air, land and sea transport) as well as the emissions associated with electricity consumption in franchise stores.',
'label': 1
}
```
### Data Fields
- text: a paragraph extracted from corporate annual reports and sustainability reports
- label: the label (0 -> none (i.e., not climate-related), 1 -> metrics, 2 -> strategy, 3 -> risk, 4 -> governance)
### Data Splits
The dataset is split into:
- train: 1,300
- test: 400
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Our dataset contains paragraphs extracted from financial disclosures by firms. We collect text from corporate annual reports and sustainability reports.
For more information regarding our sample selection, please refer to the Appendix of our paper (see [citation](#citation-information)).
#### Who are the source language producers?
Mainly large listed companies.
### Annotations
#### Annotation process
For more information on our annotation process and annotation guidelines, please refer to the Appendix of our paper (see [citation](#citation-information)).
#### Who are the annotators?
The authors and students at Universität Zürich and Friedrich-Alexander-Universität Erlangen-Nürnberg with majors in finance and sustainable finance.
### Personal and Sensitive Information
Since our text sources contain public information, no personal and sensitive information should be included.
## 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
- Julia Anna Bingler
- Mathias Kraus
- Markus Leippold
- Nicolas Webersinke
### Licensing Information
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
If you are interested in commercial use of the dataset, please contact [[email protected]](mailto:[email protected]).
### Citation Information
```bibtex
@techreport{bingler2023cheaptalk,
title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
type={Working paper},
institution={Available at SSRN 3998435},
year={2023}
}
```
### Contributions
Thanks to [@webersni](https://github.com/webersni) for adding this dataset.
|
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] |
c-s-ale/dolly-15k-instruction-alpaca-format
|
c-s-ale
| 2023-04-13T06:08:38Z | 53 | 20 |
[
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-3.0",
"instruction",
"region:us"
] | null | 2023-04-13T06:01:38Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: category
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 12271354
num_examples: 15015
download_size: 7801648
dataset_size: 12271354
license: cc-by-3.0
language:
- en
tags:
- instruction
pretty_name: Databricks Dolly 15k (Alpaca format, citations removed)
size_categories:
- 10K<n<100K
---
# Dataset Description
- **Blog:** https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm
- **Repo:** https://github.com/databrickslabs/dolly
# Databricks Dolly 15k Dataset with citations removed and in Alpaca Format
**NOTE**
This is a reupload of the Databricks dataset found [here](https://github.com/databrickslabs/dolly/tree/master/data), but modified to be in Alpaca format, and with the citation numbers removed.
This work is not my own, and all credit goes to Databricks.
# Dataset Overview
`databricks-dolly-15k` is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language
models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the
types of questions and instructions appropriate to each category.
Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly.
For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the `context` field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. `[42]`) which we recommend users remove for downstream applications.
# Intended Uses
While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories.
Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets.
# Dataset
## Purpose of Collection
As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications.
## Sources
- **Human-generated data**: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories.
- **Wikipedia**: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages.
## Annotator Guidelines
To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor.
The annotation guidelines for each of the categories are as follows:
- **Creative Writing**: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better.
- **Closed QA**: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form.
- **Open QA**: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation.
- **Summarization**: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form.
- **Information Extraction**: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form.
- **Classification**: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better.
- **Brainstorming**: Think up lots of examples in response to a question asking to brainstorm ideas.
## Personal or Sensitive Data
This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information.
## Language
American English
# Known Limitations
- Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia
- Some annotators may not be native English speakers
- Annotator demographics and subject matter may reflect the makeup of Databricks employees
# License/Attribution
**Copyright (2023) Databricks, Inc.**
This dataset was developed at Databricks (https://www.databricks.com) and its use is subject to the CC BY-SA 3.0 license.
Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license:
Wikipedia (various pages) - https://www.wikipedia.org/
Copyright © Wikipedia editors and contributors.
|
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slvnwhrl/tenkgnad-clustering-p2p
|
slvnwhrl
| 2023-04-24T12:06:18Z | 53 | 0 |
[
"size_categories:10K<n<100K",
"language:de",
"license:cc-by-nc-sa-4.0",
"embeddings",
"clustering",
"benchmark",
"arxiv:2210.07316",
"region:us"
] | null | 2023-04-21T14:07:23Z |
---
license: cc-by-nc-sa-4.0
language:
- de
tags:
- embeddings
- clustering
- benchmark
size_categories:
- 10K<n<100K
---
This dataset can be used as a benchmark for clustering word embeddings for <b>German</b>.
The datasets contains news article titles and is based on the dataset of the [One Million Posts Corpus](https://ofai.github.io/million-post-corpus/) and [10kGNAD](https://github.com/tblock/10kGNAD). It contains 10'275 unique samples, 10 splits with 1'436 to 9'962 samples and 9 unique classes. Splits are built similarly to MTEB's [TwentyNewsgroupsClustering](https://huggingface.co/datasets/mteb/twentynewsgroups-clustering) ([Paper](https://arxiv.org/abs/2210.07316)).
Have a look at [German Text Embedding Clustering Benchmark](https://github.com/ClimSocAna/tecb-de) for more infos, datasets and evaluation results.
|
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] |
yezhengli9/wmt20-en-zh
|
yezhengli9
| 2023-06-15T00:48:48Z | 53 | 0 |
[
"region:us"
] | null | 2023-06-15T00:44:45Z |
---
dataset_info:
features:
- name: id (string)
dtype: string
- name: translation (translation)
dtype: string
splits:
- name: train
num_bytes: 811187
num_examples: 1418
download_size: 444205
dataset_size: 811187
---
# Dataset Card for "wmt20-en-zh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ehartford/open-instruct-uncensored
|
ehartford
| 2023-06-22T18:41:10Z | 53 | 19 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-22T16:36:28Z |
---
license: apache-2.0
---
This is [Allen AI's open-instruct dataset.](https://github.com/allenai/open-instruct)
It is used to train the Tulu family of models.
- https://huggingface.co/allenai/tulu-7b
- https://huggingface.co/allenai/tulu-13b
- https://huggingface.co/allenai/tulu-30b
- https://huggingface.co/allenai/tulu-65b
I have done the following:
1) Download the open-instruct repo
2) Execute the scripts/prepare_train_data.sh modified to download the "unfiltered" version of sharegpt dataset
3) Merged data/processed/**/*.jsonl into a single "open-instruct.jsonl"
4) Executed my "remove_refusals.py" against that "open-instruct.jsonl" to produce a "open-instruct-uncensored.jsonl"
I am currently training this "open-instruct-uncensored.jsonl" to a new model series named ehartford/tulu-uncensored
More info to come.
|
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] |
Xenova/quickdraw
|
Xenova
| 2023-06-24T15:04:27Z | 53 | 2 |
[
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"image-classification"
] | 2023-06-23T21:18:53Z |
---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: quick-draw-dataset
pretty_name: Quick, Draw!
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': aircraft carrier
'1': airplane
'2': alarm clock
'3': ambulance
'4': angel
'5': animal migration
'6': ant
'7': anvil
'8': apple
'9': arm
'10': asparagus
'11': axe
'12': backpack
'13': banana
'14': bandage
'15': barn
'16': baseball bat
'17': baseball
'18': basket
'19': basketball
'20': bat
'21': bathtub
'22': beach
'23': bear
'24': beard
'25': bed
'26': bee
'27': belt
'28': bench
'29': bicycle
'30': binoculars
'31': bird
'32': birthday cake
'33': blackberry
'34': blueberry
'35': book
'36': boomerang
'37': bottlecap
'38': bowtie
'39': bracelet
'40': brain
'41': bread
'42': bridge
'43': broccoli
'44': broom
'45': bucket
'46': bulldozer
'47': bus
'48': bush
'49': butterfly
'50': cactus
'51': cake
'52': calculator
'53': calendar
'54': camel
'55': camera
'56': camouflage
'57': campfire
'58': candle
'59': cannon
'60': canoe
'61': car
'62': carrot
'63': castle
'64': cat
'65': ceiling fan
'66': cell phone
'67': cello
'68': chair
'69': chandelier
'70': church
'71': circle
'72': clarinet
'73': clock
'74': cloud
'75': coffee cup
'76': compass
'77': computer
'78': cookie
'79': cooler
'80': couch
'81': cow
'82': crab
'83': crayon
'84': crocodile
'85': crown
'86': cruise ship
'87': cup
'88': diamond
'89': dishwasher
'90': diving board
'91': dog
'92': dolphin
'93': donut
'94': door
'95': dragon
'96': dresser
'97': drill
'98': drums
'99': duck
'100': dumbbell
'101': ear
'102': elbow
'103': elephant
'104': envelope
'105': eraser
'106': eye
'107': eyeglasses
'108': face
'109': fan
'110': feather
'111': fence
'112': finger
'113': fire hydrant
'114': fireplace
'115': firetruck
'116': fish
'117': flamingo
'118': flashlight
'119': flip flops
'120': floor lamp
'121': flower
'122': flying saucer
'123': foot
'124': fork
'125': frog
'126': frying pan
'127': garden hose
'128': garden
'129': giraffe
'130': goatee
'131': golf club
'132': grapes
'133': grass
'134': guitar
'135': hamburger
'136': hammer
'137': hand
'138': harp
'139': hat
'140': headphones
'141': hedgehog
'142': helicopter
'143': helmet
'144': hexagon
'145': hockey puck
'146': hockey stick
'147': horse
'148': hospital
'149': hot air balloon
'150': hot dog
'151': hot tub
'152': hourglass
'153': house plant
'154': house
'155': hurricane
'156': ice cream
'157': jacket
'158': jail
'159': kangaroo
'160': key
'161': keyboard
'162': knee
'163': knife
'164': ladder
'165': lantern
'166': laptop
'167': leaf
'168': leg
'169': light bulb
'170': lighter
'171': lighthouse
'172': lightning
'173': line
'174': lion
'175': lipstick
'176': lobster
'177': lollipop
'178': mailbox
'179': map
'180': marker
'181': matches
'182': megaphone
'183': mermaid
'184': microphone
'185': microwave
'186': monkey
'187': moon
'188': mosquito
'189': motorbike
'190': mountain
'191': mouse
'192': moustache
'193': mouth
'194': mug
'195': mushroom
'196': nail
'197': necklace
'198': nose
'199': ocean
'200': octagon
'201': octopus
'202': onion
'203': oven
'204': owl
'205': paint can
'206': paintbrush
'207': palm tree
'208': panda
'209': pants
'210': paper clip
'211': parachute
'212': parrot
'213': passport
'214': peanut
'215': pear
'216': peas
'217': pencil
'218': penguin
'219': piano
'220': pickup truck
'221': picture frame
'222': pig
'223': pillow
'224': pineapple
'225': pizza
'226': pliers
'227': police car
'228': pond
'229': pool
'230': popsicle
'231': postcard
'232': potato
'233': power outlet
'234': purse
'235': rabbit
'236': raccoon
'237': radio
'238': rain
'239': rainbow
'240': rake
'241': remote control
'242': rhinoceros
'243': rifle
'244': river
'245': roller coaster
'246': rollerskates
'247': sailboat
'248': sandwich
'249': saw
'250': saxophone
'251': school bus
'252': scissors
'253': scorpion
'254': screwdriver
'255': sea turtle
'256': see saw
'257': shark
'258': sheep
'259': shoe
'260': shorts
'261': shovel
'262': sink
'263': skateboard
'264': skull
'265': skyscraper
'266': sleeping bag
'267': smiley face
'268': snail
'269': snake
'270': snorkel
'271': snowflake
'272': snowman
'273': soccer ball
'274': sock
'275': speedboat
'276': spider
'277': spoon
'278': spreadsheet
'279': square
'280': squiggle
'281': squirrel
'282': stairs
'283': star
'284': steak
'285': stereo
'286': stethoscope
'287': stitches
'288': stop sign
'289': stove
'290': strawberry
'291': streetlight
'292': string bean
'293': submarine
'294': suitcase
'295': sun
'296': swan
'297': sweater
'298': swing set
'299': sword
'300': syringe
'301': t-shirt
'302': table
'303': teapot
'304': teddy-bear
'305': telephone
'306': television
'307': tennis racquet
'308': tent
'309': The Eiffel Tower
'310': The Great Wall of China
'311': The Mona Lisa
'312': tiger
'313': toaster
'314': toe
'315': toilet
'316': tooth
'317': toothbrush
'318': toothpaste
'319': tornado
'320': tractor
'321': traffic light
'322': train
'323': tree
'324': triangle
'325': trombone
'326': truck
'327': trumpet
'328': umbrella
'329': underwear
'330': van
'331': vase
'332': violin
'333': washing machine
'334': watermelon
'335': waterslide
'336': whale
'337': wheel
'338': windmill
'339': wine bottle
'340': wine glass
'341': wristwatch
'342': yoga
'343': zebra
'344': zigzag
splits:
