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|---|---|---|---|---|---|---|---|---|---|
usvsnsp/pile-semantic-memorization-filter-results | 2023-09-19T18:56:42.000Z | [
"region:us"
] | usvsnsp | null | null | null | 0 | 8 | ---
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---
# Dataset Card for "pile-semantic-memorization-filter-results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gunika-dhingra/findsum_worked | 2023-09-20T05:09:07.000Z | [
"region:us"
] | gunika-dhingra | null | null | null | 0 | 8 | Entry not found |
legacy107/qa_wikipedia_chunked | 2023-09-21T04:25:09.000Z | [
"region:us"
] | legacy107 | null | null | null | 0 | 8 | ---
configs:
- config_name: default
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- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
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download_size: 2970213547
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---
# Dataset Card for "qa_wikipedia_chunked"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Linyuyu/zhouguangbo | 2023-10-10T07:07:33.000Z | [
"region:us"
] | Linyuyu | null | null | null | 0 | 8 | Entry not found |
maxxrichard/sports_plan | 2023-09-20T13:15:01.000Z | [
"region:us"
] | maxxrichard | null | null | null | 0 | 8 | Entry not found |
sugeun/pat_japan | 2023-09-21T04:03:32.000Z | [
"region:us"
] | sugeun | null | null | null | 0 | 8 | Entry not found |
Jackoon/dataset_huy | 2023-09-21T07:57:03.000Z | [
"region:us"
] | Jackoon | null | null | null | 0 | 8 | Entry not found |
DarrenLo/ygo_tcgcards | 2023-10-05T13:45:10.000Z | [
"region:us"
] | DarrenLo | null | null | null | 0 | 8 | |
ToniAqqia/chico_synthetic | 2023-09-21T19:12:49.000Z | [
"license:mit",
"region:us"
] | ToniAqqia | null | null | null | 0 | 8 | ---
license: mit
---
|
minwook/novatusTest | 2023-09-22T02:27:11.000Z | [
"region:us"
] | minwook | null | null | null | 0 | 8 | ---
dataset_info:
features:
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "novatusTest"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kewu93/three_styles_prompted_250_512x512_50perclass_identity | 2023-09-22T13:24:26.000Z | [
"region:us"
] | kewu93 | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
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- name: val
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num_examples: 150
download_size: 0
dataset_size: 8651954.0
---
# Dataset Card for "three_styles_prompted_250_512x512_50perclass_identity"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sankettgorey/donut_model_invoice | 2023-09-22T13:13:42.000Z | [
"region:us"
] | sankettgorey | null | null | null | 0 | 8 | Entry not found |
aditijha/instruct_control_and_lima | 2023-09-22T21:13:23.000Z | [
"region:us"
] | aditijha | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: prompt
dtype: string
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- name: source
dtype: string
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num_bytes: 7084154
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download_size: 4023227
dataset_size: 7084154
---
# Dataset Card for "instruct_control_and_lima"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aditijha/instruct_v1_2k | 2023-09-22T21:16:55.000Z | [
"region:us"
] | aditijha | null | null | null | 0 | 8 | ---
dataset_info:
features:
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dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 1475295.9366042826
num_examples: 2000
download_size: 788725
dataset_size: 1475295.9366042826
---
# Dataset Card for "instruct_v1_2k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
seank0602/A03_fandom_pygmalion | 2023-09-23T17:35:57.000Z | [
"region:us"
] | seank0602 | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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list:
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- name: value
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num_bytes: 1477380
num_examples: 750
download_size: 381654
dataset_size: 1477380
---
# Dataset Card for "A03_fandom_pygmalion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Hadnet/olavo-notes-dataset | 2023-09-23T19:15:28.000Z | [
"region:us"
] | Hadnet | null | null | null | 0 | 8 | ---
dataset_info:
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "olavo-notes-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dim/povarenok_links | 2023-09-23T22:52:09.000Z | [
"region:us"
] | dim | null | null | null | 0 | 8 | ---
dataset_info:
features:
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dtype: string
- name: ingridients
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- name: likes
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- name: link
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download_size: 2195713
dataset_size: 15412981
---
# Dataset Card for "povarenok_links"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kris8an/begin_only | 2023-09-25T15:01:40.000Z | [
"region:us"
] | Kris8an | null | null | null | 0 | 8 | Entry not found |
Kris8an/64_samples | 2023-09-25T16:10:07.000Z | [
"region:us"
] | Kris8an | null | null | null | 0 | 8 | Entry not found |
Kris8an/gtp_70k | 2023-09-25T16:58:37.000Z | [
"region:us"
] | Kris8an | null | null | null | 0 | 8 | Entry not found |
aditijha/instruct_v1_5k_and_lima | 2023-09-26T02:22:11.000Z | [
"region:us"
] | aditijha | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 6691318
num_examples: 6000
download_size: 3598588
dataset_size: 6691318
---
# Dataset Card for "instruct_v1_5k_and_lima"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aditijha/instruct_v1_10k_and_lima | 2023-09-26T02:22:34.000Z | [
"region:us"
] | aditijha | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
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dtype: string
splits:
- name: train
num_bytes: 10473658
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download_size: 5587292
dataset_size: 10473658
---
# Dataset Card for "instruct_v1_10k_and_lima"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nitinbhayana/review_data_training | 2023-09-26T07:26:09.000Z | [
"region:us"
] | nitinbhayana | null | null | null | 0 | 8 | Entry not found |
open-ko-llm-leaderboard/results | 2023-10-10T22:41:02.000Z | [
"region:us"
] | open-ko-llm-leaderboard | null | null | null | 0 | 8 | Entry not found |
NusaCrowd/nergrit | 2023-09-26T12:35:09.000Z | [
"language:ind",
"license:mit",
"named-entity-recognition",
"region:us"
] | NusaCrowd | Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).