- name: train
num_bytes: 19761125464.75
num_examples: 50426266
download_size: 18927763475
dataset_size: 19761125464.75
---
# Dataset Card for Quick, Draw!
This is a processed version of Google's [Quick, Draw](https://huggingface.co/datasets/quickdraw/) dataset to be compatible with the latest versions of 🤗 Datasets that support .parquet files. NOTE: this dataset only contains the "preprocessed_bitmaps" subset of the original dataset.
|
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-0.027513805776834488,
-0.6470264196395874
] |
psymon/namuwiki_alpaca_dataset
|
psymon
| 2023-06-29T07:29:01Z | 53 | 9 |
[
"language:ko",
"license:cc-by-nc-sa-2.0",
"region:us"
] | null | 2023-06-29T07:18:44Z |
---
license: cc-by-nc-sa-2.0
language:
- ko
---
## namuwiki for Stanford Alpaca
나무위키 덤프 파일을 Stanford Alpaca 학습에 맞게 수정한 데이터셋입니다.
데이터 형식은 Stanford Alpaca와 동일합니다. instruction은 '나무위키 문서 제목' + '에 대해 설명해줘.' 형태이고,<br>
output은 문서 == 개요 == 에 해당하는 내용입니다. 개요가 없는 항목, 개요가 너무 짧은 항목은 제외하였습니다.
|
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KagglingFace/vit-cats-dogs
|
KagglingFace
| 2023-07-02T12:19:43Z | 53 | 1 |
[
"license:mit",
"region:us"
] | null | 2023-07-02T11:56:09Z |
---
license: mit
---
|
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od21wk/political_news_justifications
|
od21wk
| 2023-08-28T08:18:11Z | 53 | 5 |
[
"license:mit",
"region:us"
] | null | 2023-07-31T07:13:12Z |
---
license: mit
---
# Dataset Card: News Headlines and Justifications for Fake News Classification
## Overview
In an effort to combat fake news, researchers from the University of Leeds have curated and collected a dataset of fake news along with the justification why a certain news headline is fake or accurate. This dataset contains news headlines along with corresponding justifications to determine if the news is fake or not fake (factual). The dataset aims to balance political news records with 7971 instances and scientific misconceptions with 626 instances, providing a diverse set of information for training and evaluating models for fake news detection and fact-checking tasks.
## Dataset Description
- **Dataset Size**: 8597 records
- 7971 political news headlines with justifications (not fake and factual)
- 626 scientific misconception headlines with proofs (fake)
- **Data Format**:
The data is provided in JSONL format, and each record consists of two main components:
1. `"prompt"`: The news headline or scientific misconception statement that requires classification.
2. `"completion"`: The corresponding justification or proof that either validates the information as factual or identifies it as a fake/false claim.
## Example Records
### Political News Example:
```
{
"prompt": "Since Trump labeled ANTIFA a terrorist organization their rights were stripped by the Patriot Act which states if someone is labeled a terrorist they are not afforded due process. - a statement by Instagram posts.",
"completion": "The statement claims that since Trump labeled ANTIFA a terrorist organization, their rights were stripped by the Patriot Act. This is false. The Patriot Act does not give federal law enforcement officials the right to detain suspected terrorists who are U.S. citizens without probable cause. Also, Antifa is not a designated terrorist organization and is not listed on the State Department's list of foreign terrorist organizations. Even if it were, American activists would still have due process rights thanks to the Fifth and Fourteenth Amendments. Antifa is not a structured organization and has no leaders, it is organized into autonomous local groups. Therefore, the statement that rights were stripped by the Patriot Act is false."
}
```
### Scientific Misconception Example:
```
{
"prompt": "Economic recessions always lead to long-term declines in productivity.",
"completion": "The belief that economic recessions always lead to long-term declines in productivity is a misconception. While recessions can have negative short-term effects on productivity due to factors such as layoffs, reduced investments, and business closures, they can also lead to structural adjustments, technological advancements, and efficiency gains. Proper policy responses, including supportive measures for businesses and investments in human capital, can help mitigate the short-term productivity effects and pave the way for long-term productivity growth."
}
```
## Potential Use Cases
- Fake News Detection: This dataset can be used to train machine learning models for fake news classification, helping identify misleading or false information in political news headlines.
- Fact-Checking: The dataset can aid in building fact-checking systems that analyze news statements and verify their accuracy with accompanying justifications.
- NLP Research: Researchers can use this dataset to explore natural language processing techniques for misinformation detection and debunking scientific misconceptions.
## Data Collection Method
The data collection method for this dataset is not provided in this card. However, it's essential to ensure that the data collection process follows ethical guidelines, respects privacy, and avoids any biases.
## Limitations and Ethical Considerations
- Dataset Bias: Care should be taken to avoid any potential biases present in the dataset, whether in terms of the sources or the selection of news headlines.
- Fact-Checking Integrity: The dataset's accuracy relies on the trustworthiness of the sources used for fact-checking political news headlines.
- Privacy Concerns: Personal information, if present, should be anonymized to protect individuals' privacy and adhere to data protection regulations.
## Citation
```bibtex
@misc{PolNewsJustification,
title = {PolNewsJustification: An Open Dataset of news headline justification},
author = {Waleed Kareem},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
}
```
|
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] |
dim/oasst_ru
|
dim
| 2023-08-13T14:31:15Z | 53 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-08-13T14:25:50Z |
---
license: mit
dataset_info:
features:
- name: conversation_ids
sequence: string
- name: conversation_text
sequence: string
- name: status
dtype: string
splits:
- name: train
num_bytes: 7962688
num_examples: 3140
download_size: 2781053
dataset_size: 7962688
---
|
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] |
open-llm-leaderboard/details_uukuguy__speechless-codellama-platypus-13b
|
open-llm-leaderboard
| 2023-10-25T04:45:26Z | 53 | 0 |
[
"region:us"
] | null | 2023-08-31T15:51:42Z |
---
pretty_name: Evaluation run of uukuguy/speechless-codellama-platypus-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-codellama-platypus-13b](https://huggingface.co/uukuguy/speechless-codellama-platypus-13b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_uukuguy__speechless-codellama-platypus-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T04:45:04.706301](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-codellama-platypus-13b/blob/main/results_2023-10-25T04-45-04.706301.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.008494127516778523,\n\
\ \"em_stderr\": 0.0009398243325411525,\n \"f1\": 0.05910234899328872,\n\
\ \"f1_stderr\": 0.001500499797469734,\n \"acc\": 0.3734290226086707,\n\
\ \"acc_stderr\": 0.010636722374509789\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.008494127516778523,\n \"em_stderr\": 0.0009398243325411525,\n\
\ \"f1\": 0.05910234899328872,\n \"f1_stderr\": 0.001500499797469734\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09097801364670205,\n \
\ \"acc_stderr\": 0.007921322844013643\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6558800315706393,\n \"acc_stderr\": 0.013352121905005935\n\
\ }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-codellama-platypus-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|arc:challenge|25_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|arc:challenge|25_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T18_50_42.836793
path:
- '**/details_harness|drop|3_2023-10-18T18-50-42.836793.parquet'
- split: 2023_10_25T04_45_04.706301
path:
- '**/details_harness|drop|3_2023-10-25T04-45-04.706301.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T04-45-04.706301.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T18_50_42.836793
path:
- '**/details_harness|gsm8k|5_2023-10-18T18-50-42.836793.parquet'
- split: 2023_10_25T04_45_04.706301
path:
- '**/details_harness|gsm8k|5_2023-10-25T04-45-04.706301.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T04-45-04.706301.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hellaswag|10_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hellaswag|10_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T15:51:18.379129.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T15-51-14.957387.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T15-51-14.957387.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T15-51-14.957387.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T15:51:18.379129.parquet'
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path:
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path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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- split: latest
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- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
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path:
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T15-51-14.957387.parquet'
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data_files:
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- config_name: harness_hendrycksTest_philosophy_5
data_files:
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- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T15-51-14.957387.parquet'
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T15:51:18.379129.parquet'
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- split: latest
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T15:51:18.379129.parquet'
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- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T15:51:18.379129.parquet'
- split: 2023_09_12T15_51_14.957387
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_31T15_51_18.379129
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T15:51:18.379129.parquet'
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T15-51-14.957387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T15:51:18.379129.parquet'
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data_files:
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path:
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path:
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- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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- '**/details_harness|truthfulqa:mc|0_2023-09-12T15-51-14.957387.parquet'
- config_name: harness_winogrande_5
data_files:
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- '**/details_harness|winogrande|5_2023-10-18T18-50-42.836793.parquet'
- split: 2023_10_25T04_45_04.706301
path:
- '**/details_harness|winogrande|5_2023-10-25T04-45-04.706301.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T04-45-04.706301.parquet'
- config_name: results
data_files:
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path:
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path:
- results_2023-09-12T15-51-14.957387.parquet
- split: 2023_10_18T18_50_42.836793
path:
- results_2023-10-18T18-50-42.836793.parquet
- split: 2023_10_25T04_45_04.706301
path:
- results_2023-10-25T04-45-04.706301.parquet
- split: latest
path:
- results_2023-10-25T04-45-04.706301.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-codellama-platypus-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-codellama-platypus-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [uukuguy/speechless-codellama-platypus-13b](https://huggingface.co/uukuguy/speechless-codellama-platypus-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_uukuguy__speechless-codellama-platypus-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T04:45:04.706301](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-codellama-platypus-13b/blob/main/results_2023-10-25T04-45-04.706301.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.008494127516778523,
"em_stderr": 0.0009398243325411525,
"f1": 0.05910234899328872,
"f1_stderr": 0.001500499797469734,
"acc": 0.3734290226086707,
"acc_stderr": 0.010636722374509789
},
"harness|drop|3": {
"em": 0.008494127516778523,
"em_stderr": 0.0009398243325411525,
"f1": 0.05910234899328872,
"f1_stderr": 0.001500499797469734
},
"harness|gsm8k|5": {
"acc": 0.09097801364670205,
"acc_stderr": 0.007921322844013643
},
"harness|winogrande|5": {
"acc": 0.6558800315706393,
"acc_stderr": 0.013352121905005935
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
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dongyoung4091/hh-generated_flan_t5_large_flan_t5_zeroshot
|
dongyoung4091
| 2023-09-08T11:53:45Z | 53 | 0 |
[
"region:us"
] | null | 2023-09-08T11:53:41Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: zeroshot_helpfulness
dtype: float64
- name: zeroshot_specificity
dtype: float64
- name: zeroshot_intent
dtype: int64
- name: zeroshot_factuality
dtype: int64
- name: zeroshot_easy-to-understand
dtype: int64
- name: zeroshot_relevance
dtype: int64
- name: zeroshot_readability
dtype: float64
- name: zeroshot_enough-detail
dtype: float64
- name: 'zeroshot_biased:'
dtype: int64
- name: zeroshot_fail-to-consider-individual-preferences
dtype: int64
- name: zeroshot_repetetive
dtype: int64
- name: zeroshot_fail-to-consider-context
dtype: int64
- name: zeroshot_too-long
dtype: float64
splits:
- name: train
num_bytes: 6352357
num_examples: 25600
download_size: 798475
dataset_size: 6352357
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hh-generated_flan_t5_large_flan_t5_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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TokenBender/roleplay_alpaca
|
TokenBender
| 2023-09-24T19:32:28Z | 53 | 2 |
[
"license:artistic-2.0",
"region:us"
] | null | 2023-09-10T13:03:22Z |
---
license: artistic-2.0
---
|
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math-eval/TAL-SCQ5K
|
math-eval
| 2023-09-15T06:37:10Z | 53 | 21 |
[
"license:mit",
"region:us"
] | null | 2023-09-13T08:58:01Z |
---
license: mit
---
<h1 align="center">TAL-SCQ5K</h1>
## Dataset Description
### Dataset Summary
TAL-SCQ5K-EN/TAL-SCQ5K-CN are high quality mathematical competition datasets in English and Chinese language created by TAL Education Group, each consisting of 5K questions(3K training and 2K testing). The questions are in the form of multiple-choice and cover mathematical topics at the primary,junior high and high school levels. In addition, detailed solution steps are provided to facilitate CoT training and all the mathematical expressions in the questions have been presented as standard text-mode Latex.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in TAL-SCQ5K-EN is in English and TAL-SCQ5K-CN is in Chinese.
## Dataset Structure
### Data Instances
```
{
"dataset_name": "prime_math_competition_en_single_choice_8K_dev",
"dataset_version": "2023-07-07",
"qid": "244",
"queId": "8afc802a8c304199b1040f11ffa2e92a",
"competition_source_list": [],
"difficulty": "2",
"qtype": "single_choice",
"problem": "A $14$-digit. number $666666 XY 444444$ is a multiple of $26$. If $X$ and $Y$ are both positive, what is the smallest vaue of $X+ Y$? ",
"answer_option_list": [
[{
"aoVal": "A",
"content": "$$3$$ "
}],
[{
"aoVal": "B",
"content": "$$4$$ "
}],
[{
"aoVal": "C",
"content": "$$9$$ "
}],
[{
"aoVal": "D",
"content": "$$14$$ "
}],
[{
"aoVal": "E",
"content": "None of the above "
}]
],
"knowledge_point_routes": ["Overseas Competition->Knowledge Point->Number Theory Modules->Division without Remainders->Divisibility Rules"],
"answer_analysis": ["Since $1001$ is a multiple of $13$, $111111 = 111 \\times 1001$ is also a multiple of $13$. It follows that both $666666$ and $444444$ are both multiples of $26$. $666666XY 444444 = 66666600000000 + XY 000000 + 444444$ $\\Rightarrow XY$ must be divisible by $13$. Smallest $X+Y=1+3=4$. "],
"answer_value": "B"
}
```
### Data Fields
* "dataset_name": identification of the source dataset name from which TAL-SCQ5K-EN/TAL-SCQ5K-CN has been created, use only for inner of TAL education group, please ignore.
* "dataset_version": identification of the source dataset version from which TAL-SCQ5K-EN/TAL-SCQ5K-CN has been created, use only for inner of TAL education group, please ignore.
* "qid": identification of local id of the question in the source dataset from which TAL-SCQ5K-EN/TAL-SCQ5K-CN has been created, use only for inner of TAL education group, please ignore.
* "queId": identification of global id of the question, use only for inner of TAL education group, please ignore.
* "competition_source_list": identification of math competitions in which the questions appeared, if have been logged.
* "difficulty": difficulty level of the questions, value ranged from 0 to 4
* "qtype": question type, valued as "single_choice" for all the questions in this dataset indicates that all the questions are multiple-choice questions with unique ground-truth answer.
* "problem": the question string to a math competition question.
* "answer_option_list": answer choices to be selected
* "knowledge_point_routes": knowledge point route from coarse-grained to fine-grained.
* "answer_analysis": step-by-step answer analysis of the questions, which helps CoT training
* "answer_value": value of the ground-truth answer choice
### Data Splits
<style>
table th:first-of-type {
width: 40%;
}
table th:nth-of-type(2) {
width: 30%;
}
table th:nth-of-type(3) {
width: 30%;
}
</style>
| name|train|test |
|:---:|:----:|:----:|
|TAL-SCQ5K-EN|3K |2K |
|TAL-SCQ5K-CN|3K |2K |
## Usage
Each of the above datasets is located in a separate sub-directory. To load an individual subset, use the data_dir argument of the load_dataset() function as follows:
```python
from datasets import load_dataset
# Load all subsets (share the same schema)
dataset = load_dataset("math-eval/TAL-SCQ5K")
# Load TAL-SCQ5K-EN
dataset = load_dataset("math-eval/TAL-SCQ5K", data_dir="TAL-SCQ5K-EN")
# Load TAL-SCQ5K-CN
dataset = load_dataset("math-eval/TAL-SCQ5K", data_dir="TAL-SCQ5K-CN")
```
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The TAL-SCQ5K dataset is licensed under the [MIT License](https://opensource.org/license/mit/)
### Citation Information
[More Information Needed]
### Contact
The original authors host this dataset on GitHub here: https://github.com/math-eval/TAL-SCQ5K You can submit inquiries to: [email protected]
|
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skadewdl3/recipe-nlg-llama2
|
skadewdl3
| 2023-10-04T07:40:19Z | 53 | 0 |
[
"region:us"
] | null | 2023-09-20T07:17:54Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: title
dtype: string
- name: ingredients
dtype: string
- name: directions
dtype: string
- name: link
dtype: string
- name: source
dtype: string
- name: NER
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 3317395276.3463464
num_examples: 2008027
- name: test
num_bytes: 368600943.6536536
num_examples: 223115
download_size: 168971675
dataset_size: 3685996220.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "recipe-nlg-llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
renumics/emodb
|
renumics
| 2023-11-09T09:18:16Z | 53 | 0 |
[
"region:us"
] | null | 2023-10-04T04:49:02Z |
---
dataset_info:
features:
- name: age
dtype: float32
- name: gender
dtype:
class_label:
names:
'0': female
'1': male
- name: emotion
dtype:
class_label:
names:
'0': anger
'1': boredom
'2': disgust
'3': fear
'4': happiness
'5': neutral
'6': sadness
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 47623397.0
num_examples: 535
download_size: 46870260
dataset_size: 47623397.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emodb"
This is a mirror for the emodb dataset. You can find the original version here:
http://emodb.bilderbar.info/docu/
## Explore this dataset
You can interactively explore this dataset with Spotlight:
```python
import datasets
from renumics import spotlight
ds = datasets.load_dataset('renumics/emodb', split='all')
spotlight.show(ds)
```
|
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flytech/llama-python-codes-30k
|
flytech
| 2023-11-05T16:39:12Z | 53 | 9 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:llama2",
"code",
"python",
"instruct",
"llama",
"flytech",
"region:us"
] |
[
"question-answering",
"text-generation",
"text2text-generation"
] | 2023-10-08T16:10:50Z |
---
author: FlyTech
license: llama2
task_categories:
- question-answering
- text-generation
- text2text-generation
language:
- en
tags:
- code
- python
- instruct
- llama
- flytech
pretty_name: Llama1/2 Python Codes 30k Tokenized
size_categories:
- 10M<n<100M
---
### <span style="color:#3560B0; font-weight: bold;">Python Codes - 30k examples, Llama1&2 tokenized dataset</span>
### <span style="color:#3560B0; font-weight: bold;">Author</span>
**<span style="color:#266090;">FlyTech</span>**
<span style="color:#3560B0"></br>For general guide on how to create, quantize, merge or inference the model and more, visit:</span>
<a href="https://hackmd.io/@swearek/rJYVR_-7a" target="_blank">hackmd.io/my_first_ai</a>
### <span style="color:#3560B0; font-weight: bold;">Overview</span>
<span style="color:#266090">This dataset serves as a rich resource for various Natural Language Processing tasks such as:</span>
- <span style="color:#E91E63;">Question Answering</span>
- <span style="color:#8BC34A;">Text Generation</span>
- <span style="color:#FFC107;">Text-to-Text Generation</span>
<b><span style="color:#266090">It primarily focuses on instructional tasks in Python, tokenized specifically for the Llama architecture.
The dataset is a blend of GPT-4 generated content, custom codes, behavioral approaches and tasks extending beyond Python.</span></b>
<hr style="height:1px;border:none;color:#333;background-color:#136;" />
### <span style="color:#A45356; font-weight: bold;">IMPORTANT!</span>
<b><span style="color:#A8A8C9; background-color: #153055">
The llama-python-codes-30k dataset is not cleaned.
It has a very low number of unique input entries.</br>
For the fully cleaned version of the dataset, detokenized and with filtered-out input entries,
please refer to this link:
</span></b>
<a href="https://huggingface.co/datasets/flytech/python-codes-25k" style="color:#356090">flytech/python-codes-25k</a>
<hr style="height:1px;border:none;color:#333;background-color:#136;" />
### <span style="color:#3560B0; font-weight: bold;">Dataset Metrics</span>
**<span style="color:#3560B0;">Token Count (via LlamaTokenizer)</span>**
- **<span style="color:#4CAF50;">Maximum</span>: 508**
- **<span style="color:#2196F3;">Average</span>: 158.06**
- **<span style="color:#F44336;">Total</span>: 13,993,984**
**<span style="color:#006688;">Word Count</span>: 1,890,810**
**<span style="color:#006688;">Number of Examples</span>: 27,331**
### <b><span style="color:#3560B0; font-weight: bold;">Usage</span></b>
```python
from datasets import load_dataset
dataset = load_dataset('flytech/llama-python-codes-30k', split='train')
# One can map the dataset in any way, for the sake of example:
dataset = dataset.map(lambda example: {'text': example['instruction'] + ' ' + example['input'] + ' ' + example['output']})['text']
```
### <span style="color:#607D8B; font-weight: bold;">License</span>
This dataset is under the `llama2` license.