The Named Entity Recognition contains 18 entities as follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'GPE': Geopolitical Entity
'LAW': Law Entity (such as Undang-Undang)
'LOC': Location
'MON': Money
'NOR': Political Organization
'ORD': Ordinal
'ORG': Organization
'PER': Person
'PRC': Percent
'PRD': Product
'QTY': Quantity
'REG': Religion
'TIM': Time
'WOA': Work of Art
'LAN': Language | @misc{Fahmi_NERGRIT_CORPUS_2019,
author = {Fahmi, Husni and Wibisono, Yudi and Kusumawati, Riyanti},
title = {{NERGRIT CORPUS}},
url = {https://github.com/grit-id/nergrit-corpus},
year = {2019}
} | null | 0 | 8 | ---
license: mit
tags:
- named-entity-recognition
language:
- ind
---
# nergrit
Nergrit Corpus is a dataset collection of Indonesian Named Entity Recognition (NER), Statement Extraction,
and Sentiment Analysis developed by PT Gria Inovasi Teknologi (GRIT).
The Named Entity Recognition contains 18 entities as follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'GPE': Geopolitical Entity
'LAW': Law Entity (such as Undang-Undang)
'LOC': Location
'MON': Money
'NOR': Political Organization
'ORD': Ordinal
'ORG': Organization
'PER': Person
'PRC': Percent
'PRD': Product
'QTY': Quantity
'REG': Religion
'TIM': Time
'WOA': Work of Art
'LAN': Language
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@misc{Fahmi_NERGRIT_CORPUS_2019,
author = {Fahmi, Husni and Wibisono, Yudi and Kusumawati, Riyanti},
title = {{NERGRIT CORPUS}},
url = {https://github.com/grit-id/nergrit-corpus},
year = {2019}
}
```
## License
MIT
## Homepage
[https://github.com/grit-id/nergrit-corpus](https://github.com/grit-id/nergrit-corpus)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
Srinivas7/Data_bot | 2023-09-26T18:57:08.000Z | [
"license:other",
"region:us"
] | Srinivas7 | null | null | null | 0 | 8 | ---
license: other
---
|
Aliki/areto26 | 2023-09-27T08:43:35.000Z | [
"region:us"
] | Aliki | null | null | null | 0 | 8 | Entry not found |
yuanmei424/xxt_en | 2023-09-26T19:00:06.000Z | [
"region:us"
] | yuanmei424 | null | null | null | 0 | 8 | ---
dataset_info:
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dtype: image
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 5329195147.25
num_examples: 2283951
download_size: 526250170
dataset_size: 5329195147.25
---
# Dataset Card for "xxt_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Audi0911/loganalyzer | 2023-09-26T19:27:52.000Z | [
"license:openrail",
"region:us"
] | Audi0911 | null | null | null | 0 | 8 | ---
license: openrail
---
|
Rageshhf/llama_recommendation_datset | 2023-09-27T07:01:36.000Z | [
"region:us"
] | Rageshhf | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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dtype: string
- name: instruction
dtype: string
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dtype: string
splits:
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num_bytes: 11447133
num_examples: 3283
download_size: 3250903
dataset_size: 11447133
---
# Dataset Card for "llama_recommendation_datset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Aples/FineTune_Dataset_Aples_1K | 2023-09-27T19:26:31.000Z | [
"license:mit",
"region:us"
] | Aples | null | null | null | 0 | 8 | ---
license: mit
---
|
Globaly/categories-1k | 2023-09-27T21:25:06.000Z | [
"region:us"
] | Globaly | null | null | null | 0 | 8 | Entry not found |
infCapital/viet-llama2-ft-tiny | 2023-09-28T07:12:29.000Z | [
"license:mit",
"region:us"
] | infCapital | null | null | null | 0 | 8 | ---
license: mit
dataset_info:
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dtype: string
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splits:
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download_size: 143427693
dataset_size: 287453537
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Surajsangwan90/Benchmark_LLM | 2023-09-30T06:33:29.000Z | [
"region:us"
] | Surajsangwan90 | null | null | null | 0 | 8 | Entry not found |
Anas986/amazon-shoe-reviews | 2023-09-29T10:00:32.000Z | [
"region:us"
] | Anas986 | null | null | null | 0 | 8 | ---
dataset_info:
features:
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num_examples: 9000
download_size: 10009431
dataset_size: 16809292.0
---
# Dataset Card for "amazon-shoe-reviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TheAIchemist13/malyalam_asr_dataset | 2023-09-29T12:16:02.000Z | [
"region:us"
] | TheAIchemist13 | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: audio
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num_bytes: 1437332887.196
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num_bytes: 576755142.814
num_examples: 1103
download_size: 1668143452
dataset_size: 2014088030.0100002
---
# Dataset Card for "malyalam_asr_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jonathanasdf/MathGLM-dataset-5M | 2023-09-29T19:10:31.000Z | [
"license:afl-3.0",
"region:us"
] | jonathanasdf | null | null | null | 0 | 8 | ---
license: afl-3.0
---
Every 10th row from https://github.com/THUDM/MathGLM (original dataset has 50M entries) |
anirudh-sub/debate_dataset_v2 | 2023-09-29T23:05:46.000Z | [
"region:us"
] | anirudh-sub | null | null | null | 0 | 8 | Entry not found |
anirudh-sub/debate_dataset_v3.1 | 2023-09-30T00:52:51.000Z | [
"region:us"
] | anirudh-sub | null | null | null | 0 | 8 | Entry not found |
amphora/plat-clean | 2023-09-30T01:19:55.000Z | [
"region:us"
] | amphora | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 33608338
num_examples: 24926
download_size: 16086395
dataset_size: 33608338
---
# Dataset Card for "plat-clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AtAndDev/ShareGPT-Vicuna-v3-cleaned-unfiltered | 2023-09-30T12:03:18.000Z | [
"region:us"
] | AtAndDev | null | null | null | 0 | 8 | ---
dataset_info:
features:
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dtype: string
- name: text
dtype: string
splits:
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num_bytes: 1211675
num_examples: 145
download_size: 0
dataset_size: 1211675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ShareGPT-Vicuna-v3-cleaned-unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mHossain/social_mdedia_comment_v2 | 2023-09-30T12:51:40.000Z | [
"region:us"
] | mHossain | null | null | null | 0 | 8 | Entry not found |
youngermax/sherlock | 2023-10-01T01:35:46.000Z | [
"region:us"
] | youngermax | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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dtype: string
- name: infobox
dtype: string
splits:
- name: train
num_bytes: 373301449
num_examples: 27906
download_size: 216489948
dataset_size: 373301449
---
# Dataset Card for "sherlock"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SebastianMoncaleano/llama2-5.2k-cammel | 2023-10-01T20:56:23.000Z | [
"region:us"
] | SebastianMoncaleano | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1261568
num_examples: 5249
download_size: 240693
dataset_size: 1261568
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama2-5.2k-cammel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Valarmathy/cricket_indvspak | 2023-10-02T04:36:44.000Z | [
"task_categories:table-question-answering",
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"license:cc0-1.0",
"region:us"
] | Valarmathy | null | null | null | 0 | 8 | ---
license: cc0-1.0
configs:
- config_name: Valarmathy--cricket_indvspak
task_categories:
- table-question-answering
- tabular-classification
size_categories:
- 1K<n<10K
--- |
BaffledCoder/EnvironmentalScience | 2023-10-06T13:09:13.000Z | [
"region:us"
] | BaffledCoder | null | null | null | 0 | 8 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[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] |
cbasconc/levels | 2023-10-02T20:01:50.000Z | [
"language:es",
"region:us"
] | cbasconc | null | null | null | 0 | 8 | ---
language:
- es
pretty_name: levels
--- |
neural-bridge/full_cqa_22k | 2023-10-02T20:14:12.000Z | [
"region:us"
] | neural-bridge | null | null | null | 0 | 8 | ---
dataset_info:
features:
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dtype: string
splits:
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num_bytes: 43183498.53262665
num_examples: 17433
- name: test
num_bytes: 10797732.467373349
num_examples: 4359
download_size: 32335855
dataset_size: 53981231.0
---
# Dataset Card for "full_cqa_12k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
relaxtraffic/attrain | 2023-10-03T09:36:58.000Z | [
"region:us"
] | relaxtraffic | null | null | null | 0 | 8 | Entry not found |
Falah/3d_perspective_drawing | 2023-10-03T12:36:18.000Z | [
"region:us"
] | Falah | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 174080
num_examples: 1000
download_size: 18501
dataset_size: 174080
---
# Dataset Card for "3d_perspective_drawing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kris8an/30k_no_obs | 2023-10-03T16:43:30.000Z | [
"region:us"
] | Kris8an | null | null | null | 0 | 8 | Entry not found |
yudiwbs/olimpiade | 2023-10-04T02:44:10.000Z | [
"region:us"
] | yudiwbs | null | null | null | 0 | 8 | Sumber: https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results
Dataset untuk modul praktikum https://docs.google.com/document/d/1ehUlhdLeubEJz9qc3fvGeCRtZindHhowyGhg5Pbqq3w/edit |
lhallee/uniref_small | 2023-10-04T03:12:15.000Z | [
"region:us"
] | lhallee | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: uniref
dtype: string
splits:
- name: train
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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) |
dz-data-ai/a10_command_script | 2023-10-04T08:29:15.000Z | [
"license:cc",
"region:us"
] | dz-data-ai | null | null | null | 0 | 8 | ---
license: cc
pretty_name: 아마란스10 커맨드 스크립트
--- |
AndyLiu0104/Soldering-Data-Tiny-1004-unsolder-area | 2023-10-04T16:28:52.000Z | [
"region:us"
] | AndyLiu0104 | null | null | null | 0 | 8 | ---
dataset_info:
features:
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- name: text
dtype: string
splits:
- name: train
num_bytes: 18073742.875
num_examples: 10481
download_size: 0
dataset_size: 18073742.875
---
# Dataset Card for "Soldering-Data-Tiny-1004-unsolder-area"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HamdanXI/cleaned_daily_dialog_sentence | 2023-10-04T07:42:26.000Z | [
"region:us"
] | HamdanXI | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: dialogue
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download_size: 3467625
dataset_size: 5434241
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cleaned_daily_dialog_sentence"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Viswa09/gujarati_speechdata | 2023-10-05T03:40:48.000Z | [
"region:us"
] | Viswa09 | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
dtype: audio
- name: language
dtype: string
- name: language_probability
dtype: float64
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splits:
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num_examples: 434
download_size: 2099229077
dataset_size: 4531512898.0
---
# Dataset Card for "gujarati_speechdata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BlazeLlama/euclid_elements_eng_propositions | 2023-10-04T21:35:19.000Z | [
"license:apache-2.0",
"region:us"
] | BlazeLlama | null | null | null | 0 | 8 | ---
license: apache-2.0
---
|
gayanin/pubmed-abs-del-25 | 2023-10-05T00:35:34.000Z | [
"region:us"
] | gayanin | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
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dtype: int64
- name: refs
dtype: string
- name: del_25
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num_examples: 9341
- name: validation
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num_examples: 9341
download_size: 13058446
dataset_size: 23290020
---
# Dataset Card for "pubmed-abs-del-25"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kkboy1/leaudiocsv | 2023-10-05T01:29:33.000Z | [
"region:us"
] | kkboy1 | null | null | null | 0 | 8 | |
ikiransuryavanshi/llama_training | 2023-10-05T09:46:13.000Z | [
"region:us"
] | ikiransuryavanshi | null | null | null | 0 | 8 | Entry not found |
daspartho/agree_disagree | 2023-10-05T13:46:44.000Z | [
"region:us"
] | daspartho | null | null | null | 1 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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dtype: string
- name: reply
dtype: string
- name: sentiment
dtype: int64
splits:
- name: train
num_bytes: 267030
num_examples: 1660
download_size: 113328
dataset_size: 267030
---
# Dataset Card for "agree_disagree"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vsarathy/nl-robotics-translation-simple_english-30k-context | 2023-10-05T14:43:52.000Z | [
"region:us"
] | vsarathy | null | null | null | 0 | 8 | Entry not found |
lofcz/cs_autotherapy | 2023-10-05T21:43:21.000Z | [
"license:mit",
"region:us"
] | lofcz | null | null | null | 0 | 8 | ---
license: mit
---
|