<hr style="height:1px;border:none;color:#333;background-color:#136;" />
### CONTRIBUTIONS
```python
# All contributions to the repository are welcome.
# Feel free to use the dataset for the Llama models,
# or visit:
```
<a href="https://huggingface.co/datasets/flytech/python-codes-25k" style="color:#356090">flytech/python-codes-25k</a>
```python
# To preprocess and tokenize the dataset as per your model requirements!
```
### <span style="color:#266090; font-weight: bold;">Tags</span>
- `code`
- `python`
- `instruct`
- `flytech`
|
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coastalcph/fair-rationales
|
coastalcph
| 2023-10-13T12:54:10Z | 53 | 3 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"source_datasets:extended",
"language:en",
"license:mit",
"bias",
"fairness",
"rationale",
"demographic",
"region:us"
] |
[
"text-classification"
] | 2023-10-12T11:57:58Z |
---
license: mit
language:
- en
annotations_creators:
- crowdsourced
source_datasets:
- extended
task_categories:
- text-classification
task_ids:
- sentiment-classification
- open-domain-qa
tags:
- bias
- fairness
- rationale
- demographic
pretty_name: FairRationales
---
# Dataset Card for "FairRationales"
## Dataset Summary
We present a new collection of annotations for a subset of CoS-E [[1]](#1), DynaSent [[2]](#2), and SST [[3]](#3)/Zuco [[4]](#4) with demographics-augmented annotations, balanced across age and ethnicity.
We asked participants to choose a label and then provide supporting evidence (rationales) based on the input sentence for their answer.
Existing rationale datasets are typically constructed by giving annotators 'gold standard' labels,
and having them provide rationales for these labels.
Instead, we let annotators provide rationales for labels they choose themselves. This lets them engage
in the decision process, but it also acknowledges
that annotators with different backgrounds may disagree on classification decisions. Explaining other
people’s choices is error-prone [[5]](#5), and we do not want to bias the rationale
annotations by providing labels that align better
with the intuitions of some demographics than with
those of others.
Our annotators are balanced across age and ethnicity for six demographic groups, defined by
ethnicity {Black/African American, White/Caucasian, Latino/Hispanic} and age {Old, Young}.
Therefore, we can refer to our groups as their cross-product: **{BO, BY, WO, WY, LO, LY}**.
## Dataset Details
### DynaSent
We re-annotate N=480 instances
six times (for six demographic groups), comprising
240 instances labeled as positive, and 240 instances
labeled as negative in the DynaSent Round 2 **test**
set (see [[2]](#2)). This amounts to 2,880
annotations, in total.
To annotate rationales, we formulate the task as
marking 'supporting evidence' for the label, following how the task is defined by [[6]](#6). Specifically, we ask annotators to mark
all the words, in the sentence, they think shows
evidence for their chosen label.
#### >Our annotations:
negative 1555 |
positive 1435 |
no sentiment 470
Total 3460
Note that all the data is uploaded under a single 'train' split (read [## Uses](uses) for further details).
### SST2
We re-annotate N=263 instances six
times (for six demographic groups), which are all
the positive and negative instances from the Zuco*
dataset of Hollenstein et al. (2018), comprising a
**mixture of train, validation and test** set instances
from SST-2, *which should be removed from the original SST
data before training any model*.
These 263 reannotated instances do not contain any instances originally marked as `neutral` (or not conveying sentiment) because rationale annotation for neutral instances is ill-defined. Yet,
we still allow annotators to evaluate a sentence as
neutral, since we do not want to force our annotators to provide rationales for positive and negative
sentiment that they do not see.
*The Zuco data contains eye-tracking data for 400 instances from SST. By annotating some of these with rationales,
we add an extra layer of information for future research.
#### >Our annotations:
positive 1027 |
negative 900 |
no sentiment 163
Total 2090
Note that all the data is uploaded under a single 'train' split (read [## Uses](uses) for further details).
### CoS-E
We use the simplified version of CoS-E released by [[6]](#6).
We re-annotate N=500 instances from
the CoS-E **test** set six times (for six demographic groups)
and ask annotators to firstly select the answer to
the question that they find most correct and sensible, and then mark words that justifies that answer.
Following [[7]](#7), we specify the
rationale task with a wording that should guide
annotators to make short, precise rationale annotations:
‘For each word in the question, if you
think that removing it will decrease your
confidence toward your chosen label,
please mark it.’
#### >Our annotations:
Total 3760
Note that all the data is uploaded under a single 'train' split (read [## Uses](uses) for further details).
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/terne/Being_Right_for_Whose_Right_Reasons
- **Paper:** [Being Right for Whose Right Reasons?](https://aclanthology.org/2023.acl-long.59/)
<a id="uses">## Uses</a>
<!-- Address questions around how the dataset is intended to be used. -->
In our paper, we present a collection of three
existing datasets (SST2, DynaSent and Cos-E) with demographics-augmented annotations to enable profiling of models, i.e., quantifying their alignment (or agreement) with rationales provided
by different socio-demographic groups. Such profiling enables us to ask whose right reasons models are being right for and fosters future research on performance equality/robustness.
For each dataset, we provide the data under a unique **'train'** split due to the current limitation of not being possible to upload a dataset with a single *'test'* split.
Note, however, that the original itended used of these collection of datasets was to **test** the quality & alignment of post-hoc explainability methods.
If you use it following different splits, please clarify it to ease reproducibility of your work.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
| Variable | Description |
| --- | --- |
| QID | The ID of the Question (i.e. the annotation element/sentence) in the Qualtrics survey. Every second question asked for the classification and every other asked for the rationale, of the classification, to be marked. These two questions and answers for the same sentence is merged to one row and therefore the QID looks as if every second is skipped. |
| text_id | A numerical ID given to each unique text/sentence for easy sorting before comparing annotations across groups. |
| sentence | The text/sentence that is annotated, in it's original formatting. |
| label | The (new) label given by the respective annotator/participant from Prolific. |
| label_index | The numerical format of the (new) label. |
| original_label | The label from the original dataset (Cose/Dynasent/SST). |
| rationale | The tokens marked as rationales by our annotators. |
| rationale_index | The indeces of the tokens marked as rationales. In the processed files the index start at 0. However in the unprocessed files ("_all.csv", "_before_exclussions.csv") the index starts at 1.|
| rationale_binary | A binary version of the rationales where a token marked as part of the rationale = 1 and tokens not marked = 0. |
| age | The reported age of the annotator/participant (i.e. their survey response). This may be different from the age-interval the participant was recruited by (see recruitment_age). |
| recruitment_age | The age interval specified for the Prolific job to recruit the participant by. A mismatch between this and the participant's reported age, when asked in our survey, may mean a number of things, such as: Prolific's information is wrong or outdated; the participant made a mistake when answering the question; the participant was inattentive. |
| ethnicity | The reported ethnicity of the annotator/participant. This may be different from the ethnicity the participant was recruited by (see recruitment_ethnicity). |
| recruitment_ethnicity | The ethnicity specified for the Prolific job to recruit the participant by. Sometimes there is a mismatch between the information Prolific has on participants (which we use for recruitment) and what the participants report when asked again in the survey/task. This seems especially prevalent with some ethnicities, likely because participants may in reality identify with more than one ethnic group. |
| gender | The reported gender of the annotator/participant. |
| english_proficiency | The reported English-speaking ability (proxy for English proficiency) of the annotator/participant. Options were "Not well", "Well" or "Very well". |
| attentioncheck | All participants were given a simple attention check question at the very end of the Qualtrics survey (i.e. after annotation) which was either PASSED or FAILED. Participants who failed the check were still paid for their work, but their response should be excluded from the analysis. |
| group_id | An id describing the socio-demographic subgroup a participant belongs to and was recruited by. |
| originaldata_id | The id given to the text/sentence in the original dataset. In the case of SST data, this refers to ids within the Zuco dataset – a subset of SST which was used in our study.|
| annotator_ID | Anonymised annotator ID to enable analysis such as annotators (dis)agreement |
| sst2_id | The processed SST annotations contain an extra column with the index of the text in the SST-2 dataset. -1 means that we were unable to match the text to an instance in SST-2 |
| sst2_split | The processed SST annotations contain an extra column refering to the set which the instance appears in within SST-2. Some instances a part of the train set and should therefore be removed before training a model on SST-2 and testing on our annotations. |
## Dataset Creation
### Curation Rationale
Terne Sasha Thorn Jakobsen, Laura Cabello, Anders Søgaard. Being Right for Whose Right Reasons?
In the Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
#### Annotation process
We refer to our [paper](https://aclanthology.org/2023.acl-long.59/) for further details on the data (Section 3), and specifically on the Annotation Process (Section 3.1) and Annotator Population (Section 3.2).
#### Who are the annotators?
Annotators were recruited via Prolific and consented to the use of their responses and demographic information for research purposes.
The annotation tasks were conducted through Qualtrics surveys. The exact surveys can be found [here](https://github.com/terne/Being_Right_for_Whose_Right_Reasons/tree/main/data/qualtrics_survey_exports).
## References
<a id="1">[1]</a>
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Explain Yourself! Leveraging Language Models for Commonsense Reasoning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4932–4942, Florence, Italy. Association for Computational Linguistics.
<a id="2">[2]</a>
Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela. 2021. DynaSent: A Dynamic Benchmark for Sentiment Analysis. In 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), pages 2388–2404, Online. Association for Computational Linguistics.
<a id="3">[3]</a>
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631–1642, Seattle, Washington, USA. Association for Computational Linguistics.
<a id="4">[4]</a>
Nora Hollenstein, Jonathan Rotsztejn, Marius Troendle, Andreas Pedroni, Ce Zhang, and Nicolas Langer. 2018. Zuco, a simultaneous eeg and eye-tracking resource for natural sentence reading. Scientific Data.
<a id="5">[5]</a>
Kate Barasz and Tami Kim. 2022. Choice perception: Making sense (and nonsense) of others’ decisions. Current opinion in psychology, 43:176–181.
<a id="6">[6]</a>
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2019. Eraser: A benchmark to evaluate rationalized nlp models.