minh21/COVID-QA-sentence-transformer-biencoder-data-75_25 | 2023-10-06T07:38:09.000Z | [
"region:us"
] | minh21 | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: string
- name: positive
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- name: negative
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num_examples: 1360
download_size: 1946559
dataset_size: 27662590
---
# Dataset Card for "COVID-QA-sentence-transformer-biencoder-data-75_25"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
berardi6/LBcmopcenscaspnewwsx2 | 2023-10-06T10:35:51.000Z | [
"region:us"
] | berardi6 | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 503461
num_examples: 1788
download_size: 0
dataset_size: 503461
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "LBcmopcenscaspnewwsx2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
c123ian/khan_academy_context | 2023-10-06T12:04:19.000Z | [
"region:us"
] | c123ian | null | null | null | 0 | 8 | ---
dataset_info:
features:
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dtype: string
splits:
- name: train
num_bytes: 20828078
num_examples: 2167
download_size: 8344879
dataset_size: 20828078
---
# Dataset Card for "khan_academy_context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dmrau/trec_dl19 | 2023-10-09T13:07:39.000Z | [
"region:us"
] | dmrau | null | null | null | 0 | 8 | ---
configs:
- config_name: default
data_files:
- split: queries
path: data/queries-*
- split: corpus
path: data/corpus-*
dataset_info:
features:
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dtype: string
- name: text
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num_examples: 43
- name: corpus
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num_examples: 5482
download_size: 1207481
dataset_size: 2184004
---
# Dataset Card for "trec_dl19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tog/dolphin_5k_test | 2023-10-06T15:06:19.000Z | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] | tog | null | null | null | 0 | 8 | ---
language:
- en
license: apache-2.0
task_categories:
- text-generation
dataset_info:
features:
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dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
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num_bytes: 8726321.400179625
num_examples: 5000
download_size: 4973800
dataset_size: 8726321.400179625
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Tiny Dolphin 🐬
see https://erichartford.com/dolphin
## Dataset details
This dataset is an extract of ~1 million of FLANv2 augmented with GPT-4 completions (flan1m-alpaca-uncensored.jsonl). It is derived from this [dataset](https://huggingface.co/datasets/ehartford/dolphin)
### Loading
```python
dataset = load_dataset("tog/dolphin_5k_test)
```
This dataset is licensed apache-2.0 for commercial or non-commercial use. |
MaxReynolds/Lee_Souder_Combined | 2023-10-06T20:24:19.000Z | [
"region:us"
] | MaxReynolds | null | null | null | 0 | 8 | ---
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: 1084639.0
num_examples: 37
download_size: 1080965
dataset_size: 1084639.0
---
# Dataset Card for "Lee_Souder_Combined"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
stepkurniawan/qa-rag-llama2-13B-chat-hf | 2023-10-07T22:19:36.000Z | [
"license:mit",
"region:us"
] | stepkurniawan | null | null | null | 0 | 8 | ---
license: mit
dataset_info:
features:
- name: question
dtype: string
- name: ground_truths
sequence: string
- name: answer
dtype: string
- name: contexts
dtype: string
splits:
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num_bytes: 296284
num_examples: 100
download_size: 176771
dataset_size: 296284
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
The Question-Answer Dataset is : https://huggingface.co/datasets/stepkurniawan/qa_sustainability_wiki
It is answered by this LLM: https://huggingface.co/meta-llama/Llama-2-13b-hf
This dataset will be evaluated later by some framework like RAGAS, and be judged on how good the RAG model is. |
LongJiAn/marsh-capstone | 2023-10-07T06:17:47.000Z | [
"region:us"
] | LongJiAn | null | null | null | 0 | 8 | Entry not found |
JiggaBooJombs/Novelist | 2023-10-07T10:20:27.000Z | [
"license:apache-2.0",
"region:us"
] | JiggaBooJombs | null | null | null | 0 | 8 | ---
license: apache-2.0
---
|
carnival13/massive_val_DA5_tokenized | 2023-10-07T11:03:09.000Z | [
"region:us"
] | carnival13 | null | null | null | 0 | 8 | ---
dataset_info:
features:
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dtype: int64
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sequence: int32
- name: attention_mask
sequence: int8
splits:
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num_examples: 24160
download_size: 3778628
dataset_size: 16518310
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "massive_val_DA5_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SuodhanJ6/elliptic_txs_features | 2023-10-08T06:20:10.000Z | [
"region:us"
] | SuodhanJ6 | null | null | null | 0 | 8 | Entry not found |
Falah/book_cover_prompts_with_sections | 2023-10-08T08:54:49.000Z | [
"region:us"
] | Falah | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 393452
num_examples: 1000
download_size: 45494
dataset_size: 393452
---
# Dataset Card for "book_cover_prompts_with_sections"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/synpre_delete_1M | 2023-10-08T09:12:38.000Z | [
"region:us"
] | tyzhu | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 1742619734
num_examples: 1000000
- name: validation
num_bytes: 17552085
num_examples: 10000
download_size: 1091004286
dataset_size: 1760171819
---
# Dataset Card for "synpre_delete_1M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pythainlp/thaisum | 2023-10-08T14:06:17.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_d... | pythainlp | null | null | null | 0 | 8 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- th
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: ThaiSum
---
# Dataset Card for ThaiSum
This dataset was forked from [thaisum](https://huggingface.co/datasets/thaisum) to HF hub.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/nakhunchumpolsathien/ThaiSum
- **Repository:** https://github.com/nakhunchumpolsathien/ThaiSum
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://github.com/nakhunchumpolsathien
### Dataset Summary
ThaiSum is a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs written by journalists.
### Supported Tasks and Leaderboards
summarization, language modeling
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'body': 'กีเก ซานเชซ ฟลอเรส\xa0 กุนซือเลือดกระทิงของทีมวัตฟอร์ด\xa0 เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง,สำนักข่าวต่างประเทศรายงานวันที่ 27 ก.ย. ว่า กีเก ซานเชซ ฟลอเรส\xa0 ผู้จัดการทีมชาวสเปน ของ แตนอาละวาด วัตฟอร์ด\xa0 ยอมรับทีมของเขาเล่นได้ไม่ดีพอเอง ในเกมพรีเมียร์ลีก อังกฤษ นัดเปิดบ้านพ่าย อินทรีผงาด คริสตัล พาเลซ 0-1 เมื่อคืนวันอาทิตย์ที่ผ่านมา,เกมนี้จุดเปลี่ยนมาอยู่ที่การได้จุดโทษในช่วงครึ่งหลังของ คริสตัล พาเลซ ซึ่งไม่ค่อยชัดเจนเท่าไหร่ว่า อัลลัน นียอม นั้นไปทำฟาล์วใส่ วิลฟรีด ซาฮา ในเขตโทษหรือไม่ แต่ผู้ตัดสินก็ชี้เป็นจุดโทษ ซึ่ง โยอัน กาบาย สังหารไม่พลาด และเป็นประตูชัยช่วยให้ คริสตัล พาเลซ เอาชนะ วัตฟอร์ด ไป 1-0 และเป็นการพ่ายแพ้ในบ้านนัดแรกของวัตฟอร์ดในฤดูกาลนี้อีกด้วย,ฟลอเรส กล่าวว่า มันเป็นเรื่องยากในการหยุดเกมรุกของคริสตัล พาเลซ ซึ่งมันอึดอัดจริงๆสำหรับเรา เราเล่นกันได้ไม่ดีนักในตอนที่ได้ครองบอล เราต้องเล่นทางริมเส้นให้มากกว่านี้ เราไม่สามารถหยุดเกมสวนกลับของพวกเขาได้ และแนวรับของเราก็ยืนไม่เป็นระเบียบสักเท่าไหร่ในช่วงครึ่งแรก ส่วนเรื่องจุดโทษการตัดสินใจขั้นสุดท้ายมันอยู่ที่ผู้ตัดสิน ซึ่งมันเป็นการตัดสินใจที่สำคัญ ผมเองก็ไม่รู้ว่าเขาตัดสินถูกหรือเปล่า บางทีมันอาจเป็นจุดที่ตัดสินเกมนี้เลย แต่เราไม่ได้แพ้เกมนี้เพราะจุดโทษ เราแพ้ในวันนี้เพราะเราเล่นไม่ดีและคริสตัล พาเลซ เล่นดีกว่าเรา เราไม่ได้มีฟอร์มการเล่นที่ดีในเกมนี้เลย', 'summary': 'กีเก ซานเชซ ฟลอเรส กุนซือเลือดกระทิงของทีมวัตฟอร์ด เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง', 'tags': 'พรีเมียร์ลีก,วัตฟอร์ด,คริสตัล พาเลซ,กีเก ซานเชซ ฟลอเรส,ข่าวกีฬา,ข่าว,ไทยรัฐออนไลน์', 'title': 'ฟลอเรส รับ วัตฟอร์ดห่วยเองเกมพ่ายพาเลซคาบ้าน', 'type': '', 'url': 'https://www.thairath.co.th/content/528322'}
```
### Data Fields
- `title`: title of article
- `body`: body of article
- `summary`: summary of article
- `type`: type of article, if any
- `tags`: tags of article, separated by `,`
- `url`: URL of article
### Data Splits
train/valid/test: 358868 / 11000 / 11000
## Dataset Creation
### Curation Rationale
Sequence-to-sequence (Seq2Seq) models have shown great achievement in text summarization. However, Seq2Seq model often requires large-scale training data to achieve effective results. Although many impressive advancements in text summarization field have been made, most of summarization studies focus on resource-rich languages. The progress of Thai text summarization is still far behind. The dearth of large-scale dataset keeps Thai text summarization in its infancy. As far as our knowledge goes, there is not a large-scale dataset for Thai text summarization available anywhere. Thus, we present ThaiSum, a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard.