<a id="7">[7]</a>
Cheng-Han Chiang and Hung-yi Lee. 2022. Reexamining human annotations for interpretable nlp.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```bibtex
@inproceedings{thorn-jakobsen-etal-2023-right,
title = "Being Right for Whose Right Reasons?",
author = "Thorn Jakobsen, Terne Sasha and
Cabello, Laura and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.59",
doi = "10.18653/v1/2023.acl-long.59",
pages = "1033--1054",
abstract = "Explainability methods are used to benchmark the extent to which model predictions align with human rationales i.e., are {`}right for the right reasons{'}. Previous work has failed to acknowledge, however, that what counts as a rationale is sometimes subjective. This paper presents what we think is a first of its kind, a collection of human rationale annotations augmented with the annotators demographic information. We cover three datasets spanning sentiment analysis and common-sense reasoning, and six demographic groups (balanced across age and ethnicity). Such data enables us to ask both what demographics our predictions align with and whose reasoning patterns our models{'} rationales align with. We find systematic inter-group annotator disagreement and show how 16 Transformer-based models align better with rationales provided by certain demographic groups: We find that models are biased towards aligning best with older and/or white annotators. We zoom in on the effects of model size and model distillation, finding {--}contrary to our expectations{--} negative correlations between model size and rationale agreement as well as no evidence that either model size or model distillation improves fairness.",
}
```
## Dataset Card Contact
Thanks to [@lautel](https://github.com/lautel) for adding this dataset.
|
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Sree1994/ddb_baseprompts
|
Sree1994
| 2023-10-26T22:52:37Z | 53 | 0 |
[
"region:us"
] | null | 2023-10-26T22:52:33Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: Base_prompt
dtype: string
- name: Prompt
dtype: string
splits:
- name: train
num_bytes: 14886028
num_examples: 51602
- name: test
num_bytes: 2096918
num_examples: 7299
- name: valid
num_bytes: 4301342
num_examples: 14817
download_size: 10829614
dataset_size: 21284288
---
# Dataset Card for "ddb_baseprompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
yuanbiao/imdb-card-pred
|
yuanbiao
| 2023-11-06T01:52:52Z | 53 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-05T08:47:03Z |
---
license: mit
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 23324623
num_examples: 100000
download_size: 4114305
dataset_size: 23324623
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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ibm/claim_stance
|
ibm
| 2023-11-15T10:01:56Z | 53 | 0 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-3.0",
"region:us"
] |
[
"text-classification"
] | 2023-11-06T10:29:47Z |
---
license: cc-by-3.0
task_categories:
- text-classification
language:
- en
pretty_name: Claim Stance
size_categories:
- 1K<n<10K
configs:
- config_name: claim_stance
data_files:
- split: train
path: "train.csv"
- split: test
path: "test.csv"
- config_name: claim_stance_topic
data_files:
- split: train
path: "train_topic.csv"
- split: validation
path: "dev_topic.csv"
- split: test
path: "test_topic.csv"
---
---
# Dataset Card for Claim Stance Dataset
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Notes](#notes)
## Dataset Summary
### Claim Stance
This dataset contains 2,394 labeled Wikipedia claims for 55 topics. The dataset includes the stance (Pro/Con) of each claim towards the topic,
as well as fine-grained annotations, based on the semantic model of [Stance Classification of Context-Dependent Claims](https://aclanthology.org/E17-1024/) (topic target,
topic sentiment towards its target, claim target, claim sentiment towards its target, and the relation between the targets).
The dataset is divided into a training set (25 topics, 1,039 claims) and a test set (30 topics, 1,355 claims).
The information in this card refers to this subset of the dataset unless stated otherwise.
### Claim Stance Topic
This subset contains the claims (column `text`) only associated with the topic (column `label`) in a different split to train-validation-test.
This subset can be utilized for topic classification tasks.
## Dataset Structure
* topicId - internal topic ID
* split - train or test
* topicText - the topic text
* topicTarget - sentiment target of topic
* topicSentiment - topic sentiment towards its target (1:positive/-1:negative)
* claims.claimId - claim internal ID
* claims.stance - PRO or CON
* claims.claimCorrectedText - the corrected version of the claim
* claims.claimOriginalText - the original version of the claim
* claims.Compatible - is the claim compatible with the semantic model of [Stance Classification of Context-Dependent Claims](https://aclanthology.org/E17-1024/)? (yes/no)
The following fine-grained annotations are specified only for "compatible" claims
* claims.claimTarget.text - claim sentiment target text (in the corrected version of the claim)
* claims.claimTarget.span.start - 0,
* claims.claimTarget.span.end - 31
* claims.claimSentiment - claim's sentiment towards its target (1:positive/-1:negative)
* claims.targetsRelation - relation between claim target and topic target ((1:consistent/-1:contrastive))
## Licensing Information
The datasets are released under the following licensing and copyright terms:
* (c) Copyright [Wikipedia](https://en.wikipedia.org/wiki/Wikipedia:Copyrights#Reusers.27_rights_and_obligations)
* (c) Copyright IBM 2014. Released under [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/)
## Citation Information
If you use this dataset, please cite the following paper:
```
@inproceedings{bar-haim-etal-2017-stance,
title = "Stance Classification of Context-Dependent Claims",
author = "Bar-Haim, Roy and
Bhattacharya, Indrajit and
Dinuzzo, Francesco and
Saha, Amrita and
Slonim, Noam",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1024",
pages = "251--261",
abstract = "Recent work has addressed the problem of detecting relevant claims for a given controversial topic. We introduce the complementary task of Claim Stance Classification, along with the first benchmark dataset for this task. We decompose this problem into: (a) open-domain target identification for topic and claim (b) sentiment classification for each target, and (c) open-domain contrast detection between the topic and the claim targets. Manual annotation of the dataset confirms the applicability and validity of our model. We describe an implementation of our model, focusing on a novel algorithm for contrast detection. Our approach achieves promising results, and is shown to outperform several baselines, which represent the common practice of applying a single, monolithic classifier for stance classification.",
}
```
Improved stance classification results on this dataset were published in:
```
@inproceedings{bar-haim-etal-2017-improving,
title = "Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization",
author = "Bar-Haim, Roy and
Edelstein, Lilach and
Jochim, Charles and
Slonim, Noam",
editor = "Habernal, Ivan and
Gurevych, Iryna and
Ashley, Kevin and
Cardie, Claire and
Green, Nancy and
Litman, Diane and
Petasis, Georgios and
Reed, Chris and
Slonim, Noam and
Walker, Vern",
booktitle = "Proceedings of the 4th Workshop on Argument Mining",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5104",
doi = "10.18653/v1/W17-5104",
pages = "32--38",
abstract = "Stance classification is a core component in on-demand argument construction pipelines. Previous work on claim stance classification relied on background knowledge such as manually-composed sentiment lexicons. We show that both accuracy and coverage can be significantly improved through automatic expansion of the initial lexicon. We also developed a set of contextual features that further improves the state-of-the-art for this task.",
}
```
## Notes
(1) Claim annotations and the experiments reported in [Stance Classification of Context-Dependent Claims](https://aclanthology.org/E17-1024/) and [Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization](https://aclanthology.org/W17-5104/)
are based on the corrected version of the claim. See [A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics](https://aclanthology.org/W14-2109/) for description of generating
corrected version for claims. The original version is the claim as it is found in the clean version of
the article, with no further editing.
(2) The topics and claims partially overlap with the CE-EMNLP-2015 dataset:
Common topics IDs: 1, 21, 61, 81, 101, 121, 181, 221, 323, 381, 441, 442, 443, 481, 482, 483, 601, 602,
621, 641, 642, 644, 645, 648, 662, 663, 665, 681, 683, 701, 721, 742, 743, 744, 761, 801, 803, 841, 861,
881, 923, 926, 941, 942, 944, 946
Only this dataset: 603, 661, 922, 985, 987, 990, 994, 1005, 1065
Only the CE-EMNLP-2015 dataset: 643, 646, 647, 664, 821, 902, 921, 925, 943, 945, 947, 961
|
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] |
ajibawa-2023/Python-Code-23k-ShareGPT
|
ajibawa-2023
| 2023-11-11T12:27:43Z | 53 | 12 |
[
"language:en",
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-11-10T12:11:19Z |
---
license: cc-by-nc-nd-4.0
language:
- en
---
This dataset is in Vicuna/ShareGPT format. There are 23000+ set of conversations. Each set having 2 conversations.
Along with the Python code detailed explanation is provided.
This dataset was generated using GPT-3.5, GPT-4 etc.
|
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] |
diffusers/pokemon-gpt4-captions
|
diffusers
| 2023-11-28T07:07:09Z | 53 | 9 |
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"region:us"
] |
[
"text-to-image"
] | 2023-11-28T06:54:16Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 56664550
num_examples: 833
download_size: 51051224
dataset_size: 56664550
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: other
task_categories:
- text-to-image
language:
- en
pretty_name: 'Pokemons with captions generated using GPT-4. '
size_categories:
- 1K<n<10K
---
# Dataset Card for "pokemon-gpt4-captions"
This dataset is just [lambdalabs/pokemon-blip-captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) but the captions come from GPT-4 (Turbo).
Code used to generate the captions:
```python
import base64
from io import BytesIO
import requests
from PIL import Image
def encode_image(image):
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
return img_str.decode("utf-8")
def create_payload(image_string):
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Provide caption for the image in one sentence. Be detailed but precise.",
},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_string}"},
},
],
}
],
"max_tokens": 100,
}
return payload
def get_response(image_string):
payload = create_payload(image_string)
response = requests.post(
"https://api.openai.com/v1/chat/completions", headers=headers, json=payload
)
return response.json()
image = Image.open("path_to_you_image").convert("RGB")
image_str = encode_image(image)
response = get_response(image_str)
```
Generating captions for 833 images from the [lambdalabs/pokemon-blip-captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) dataset costed about $5.
You can use this dataset for non-commercial applications.
|
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llangnickel/long-covid-classification-data
|
llangnickel
| 2022-11-24T10:29:58Z | 52 | 0 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"text-classification"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'Dataset containing abstracts from PubMed, either related to long COVID
or not. '
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
---
## Data Description
Long-COVID related articles have been manually collected by information specialists.
Please find further information [here](https://doi.org/10.1093/database/baac048).
## Size
||Training|Development|Test|Total|
|--|--|--|--|--|
Positive Examples|215|76|70|345|
Negative Examples|199|62|68|345|
Total|414|238|138|690|
## Citation
@article{10.1093/database/baac048,
author = {Langnickel, Lisa and Darms, Johannes and Heldt, Katharina and Ducks, Denise and Fluck, Juliane},
title = "{Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID}",
journal = {Database},
volume = {2022},
year = {2022},
month = {07},
issn = {1758-0463},
doi = {10.1093/database/baac048},
url = {https://doi.org/10.1093/database/baac048},
note = {baac048},
eprint = {https://academic.oup.com/database/article-pdf/doi/10.1093/database/baac048/44371817/baac048.pdf},
}
|
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] |
enimai/MuST-C-fr
|
enimai
| 2022-11-21T18:39:41Z | 52 | 0 |
[
"task_categories:translation",
"language:en",
"language:fr",
"license:apache-2.0",
"region:us"
] |
[
"translation"
] | 2022-03-20T14:27:39Z |
---
license: apache-2.0
language:
- en
- fr
task_categories:
- translation
---
|
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] |
arize-ai/beer_reviews_label_drift_neg
|
arize-ai
| 2022-10-19T13:20:26Z | 52 | 0 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2022-10-19T12:24:21Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: sentiment-classification-reviews-with-drift
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for `reviews_with_drift`
## 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)
- [language](#language)
- [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
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### language
Text is mainly written in english.
## 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 [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.
|
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] |
arize-ai/beer_reviews_label_drift_neutral
|
arize-ai
| 2022-10-19T13:19:17Z | 52 | 0 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2022-10-19T13:16:00Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: sentiment-classification-reviews-with-drift
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for `reviews_with_drift`
## 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)
- [language](#language)
- [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
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### language
Text is mainly written in english.
## 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 [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.
|
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Dizex/FoodBase
|
Dizex
| 2022-10-31T12:48:53Z | 52 | 1 |
[
"region:us"
] | null | 2022-10-31T12:42:55Z |
---
dataset_info:
features:
- name: nltk_tokens
sequence: string
- name: iob_tags
sequence: string
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 2040036
num_examples: 600
- name: val
num_bytes: 662190
num_examples: 200
download_size: 353747
dataset_size: 2702226
---
# Dataset Card for "FoodBase"
Dataset for FoodBase corpus introduced in [this paper](https://academic.oup.com/database/article/doi/10.1093/database/baz121/5611291).