### Source Data
#### Initial Data Collection and Normalization
We used a python library named Scrapy to crawl articles from several news websites namely Thairath, Prachatai, ThaiPBS and, The Standard. We first collected news URLs provided in their sitemaps. During web-crawling, we used HTML markup and metadata available in HTML pages to identify article text, summary, headline, tags and label. Collected articles were published online from 2014 to August 2020. <br> <br>
We further performed data cleansing process to minimize noisy data. We filtered out articles that their article text or summary is missing. Articles that contains article text with less than 150 words or summary with less than 15 words were removed. We also discarded articles that contain at least one of these following tags: ‘ดวง’ (horoscope), ‘นิยาย’ (novel), ‘อินสตราแกรมดารา’ (celebrity Instagram), ‘คลิปสุดฮา’(funny video) and ‘สรุปข่าว’ (highlight news). Some summaries were completely irrelevant to their original article texts. To eliminate those irrelevant summaries, we calculated abstractedness score between summary and its article text. Abstractedness score is written formally as: <br>
<center><a href="https://www.codecogs.com/eqnedit.php?latex=\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" title="\begin{equation} \frac{|S-A|}{r} \times 100 \end{equation}" /></a></center><br>
<br>Where 𝑆 denotes set of article tokens. 𝐴 denotes set of summary tokens. 𝑟 denotes a total number of summary tokens. We omitted articles that have abstractedness score at 1-grams higher than 60%.
<br><br>
It is important to point out that we used [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp), version 2.2.4, tokenizing engine = newmm, to process Thai texts in this study. It is challenging to tokenize running Thai text into words or sentences because there are not clear word/sentence delimiters in Thai language. Therefore, using different tokenization engines may result in different segment of words/sentences.
After data-cleansing process, ThaiSum dataset contains over 358,000 articles. The size of this dataset is comparable to a well-known English document summarization dataset, CNN/Dily mail dataset. Moreover, we analyse the characteristics of this dataset by measuring the abstractedness level, compassion rate, and content diversity. For more details, see [thaisum_exploration.ipynb](https://github.com/nakhunchumpolsathien/ThaiSum/blob/master/thaisum_exploration.ipynb).
#### Dataset Statistics
ThaiSum dataset consists of 358,868 articles. Average lengths of article texts and summaries are approximately 530 and 37 words respectively. As mentioned earlier, we also collected headlines, tags and labels provided in each article. Tags are similar to keywords of the article. An article normally contains several tags but a few labels. Tags can be name of places or persons that article is about while labels indicate news category (politic, entertainment, etc.). Ultimatly, ThaiSum contains 538,059 unique tags and 59 unique labels. Note that not every article contains tags or labels.
|Dataset Size| 358,868 | articles |
|:---|---:|---:|
|Avg. Article Length| 529.5 | words|
|Avg. Summary Length | 37.3 | words|
|Avg. Headline Length | 12.6 | words|
|Unique Vocabulary Size | 407,355 | words|
|Occurring > 10 times | 81,761 | words|
|Unique News Tag Size | 538,059 | tags|
|Unique News Label Size | 59 | labels|
#### Who are the source language producers?
Journalists of respective articles
### Annotations
#### Annotation process
`summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers.
#### Who are the annotators?
`summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers.
### Personal and Sensitive Information
All data are public news articles. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- News summarization in Thai
- Language modeling for Thai news
### Discussion of Biases
- [ThaiPBS](https://www.thaipbs.or.th/home) [receives funding from Thai government](https://www.bangkokbiznews.com/blog/detail/648740).
- [Thairath](https://www.thairath.co.th/) is known as [the most popular newspaper in Thailand](https://mgronline.com/onlinesection/detail/9620000058532); no clear political leaning.
- [The Standard](https://thestandard.co/) is a left-leaning online magazine.
- [Prachathai](https://prachatai.com/) is a left-leaning, human-right-focused news site.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[@nakhunchumpolsathien](https://github.com/nakhunchumpolsathien/)
[@caramelWaffle](https://github.com/caramelWaffle)
### Licensing Information
MIT License
### Citation Information
```
@mastersthesis{chumpolsathien_2020,
title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization},
author={Chumpolsathien, Nakhun},
year={2020},
school={Beijing Institute of Technology}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. |
kargaranamir/GlotLID | 2023-10-08T23:32:03.000Z | [
"multilinguality:multilingual",
"language:zxx",
"language:pes",
"license:cc0-1.0",
"Glot",
"Glot500",
"GlotLID",
"multilingual",
"LangID",
"LID",
"Language Identification",
"region:us"
] | kargaranamir | GlotLID Corpus \ | null | null | 0 | 8 | ---
multilinguality:
- multilingual
extra_gated_heading: Access GlotLID Corpus on Hugging Face
extra_gated_description: >-
This is a form to enable access to GlotLID Corpus on Hugging Face. Please email amir@cis.lmu.de with subject of "GlotLID Corpus", a form will be sent to you and after agreeing with our
license terms and use policy your requests to access this repository will be accepted. Requests
will be processed in 1-2 days.
extra_gated_prompt: "**Your Hugging Face account email address MUST match the email you provided us in form, or your request will not be approved.**"
extra_gated_button_content: Submit
extra_gated_fields:
Name: text
Email: text
Affiliation: text
Country: text
Usecase: text
I will only use this corpus myself for the use case that I have described above: checkbox
license: cc0-1.0
language:
- zxx
- pes
pretty_name: GlotLID Corpus
tags:
- Glot
- Glot500
- GlotLID
- multilingual
- LangID
- LID
- Language Identification
---
# GlotLID Corpus
## License
We do not own any of the text from which these data has been extracted.
We license the actual packaging, the metadata and the annotations of these data under the cc0-1.0 (waiving all of the rights under copyright law).
If you are a website/dataset owner and do not want your data to be included in this corpra, please send us an email at amir@cis.lmu.de .
## Ethical Considerations
**1. Biases:** The text corpus may reflect the perspectives, opinions, or demographics of its sources or creators. It is important for users to critically evaluate the text in context.
**2. Representativeness:** While we have aimed for diversity and inclusivity, the text corpus may not fully represent all native speakers. Users should be mindful of any potential underrepresentation.
**3. Ethics:** We acknowledge that the collection and use of text data can have ethical implications. We have strived to handle the data responsibly, but we encourage users to consider the broader ethical implications of their own research or applications.
## Citation
If you use any part of this code and collected data in your research, please cite it using the following BibTeX entry.
```
@inproceedings{
kargaran2023glotlid,
title={{GlotLID}: Language Identification for Low-Resource Languages},
author={Kargaran, Amir Hossein and Imani, Ayyoob and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=dl4e3EBz5j}
}
``` |
carnival13/eng_sur_val_DA_tokenized | 2023-10-09T07:13:09.000Z | [
"region:us"
] | carnival13 | null | null | null | 0 | 8 | ---
dataset_info:
features:
- name: pass_label
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 30391635
num_examples: 22390
download_size: 5882210
dataset_size: 30391635
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eng_sur_val_DA_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
boundless-asura/wikihow | 2023-10-09T08:34:42.000Z | [
"region:us"
] | boundless-asura | null | null | null | 0 | 8 | Entry not found |
imdatta0/openbqa_sciq | 2023-10-09T08:38:01.000Z | [
"region:us"
] | imdatta0 | null | null | null | 0 | 8 | Entry not found |
VishalCh/sql-parsed | 2023-10-09T13:11:05.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"SQL",
"code",
"NLP",
"text-to-sql",
"context-sql",
"spider",
"wikisql",
"sqlglot",
"region:us"
] | VishalCh | null | null | null | 0 | 8 | ---
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
- table-question-answering
language:
- en
tags:
- SQL
- code
- NLP
- text-to-sql
- context-sql
- spider
- wikisql
- sqlglot
pretty_name: sql-create-context
size_categories:
- 10K<n<100K
--- |
clickbait_news_bg | 2023-01-25T14:28:03.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bg",
"license:unknown",
"region:us"
] | null | Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017. | @InProceedings{clickbait_news_bg,
title = {Dataset with clickbait and fake news in Bulgarian. Introduced for the Hack the Fake News 2017.},
authors={Data Science Society},
year={2017},
url={https://gitlab.com/datasciencesociety/case_fake_news/}
} | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- bg
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: Clickbait/Fake News in Bulgarian
dataset_info:
features:
- name: fake_news_score
dtype:
class_label:
names:
'0': legitimate
'1': fake
- name: click_bait_score
dtype:
class_label:
names:
'0': normal
'1': clickbait
- name: content_title
dtype: string
- name: content_url
dtype: string
- name: content_published_time
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 24480402
num_examples: 2815
- name: validation
num_bytes: 6752242
num_examples: 761
download_size: 8569575
dataset_size: 31232644
---
# Dataset Card for Clickbait/Fake News in Bulgarian
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Data Science Society / Case Fake News](https://gitlab.com/datasciencesociety/case_fake_news)
- **Repository:** [Data Science Society / Case Fake News / Data](https://gitlab.com/datasciencesociety/case_fake_news/-/tree/master/data)
- **Paper:** [This paper uses the dataset.](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a corpus of Bulgarian news over a fixed period of time, whose factuality had been questioned.
The news come from 377 different sources from various domains, including politics, interesting facts and tips&tricks.
The dataset was prepared for the Hack the
Fake News hackathon. It was provided by the
[Bulgarian Association of PR Agencies](http://www.bapra.bg/) and is
available in [Gitlab](https://gitlab.com/datasciencesociety/).
The corpus was automatically collected, and then annotated by students of journalism.
The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news
and 1,968 (i.e., 70%) are click-baits; There are 761 testing examples.
There is 98% correlation between fake news and clickbaits.
One important aspect about the training dataset is that it contains many repetitions.
This should not be surprising as it attempts to represent a natural distribution of factual
vs. fake news on-line over a period of time. As publishers of fake news often have a group of
websites that feature the same deceiving content, we should expect some repetition.
In particular, the training dataset contains
434 unique articles with duplicates. These articles have three reposts each on average, with
the most reposted article appearing 45 times.
If we take into account the labels of the reposted articles, we can see that if an article
is reposted, it is more likely to be fake news.
The number of fake news that have a duplicate in the training dataset are 1018 whereas,
the number of articles with genuine content
that have a duplicate article in the training set is 322.