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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lawcompany/KLAID
|
lawcompany
| 2022-11-17T07:09:10Z | 52 | 7 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:monolingual",
"language:ko",
"license:cc-by-nc-nd-4.0",
"region:us"
] |
[
"text-classification"
] | 2022-11-13T05:21:05Z |
---
pretty_name: KLAID
viewer: true
language: ko
multilinguality:
- monolingual
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for KLAID
## 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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Other Inquiries](#other_inquiries)
- [Licensing Information](#licensing-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://klaid.net](https://klaid.net)
- **Leaderboard:** [https://klaid.net](https://klaid.net)
- **Point of Contact:** [[email protected]]([email protected])
### Dataset Summary
Korean Legal Artificial Intelligence Datasets(KLAID) is a dataset for the development of Korean legal artificial intelligence technology. This time we offer 1 task, which is legal judgment prediction(LJP).
### Supported Tasks and Leaderboards
Legal Judgment Prediction(LJP)
### Languages
`korean`
### How to use
```python
from datasets import load_dataset
# legal judgment prediction
dataset = load_dataset("lawcompany/KLAID", 'ljp')
```
## Dataset Structure
### Data Instances
#### ljp
An example of 'train' looks as follows.
```
{
'fact': '피고인은 2022. 11. 14. 혈중알콜농도 0.123%의 술에 취한 상태로 승용차를 운전하였다.',
'laws_service': '도로교통법 제148조의2 제3항 제2호,도로교통법 제44조 제1항',
'laws_service_id': 7
}
```
Other References
You can refer to each label's 'laws service content' [here](https://storage.googleapis.com/klaid/ljp/dataset/ljp_laws_service_content.json).
'Laws service content' is the statute([source](https://www.law.go.kr/)) corresponding to each label.
### Data Fields
#### ljp
+ "fact": a `string` feature
+ "laws_service": a `string` feature
+ "laws_service_id": a classification label, with 177 legal judgment values
[More Information Needed](https://klaid.net/tasks-1)
### Data Splits
#### ljp
+ train: 161,192
## Dataset Creation
### Curation Rationale
The legal domain is arguably one of the most expertise fields that require expert knowledge to comprehend. Natural language processing requires many aspects, and we focus on the dataset requirements. As a gold standard is necessary for the testing and the training of a neural model, we hope that our dataset release will help the advances in natural language processing in the legal domain, especially for those for the Korean legal system.
### Source Data
These are datasets based on Korean legal case data.
### Personal and Sensitive Information
Due to the nature of legal case data, personal and sensitive information may be included. Therefore, in order to prevent problems that may occur with personal and sensitive information, we proceeded to de-realize the legal case.
## Considerations for Using the Data
### Other Known Limitations
We plan to upload more data and update them as some of the court records may be revised from now on, based on the ever-evolving legal system.
## Additional Information
### Other Inquiries
[[email protected]]([email protected])
### Licensing Information
Copyright 2022-present [Law&Company Co. Ltd.](https://career.lawcompany.co.kr/)
Licensed under the CC-BY-NC-ND-4.0
### Contributions
[More Information Needed]
|
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parambharat/tamil_asr_corpus
|
parambharat
| 2022-12-07T17:32:59Z | 52 | 1 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|common_voice",
"source_datasets:extended|openslr",
"language:ta",
"license:cc-by-4.0",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-12-07T16:36:05Z |
---
annotations_creators:
- found
language:
- ta
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Tamil ASR Corpus
size_categories:
- 100K<n<1M
source_datasets:
- extended|common_voice
- extended|openslr
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@parambharat](https://github.com/parambharat) for adding this dataset.
|
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jonathan-roberts1/CLRS
|
jonathan-roberts1
| 2023-03-31T15:35:22Z | 52 | 0 |
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
[
"image-classification",
"zero-shot-image-classification"
] | 2023-02-15T16:46:17Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airport
'1': bare land
'2': beach
'3': bridge
'4': commercial
'5': desert
'6': farmland
'7': forest
'8': golf course
'9': highway
'10': industrial
'11': meadow
'12': mountain
'13': overpass
'14': park
'15': parking
'16': playground
'17': port
'18': railway
'19': railway station
'20': residential
'21': river
'22': runway
'23': stadium
'24': storage tank
splits:
- name: train
num_bytes: 2969926932
num_examples: 15000
download_size: 2327956775
dataset_size: 2969926932
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "CLRS"
## Dataset Description
- **Paper** [CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification](https://www.mdpi.com/1424-8220/20/4/1226/pdf)
-
### Licensing Information
For academic purposes.
## Citation Information
[CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification](https://www.mdpi.com/1424-8220/20/4/1226/pdf)
```
@article{s20041226,
title = {CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification},
author = {Li, Haifeng and Jiang, Hao and Gu, Xin and Peng, Jian and Li, Wenbo and Hong, Liang and Tao, Chao},
year = 2020,
journal = {Sensors},
volume = 20,
number = 4,
doi = {10.3390/s20041226},
issn = {1424-8220},
url = {https://www.mdpi.com/1424-8220/20/4/1226},
article-number = 1226,
pubmedid = 32102294,
}
```
|
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wwydmanski/blog-feedback
|
wwydmanski
| 2023-02-25T16:03:19Z | 52 | 0 |
[
"task_categories:tabular-regression",
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"tabular",
"region:us"
] |
[
"tabular-regression",
"tabular-classification"
] | 2023-02-25T15:57:14Z |
---
task_categories:
- tabular-regression
- tabular-classification
tags:
- tabular
size_categories:
- 10K<n<100K
---
## Source
Source: [UCI](https://archive.ics.uci.edu/ml/datasets/BlogFeedback)
## Data Set Information:
This data originates from blog posts. The raw HTML-documents
of the blog posts were crawled and processed.
The prediction task associated with the data is the prediction
of the number of comments in the upcoming 24 hours. In order
to simulate this situation, we choose a basetime (in the past)
and select the blog posts that were published at most
72 hours before the selected base date/time. Then, we calculate
all the features of the selected blog posts from the information
that was available at the basetime, therefore each instance
corresponds to a blog post. The target is the number of
comments that the blog post received in the next 24 hours
relative to the basetime.
In the train data, the basetimes were in the years
2010 and 2011. In the test data the basetimes were
in February and March 2012. This simulates the real-world
situtation in which training data from the past is available
to predict events in the future.
The train data was generated from different basetimes that may
temporally overlap. Therefore, if you simply split the train
into disjoint partitions, the underlying time intervals may
overlap. Therefore, the you should use the provided, temporally
disjoint train and test splits in order to ensure that the
evaluation is fair.
## Attribute Information:
1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
For example, myblog.blog.org would be the source of the post myblog.blog.org/post_2010_09_10
51: Total number of comments before basetime
52: Number of comments in the last 24 hours before the
basetime
53: Let T1 denote the datetime 48 hours before basetime,
Let T2 denote the datetime 24 hours before basetime.
This attribute is the number of comments in the time period
between T1 and T2
54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime
55: The difference of Attribute 52 and Attribute 53
56...60:
The same features as the attributes 51...55, but
features 56...60 refer to the number of links (trackbacks),
while features 51...55 refer to the number of comments.
61: The length of time between the publication of the blog post
and basetime
62: The length of the blog post
63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post
263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime
270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post
277: Number of parent pages: we consider a blog post P as a
parent of blog post B, if B is a reply (trackback) to
blog post P.
278...280:
Minimum, maximum, average number of comments that the
parents received
281: The target: the number of comments in the next 24 hours
(relative to basetime)
|
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] |
jjmachan/NSFW-questions
|
jjmachan
| 2023-03-04T23:32:09Z | 52 | 6 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-03T07:25:45Z |
---
license: apache-2.0
dataset_info:
features:
- name: title
dtype: string
- name: subreddit
dtype: string
- name: post_id
dtype: string
- name: score
dtype: int64
- name: link_flair_text
dtype: string
- name: is_self
dtype: bool
- name: over_18
dtype: bool
- name: upvote_ratio
dtype: float64
- name: is_question
dtype: bool
- name: C1
dtype: string
- name: C2
dtype: string
- name: C3
dtype: string
- name: C4
dtype: string
- name: C5
dtype: string
splits:
- name: train
num_bytes: 1541472
num_examples: 1442
download_size: 904939
dataset_size: 1541472
---
|
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] |
biglam/european_art
|
biglam
| 2023-08-03T09:39:40Z | 52 | 5 |
[
"task_categories:object-detection",
"task_categories:image-classification",
"size_categories:10K<n<100K",
"license:cc-by-nc-2.0",
"lam",
"art",
"historical",
"arxiv:2211.01226",
"region:us"
] |
[
"object-detection",
"image-classification"
] | 2023-03-04T15:05:33Z |
---
dataset_info:
- config_name: raw
features:
- name: image
dtype: image
- name: source
dtype: string
- name: width
dtype: int16
- name: height
dtype: int16
- name: dept
dtype: int8
- name: segmented
dtype: int8
- name: objects
list:
- name: name
dtype:
class_label:
names:
'0': zebra
'1': tree
'2': nude
'3': crucifixion
'4': scroll
'5': head
'6': swan
'7': shield
'8': lily
'9': mouse
'10': knight
'11': dragon
'12': horn
'13': dog
'14': palm
'15': tiara
'16': helmet
'17': sheep
'18': deer
'19': person
'20': sword
'21': rooster
'22': bear
'23': halo
'24': lion
'25': monkey
'26': prayer
'27': crown of thorns
'28': elephant
'29': zucchetto
'30': unicorn
'31': holy shroud
'32': cat
'33': apple
'34': banana
'35': chalice
'36': bird
'37': eagle
'38': pegasus
'39': crown
'40': camauro
'41': saturno
'42': arrow
'43': dove
'44': centaur
'45': horse
'46': hands
'47': skull
'48': orange
'49': monk
'50': trumpet
'51': key of heaven
'52': fish
'53': cow
'54': angel
'55': devil
'56': book
'57': stole
'58': butterfly
'59': serpent
'60': judith
'61': mitre
'62': banner
'63': donkey
'64': shepherd
'65': boat
'66': god the father
'67': crozier
'68': jug
'69': lance
- name: pose
dtype:
class_label:
names:
'0': stand
'1': sit
'2': partial
'3': Unspecified
'4': squats
'5': lie
'6': bend
'7': fall
'8': walk
'9': push
'10': pray
'11': undefined
'12': kneel
'13': unrecognize
'14': unknown
'15': other
'16': ride
- name: diffult
dtype: int32
- name: xmin
dtype: float64
- name: ymin
dtype: float64
- name: xmax
dtype: float64
- name: ymax
dtype: float64
splits:
- name: train
num_bytes: 9046918
num_examples: 15156
download_size: 18160510195
dataset_size: 9046918
- config_name: coco
features:
- name: image
dtype: image
- name: source
dtype: string
- name: width
dtype: int16
- name: height
dtype: int16
- name: dept
dtype: int8
- name: segmented
dtype: int8
- name: objects
list:
- name: category_id
dtype:
class_label:
names:
'0': zebra
'1': tree
'2': nude
'3': crucifixion
'4': scroll
'5': head
'6': swan
'7': shield
'8': lily
'9': mouse
'10': knight
'11': dragon
'12': horn
'13': dog
'14': palm
'15': tiara
'16': helmet
'17': sheep
'18': deer
'19': person
'20': sword
'21': rooster
'22': bear
'23': halo
'24': lion
'25': monkey
'26': prayer
'27': crown of thorns
'28': elephant
'29': zucchetto
'30': unicorn
'31': holy shroud
'32': cat
'33': apple
'34': banana
'35': chalice
'36': bird
'37': eagle
'38': pegasus
'39': crown
'40': camauro
'41': saturno
'42': arrow
'43': dove
'44': centaur
'45': horse
'46': hands
'47': skull
'48': orange
'49': monk
'50': trumpet
'51': key of heaven
'52': fish
'53': cow
'54': angel
'55': devil
'56': book
'57': stole
'58': butterfly
'59': serpent
'60': judith
'61': mitre
'62': banner
'63': donkey
'64': shepherd
'65': boat
'66': god the father
'67': crozier
'68': jug
'69': lance
- name: image_id
dtype: string
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: segmentation
list:
list: float32
- name: iscrowd
dtype: bool
- name: image_id
dtype: string
splits:
- name: train
num_bytes: 8285204
num_examples: 15156
download_size: 18160510195
dataset_size: 8285204
license: cc-by-nc-2.0
task_categories:
- object-detection
- image-classification
tags:
- lam
- art
- historical
pretty_name: 'DEArt: Dataset of European Art'
size_categories:
- 10K<n<100K
---
# Dataset Card for DEArt: Dataset of European Art
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://doi.org/10.5281/zenodo.6984525
- **Paper:** https://arxiv.org/abs/2211.01226
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
> DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural heritage specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art.
### Supported Tasks and Leaderboards
- `object-detection`: This dataset can be used to train or evaluate models for object-detection on historical document images.
- `image-classification`: This dataset can be used for image classification tasks by using only the labels and not the bounding box information
## Dataset Structure
This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.
- The first configuration, `raw, uses the data's original format.
- The second configuration converts the annotations into a format that is closer to the `COCO` annotation format. This is done to make it easier to work with the [`image_processors`](https://huggingface.co/docs/transformers/main_classes/image_processor) (formerly known as`feature_extractor`s) from the `Transformers` models for object detection, which expects data to be in a COCO-style format.
### Data Instances
An instance from the `raw` config:
```python
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>,
'source': 'Europeana Collection',
'width': 1019,
'height': 1680,
'dept': 3,
'segmented': None,
'objects': [{'name': 40,
'pose': 3,
'diffult': 0,
'xmin': 259.0,
'ymin': 166.0,
'xmax': 679.0,
'ymax': 479.0},
{'name': 19,
'pose': 2,
'diffult': 0,
'xmin': 115.0,
'ymin': 354.0,
'xmax': 882.0,
'ymax': 1168.0},
{'name': 15,
'pose': 3,
'diffult': 0,
'xmin': 445.0,
'ymin': 1170.0,
'xmax': 579.0,
'ymax': 1302.0},
{'name': 51,
'pose': 3,
'diffult': 0,
'xmin': 354.0,
'ymin': 1196.0,
'xmax': 445.0,
'ymax': 1330.0},
{'name': 51,
'pose': 3,
'diffult': 0,
'xmin': 580.0,
'ymin': 1203.0,
'xmax': 701.0,
'ymax': 1326.0},
{'name': 57,
'pose': 3,
'diffult': 0,
'xmin': 203.0,
'ymin': 642.0,
'xmax': 882.0,
'ymax': 1172.0}]}
```
An instance from the `coco` config:
```python
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>,
'source': 'Europeana Collection',
'width': 1019,
'height': 1680,
'dept': 3,
'segmented': None,
'image_id': '0',
'annotations': [{'category_id': 40,
'image_id': '0',
'area': 131460,
'bbox': [259.0, 166.0, 420.0, 313.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 19,
'image_id': '0',
'area': 624338,
'bbox': [115.0, 354.0, 767.0, 814.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 15,
'image_id': '0',
'area': 17688,
'bbox': [445.0, 1170.0, 134.0, 132.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 51,
'image_id': '0',
'area': 12194,
'bbox': [354.0, 1196.0, 91.0, 134.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 51,
'image_id': '0',
'area': 14883,
'bbox': [580.0, 1203.0, 121.0, 123.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 57,
'image_id': '0',
'area': 359870,
'bbox': [203.0, 642.0, 679.0, 530.0],
'segmentation': [],
'iscrowd': False}]}
```
### Data Fields
The fields for the COCO config:
- `image`: The Image being annotated
- `source`: source of the image i.e.'Europeana Collection'
- `width`: width of the image
- `height`: height of the image
- `dept`: number of channels in the image
- `segmented`: Whether the image has been segmented
- `image_id`: ID for the image
- `annotations`: annotations in coco format, consisting of a list containing dictionaries with the following keys:
- `bbox`: bounding boxes for the images
- `category_id`: a label for the image
- `image_id`: id for the image
- `iscrowd`: COCO `iscrowd` flag
- `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
### Data Splits
The dataset doesn't define set splits, so only a train split is provided. The paper associated with the dataset does discuss a train and validation split, but it doesn't appear this split was shared with the dataset or associated paper.
## Dataset Creation
### Curation Rationale
The creators of the dataset authors outline some of their motivations for creating the dataset in the abstract for their paper:
> Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations.
### Source Data
The source data comes from several cultural heritage institutions that have shared openly licenced images. The dictionary below shows the institutions and the frequency with which they are the provider of images in the dataset.
```python
{'National Museum in Warsaw': 2030,
'Europeana Collection': 1991,
'The Art Institute of Chicago': 1237,
'The Metropolitan Museum of Art': 1218,
'Rijksmuseum': 1066,
'National Gallery of Art': 871,
'Philadelphia Museum of Art': 774,
'WikiArt': 687,
'National museum in Krakow': 661,
'National Gallery of Denmark': 660,
'British Museum': 618,
'Victoria and Albert Museum': 561,
'Paul Mellon Centre': 391,
'National Gallery of Scotland': 384,
'Yale University Art Gallery': 376,
'Museo Nacional Thyssen-Bornemisza': 332,
'Harvard Art Museum': 279,
'The National Museum of Norvay': 270,
'LACMA': 262,
'The Cleveland Museum of Art': 242,
'The Leiden Collection': 159,
'The Clark Museum': 77,
'Pharos': 6,
'Wikimedia Commons': 2,
'Wikipedia': 1,
'Unknown': 1}
```
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
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] |
webnlg/challenge-2023
|
webnlg
| 2023-03-10T11:22:40Z | 52 | 3 |
[
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-db_pedia",
"source_datasets:original",
"language:br",
"language:cy",
"language:ga",
"language:mt",
"language:ru",
"license:cc-by-sa-3.0",
"license:cc-by-nc-sa-4.0",
"license:gfdl",
"region:us"
] |
[
"tabular-to-text"
] | 2023-03-10T08:30:03Z |
---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
- br
- cy
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license:
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- gfdl
multilinguality:
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size_categories:
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source_datasets:
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- original
task_categories:
- tabular-to-text
task_ids:
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paperswithcode_id: null
pretty_name: WebNLG 2023 challenge
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---
# 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 2023 challenge](https://synalp.gitlabpages.inria.fr/webnlg-challenge/challenge_2023/)
- **Repository:** [GitHub repository](https://github.com/WebNLG/2023-Challenge)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [[email protected]](mailto:[email protected])
### Dataset Summary
The WebNLG 2023 challenge focuses on four under-resourced languages which are severely under-represented in research on
text generation, namely Maltese, Irish, Breton and Welsh. In addition, WebNLG 2023 once again includes Russian, which
was first featured in WebNLG 2020.
The challenge focuses on RDF-to-text generation, similarly to WebNLG 2017 but targeting Breton, Irish, Maltese, Welsh,
and Russian;
The 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 4 RDF triples:
```
<entry category="Company" eid="Id21" shape="(X (X) (X) (X) (X))" shape_type="sibling" size="4">
<modifiedtripleset>
<mtriple>Trane | foundingDate | 1913-01-01</mtriple>
<mtriple>Trane | location | Ireland</mtriple>
<mtriple>Trane | foundationPlace | La_Crosse,_Wisconsin</mtriple>
<mtriple>Trane | numberOfEmployees | 29000</mtriple>
</modifiedtripleset>
</entry>
```
the aim is to generate a text such as (English text):
```
Trane, which was founded on January 1st 1913 in La Crosse, Wisconsin, is based in Ireland. It has 29,000 employees.
```
or (Russian text):
```
Компания "Тране", основанная 1 января 1913 года в Ла-Кроссе в штате Висконсин, находится в Ирландии. В компании работают 29 тысяч человек.
```
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
### Supported Tasks and Leaderboards
The dataset supports a Structured 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.
The dataset is used in the [WebNLG 2023](https://synalp.gitlabpages.inria.fr/webnlg-challenge/challenge_2023/)
challenge.
Results are evaluated with automatic metrics: [BLEU](https://huggingface.co/metrics/bleu),
[METEOR](https://huggingface.co/metrics/meteor), [ChrF++](https://huggingface.co/metrics/chrf),
[TER](https://huggingface.co/metrics/ter) and [BERTscore](https://huggingface.co/metrics/bertscore).
Additionally, result are assessed according to criteria such as grammaticality/correctness, appropriateness/adequacy,
fluency/naturalness, etc., by native speakers.
### Languages
The dataset comprises Breton (`br`), Welsh (`cy`), Irish (`ga`), Maltese (`mt`) and Russian (`ru`) languages.
## 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': 'Airport',
'size': 1,
'eid': '1',
'original_triple_sets': {'otriple_set': [['Aarhus_Airport | cityServed | "Aarhus, Denmark"@en']]},
'modified_triple_sets': {'mtriple_set': [['Aarhus_Airport | cityServed | "Aarhus, Denmark"']]},
'shape': '(X (X))',
'shape_type': 'NA',
'lex': {'comment': ['good', 'good', '', ''],
'lid': ['Id1', 'Id2', 'Id3', 'Id3'],
'text': ['Aarhus a zo an aro-vezh Aarhus.',
"Aarhus a servijit ar c'hêr Aarhus.",
'The Aarhus is the airport of Aarhus, Denmark.',
'Aarhus Airport serves the city of Aarhus, Denmark.'],
'lang': ['br', 'br', 'en', 'en']}}
```
### Data Fields
The following fields can be found in the instances:
- `category`: the category of the DBpedia entities present in the RDF triples.
- `eid`: an example ID, only unique per split per category.
- `size`: number of RDF triples in the set.
- `shape`: (since v2) 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`: (since v2) 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).
- `test_category`: (for `webnlg_challenge_2017` and `v3`) tells whether the set of RDF triples was present in the training set or not. Several splits of the test set are available: with and without references, and for RDF-to-text generation / for semantic parsing.
- `lex`: the lexicalizations, with:
- `text`: the text to be predicted.
- `lid`: a lexicalization ID, unique per example.
- `comment`: the lexicalizations were rated by crowd workers are either `good` or `bad`
- `lang`: (for `release_v3.0_ru`) the language used because original English texts were kept in the Russian version.
### Data Splits
The dataset is split into train and validation:
| language | train | validation |
|----------|------:|-----------:|
| br | 13211 | 1399 |
| cy | 13211 | 1665 |
| ga | 13211 | 1665 |
| mt | 13211 | 1665 |
| ru | 5573 | 790 |
## Dataset Creation
### Curation Rationale
The WebNLG dataset was created to promote the development _(i)_ of RDF verbalisers and _(ii)_ of microplanners able to handle a wide range of linguistic constructions. The dataset aims at covering knowledge in different domains ("categories"). The same properties and entities can appear in several categories.
### Source Data
The data was compiled from raw DBpedia triples. [This paper](https://www.aclweb.org/anthology/C16-1141/) explains how the triples were selected.
#### Initial Data Collection and Normalization
Initial triples extracted from DBpedia were modified in several ways. See [official documentation](https://webnlg-challenge.loria.fr/docs/) for the most frequent changes that have been made. An original tripleset and a modified tripleset usually represent a one-to-one mapping. However, there are cases with many-to-one mappings when several original triplesets are mapped to one modified tripleset.
Entities that served as roots of RDF trees are listed in [this file](https://gitlab.com/shimorina/webnlg-dataset/-/blob/master/supplementary/entities_dict.json).
The English WebNLG 2020 dataset (v3.0) for training comprises data-text pairs for 16 distinct DBpedia categories:
- The 10 seen categories used in the 2017 version: Airport, Astronaut, Building, City, ComicsCharacter, Food, Monument, SportsTeam, University, and WrittenWork.
- The 5 unseen categories of 2017, which are now part of the seen data: Athlete, Artist, CelestialBody, MeanOfTransportation, Politician.
- 1 new category: Company.
The Russian dataset (v3.0) comprises data-text pairs for 9 distinct categories: Airport, Astronaut, Building, CelestialBody, ComicsCharacter, Food, Monument, SportsTeam, and University.
#### Who are the source language producers?
There are no source texts, all textual material was compiled during the annotation process.
### Annotations
#### Annotation process
Annotators were first asked to create sentences that verbalise single triples. In a second round, annotators were asked to combine single-triple sentences together into sentences that cover 2 triples. And so on until 7 triples. Quality checks were performed to ensure the quality of the annotations. See Section 3.3 in [the dataset paper](https://www.aclweb.org/anthology/P17-1017.pdf).
Russian data was translated from English with an MT system and then was post-edited by crowdworkers. See Section 2.2 of [this paper](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf).
#### Who are the annotators?
All references were collected through crowdsourcing platforms (CrowdFlower/Figure 8 and Amazon Mechanical Turk). For Russian, post-editing was done using the Yandex.Toloka crowdsourcing platform.
### Personal and Sensitive Information
Neither the dataset as published or the annotation process involves the collection or sharing of any kind of personal / demographic information.
## Considerations for Using the Data
### Social Impact of Dataset
We do not foresee any negative social impact in particular from this dataset or task.
Positive outlooks: Being able to generate good quality text from RDF data would permit, e.g., making this data more accessible to lay users, enriching existing text with information drawn from knowledge bases such as DBpedia or describing, comparing and relating entities present in these knowledge bases.
### Discussion of Biases
This dataset is created using DBpedia RDF triples which naturally exhibit biases that have been found to exist in Wikipedia such as some forms of, e.g., gender bias.
The choice of [entities](https://gitlab.com/shimorina/webnlg-dataset/-/blob/master/supplementary/entities_dict.json), described by RDF trees, was not controlled. As such, they may contain gender biases; for instance, all the astronauts described by RDF triples are male. Hence, in texts, pronouns _he/him/his_ occur more often. Similarly, entities can be related to the Western culture more often than to other cultures.
### Other Known Limitations
The quality of the crowdsourced references is limited, in particular in terms of fluency/naturalness of the collected texts.
Russian data was machine-translated and then post-edited by crowdworkers, so some examples may still exhibit issues related to bad translations.
## Additional Information
### Dataset Curators
The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil).
The dataset construction was funded by the French National Research Agency (ANR).
### 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 WebNLG corpus, cite:
```
@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 [@albertvillanova](https://huggingface.co/albertvillanova) for adding this dataset.
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wujohns/gpt2-base-learn
|
wujohns
| 2023-03-26T10:54:40Z | 52 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-16T04:28:01Z |
---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 9576485.5665
num_examples: 6033
- name: test
num_bytes: 232838.225
num_examples: 151
download_size: 4622568
dataset_size: 9809323.7915
---
|
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J4YL19/biored_tokenized
|
J4YL19
| 2023-04-06T22:33:57Z | 52 | 0 |
[
"region:us"
] | null | 2023-04-06T22:33:48Z |
---
dataset_info:
features:
- name: pmid
dtype: string
- name: passage
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence: string
splits:
- name: train
num_bytes: 2259680
num_examples: 387
- name: val
num_bytes: 604670
num_examples: 98
- name: test
num_bytes: 576610
num_examples: 97
download_size: 1083246
dataset_size: 3440960
---
# Dataset Card for "biored_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
mstz/pol
|
mstz
| 2023-04-16T17:58:01Z | 52 | 0 |
[
"task_categories:tabular-classification",
"size_categories:10k<n<100K",
"language:en",
"license:cc",
"pol",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-10T23:31:17Z |
---
language:
- en
tags:
- pol
- tabular_classification
- binary_classification
- UCI
pretty_name: Pol
size_categories:
- 10k<n<100K
task_categories:
- tabular-classification
configs:
- pol
license: cc
---
# Pol
The [Pol dataset](https://www.openml.org/search?type=data&sort=runs&id=151&status=active) from the [OpenML repository](https://www.openml.org/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| pol | Binary classification | Has the pol cost gone up?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/pol", "pol")["train"]
```
|
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] |
kiviki/SlovakSum
|
kiviki
| 2023-05-05T12:14:16Z | 52 | 4 |
[
"license:openrail",
"region:us"
] | null | 2023-04-20T07:41:09Z |
---
license: openrail
---
The SlovakSum dataset from the SlovakSum: Slovak News Summarization Dataset paper
|
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slvnwhrl/blurbs-clustering-s2s
|
slvnwhrl
| 2023-04-24T11:40:19Z | 52 | 0 |
[
"size_categories:10K<n<100K",
"language:de",
"license:cc-by-nc-4.0",
"embeddings",
"clustering",
"benchmark",
"region:us"
] | null | 2023-04-21T14:16:09Z |
---
license: cc-by-nc-4.0
language:
- de
tags:
- embeddings
- clustering
- benchmark
size_categories:
- 10K<n<100K
---
This dataset can be used as a benchmark for clustering word embeddings for <b>German</b>.
The datasets contains book titles and is based on the dataset from the [GermEval 2019 Shared Task on Hierarchical Classification of Blurbs](https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html). It contains 17'726 unqiue samples, 28 splits with 177 to 16'425 samples and 4 to 93 unique classes. Splits are built similarly to [MTEB](https://github.com/embeddings-benchmark/mteb)'s [ArxivClusteringS2S](https://huggingface.co/datasets/mteb/arxiv-clustering-s2s).
Have a look at [German Text Embedding Clustering Benchmark](https://github.com/ClimSocAna/tecb-de) for more infos, datasets and evaluation results.
|
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gimmaru/glue-sst2
|
gimmaru
| 2023-05-08T03:00:47Z | 52 | 0 |
[
"region:us"
] | null | 2023-05-08T03:00:07Z |
---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: idx
dtype: int32
splits:
- name: validation
num_bytes: 106252
num_examples: 872
download_size: 0
dataset_size: 106252
---
# Dataset Card for "glue-sst2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
under-tree/prepared-yagpt
|
under-tree
| 2023-05-18T12:26:50Z | 52 | 1 |
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:ru",
"region:us"
] |
[
"conversational",
"text-generation"
] | 2023-05-18T12:17:21Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 42680359.78397168
num_examples: 53550
- name: test
num_bytes: 7532625.216028317
num_examples: 9451
download_size: 25066987
dataset_size: 50212985
task_categories:
- conversational
- text-generation
language:
- ru
pretty_name: Dialogue Dataset for YAGPT ChatBot
size_categories:
- 10K<n<100K
---
# Dataset Card for "prepared-yagpt"
## Short Description
This dataset is aimed for training of chatbots on russian language.
It consists plenty of dialogues that allows you to train you model answer user prompts.
## Notes
1. Special tokens
- history, speaker1, speaker2 (history can be optionally removed, i.e. substituted on empty string)
2. Dataset is based on
- [Matreshka](https://huggingface.co/datasets/zjkarina/matreshka)
- [Yandex-Q](https://huggingface.co/datasets/its5Q/yandex-q)
- [Diasum](https://huggingface.co/datasets/bragovo/diasum)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Enno-Ai/fr-instructs
|
Enno-Ai
| 2023-06-26T23:16:02Z | 52 | 5 |
[
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"size_categories:10M<n<100M",
"language:fr",
"license:cc-by-2.5",
"region:us"
] |
[
"text2text-generation",
"table-question-answering"
] | 2023-05-29T14:11:48Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 5904510661
num_examples: 11794112
download_size: 1623654660
dataset_size: 5904510661
license: cc-by-2.5
task_categories:
- text2text-generation
- table-question-answering
language:
- fr
size_categories:
- 10M<n<100M
---
# A collection of 12 million french-only instructions deduplicated from various sources
Source :
- clips/mqa-fr-faq
- multilingual-wikihow-qa-16k
- MBZUAI/Bactrian-X
- argilla/databricks-dolly-15k-curated-multilingual
- innermost47/alpaca-fr
- etalab-ia/piaf
|
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] |
clarin-knext/arguana-pl-qrels
|
clarin-knext
| 2023-06-07T08:16:24Z | 52 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:13:33Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
ejschwartz/oo-method-test-split
|
ejschwartz
| 2023-09-11T19:21:06Z | 52 | 0 |
[
"task_categories:text-classification",
"region:us"
] |
[
"text-classification"
] | 2023-06-20T18:50:45Z |
---
task_categories:
- text-classification
train-eval-index:
- config: bylibrary
task: text-classification
task_id: binary_classification
splits:
eval_split: test
col_mapping:
Disassembly: text
Type: target
---
TODO: Add datacard
|
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] |
jjzha/skillspan
|
jjzha
| 2023-09-07T12:12:10Z | 52 | 0 |
[
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:37:04Z |
---
license: cc-by-4.0
language: en
---
This is the SkillSpan dataset created by:
```
@inproceedings{zhang-etal-2022-skillspan,
title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings",
author = "Zhang, Mike and
Jensen, Kristian and
Sonniks, Sif and
Plank, Barbara",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.366",
doi = "10.18653/v1/2022.naacl-main.366",
pages = "4962--4984"
}
```
There are document delimiters indicated by `idx`.
Number of samples (sentences):
- train: 4800
- dev: 3174
- test: 3569
Sources:
- Stackoverflow (tech)
- STAR (house)
Type of tags:
- Generic BIO tags with keys `tags_skill` and `tags_knowledge`
Sample:
```
{
"idx": 53,
"tokens": ["Drive", "our", "IT", "compliance", "agenda", "and", "develop", "our", "processes"],
"tags_skill": ["B", "I", "I", "I", "I", "O", "B", "I", "I"],
"tags_knowledge": ["O", "O", "O", "O", "O", "O", "O", "O", "O"],
"source": "house"
}
```
|
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