(The dataset description is from the following [paper](https://www.acl-bg.org/proceedings/2017/RANLP%202017/pdf/RANLP045.pdf).)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Bulgarian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Each entry in the dataset consists of the following elements:
* `fake_news_score` - a label indicating whether the article is fake or not
* `click_bait_score` - another label indicating whether it is a click-bait
* `content_title` - article heading
* `content_url` - URL of the original article
* `content_published_time` - date of publication
* `content` - article content
### Data Splits
The **training dataset** contains 2,815 examples, where 1,940 (i.e., 69%) are fake news
and 1,968 (i.e., 70%) are click-baits;
The **validation dataset** contains 761 testing examples.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@tsvm](https://github.com/tsvm), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
counter | 2023-01-25T14:28:41.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
... | null | The COrpus of Urdu News TExt Reuse (COUNTER) corpus contains 1200 documents with real examples of text reuse from the field of journalism. It has been manually annotated at document level with three levels of reuse: wholly derived, partially derived and non derived. | @Article{Sharjeel2016,
author="Sharjeel, Muhammad
and Nawab, Rao Muhammad Adeel
and Rayson, Paul",
title="COUNTER: corpus of Urdu news text reuse",
journal="Language Resources and Evaluation",
year="2016",
pages="1--27",
issn="1574-0218",
doi="10.1007/s10579-016-9367-2",
url="http://dx.doi.org/10.1007/s10579-016-9367-2"
} | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
- topic-classification
paperswithcode_id: counter
pretty_name: COUNTER
dataset_info:
features:
- name: source
struct:
- name: filename
dtype: string
- name: headline
dtype: string
- name: body
dtype: string
- name: total_number_of_words
dtype: int64
- name: total_number_of_sentences
dtype: int64
- name: number_of_words_with_swr
dtype: int64
- name: newspaper
dtype: string
- name: newsdate
dtype: string
- name: domain
dtype:
class_label:
names:
'0': business
'1': sports
'2': national
'3': foreign
'4': showbiz
- name: classification
dtype:
class_label:
names:
'0': wholly_derived
'1': partially_derived
'2': not_derived
- name: derived
struct:
- name: filename
dtype: string
- name: headline
dtype: string
- name: body
dtype: string
- name: total_number_of_words
dtype: int64
- name: total_number_of_sentences
dtype: int64
- name: number_of_words_with_swr
dtype: int64
- name: newspaper
dtype: string
- name: newsdate
dtype: string
- name: domain
dtype:
class_label:
names:
'0': business
'1': sports
'2': national
'3': foreign
'4': showbiz
- name: classification
dtype:
class_label:
names:
'0': wholly_derived
'1': partially_derived
'2': not_derived
splits:
- name: train
num_bytes: 2598872
num_examples: 600
download_size: 1356306
dataset_size: 2598872
---
# Dataset Card for COUNTER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://ucrel.lancs.ac.uk/textreuse/counter.php
- **Repository:** [More Information Needed]
- **Paper:** https://link.springer.com/article/10.1007%2Fs10579-016-9367-2
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [UCREL](ucrel@lancaster.ac.uk)
### Dataset Summary
The COrpus ofUrdu News TExt Reuse (COUNTER) corpus contains 1200 documents with realexamples of text reuse from the field of journalism. It has been manually annotatedat document level with three levels of reuse: wholly derived, partially derived andnon derived
### Supported Tasks and Leaderboards
other:text-reuse
### Languages
ur
## Dataset Structure
### Data Instances
Here is one example from the dataset:
```
{"derived": {
"body" :"میر پور(وقت نیوز) بنگلہ دیش نے 5 میچوں کی سیریز کےآ خری میچ میں بھی فتح حاصل کر کے سیریز میں وائٹ واش کر دیا،زمبابوے ایک میچ بھی نہ جیت سکا۔آخری میچ میں زمبابوے کے 129 رنز کا ہدف بنگال ٹائیگرز نے 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔بنگلہ دیش کے شیر بنگلہ سٹیڈیم میر پور میں کھیلے گئے آخری ایک روزہ میچ میں زمبابوے کے کپتان چکمبورا نے ٹاس جیت کے بینٹگ کا فیصلہ کیا جو ان کی ٹیم کیلئے ڈراؤنا خواب ثابت ہوا اور پوری ٹیم 30 اوورز میں 128 رنز بنا کر پویلین لوٹ گئی زمبابوے کی پہلی وکٹ 16 رنز پر گری جب سکندر رضا صرف 9 رنز بنا کر مشرقی مرتضی کی بال پر آؤٹ ہوئے اس کے بعد مساکد ازااور سباندا کی پارٹنرشپنے ٹیم کا سکور95 رنز تک پہنچا دیا ۔مساکدازا 52 رنز بنا کر جبیر الحسن کا شکار بنے جبکہ سباندا نے 37 رنز کی اننگز کھیلی اس کے بعد کئی بھی زمبابوے کا کھلاڑی جم کر نہ کھیل سکا۔بنگال ٹائیگرز کی جانب سے عمدہ باؤلنگ کے نتیجے میں کپتان چکمبورا سمیت 8 کھلاڑی ڈبل فیگر کراس نہ کر سکے ۔بنگلہ دیش کی جانب سے ایک روزہ میچوں میں ڈیبیو کرنے والے تیج السلام نے اپنے پہلے ہی میچ میں ہیٹرک کی اسلام نے 7 اوورز میں صرف 14 رنز دئے اور چار کھلاڑیوں کع آؤٹ کیا جبکہ شکیب الحسن نے 30 رنز دیکر 3 اور جبیر الحسن نے41 رنز دیکر2 کھلاڑیوں کو پویلین کی راہ دکھائی ۔ 128 رنز کے جواب میں بنگال ٹائیگرز نے بیٹنگ شروع کی مشکلات کا سامنا رہا ان کے بھی ابتدائی 3 کھلاڑی 47 رنز پر پویلین لوٹ گئے۔ تمیم اقبال 10، انعام الحق8 رنز بنا کر آؤٹ ہوئے،آل راؤنڈر شکیب الحسن بغیر کوئی رنز بنائیپویلین لوٹ گئے وکٹ کیپر مشفق الرحیم صرف 11 رنز بنا کر چتارہ کا شکار بن گئے۔محمد اللہ نے51 رنز کی میچ وننگ اننگز کھیلی جبکہ صابر رحمٰن13 رنز بنا کر ناٹ آؤٹ رہے۔ زمبابوے کی جانب سے چتارہ نے 3 اور پنیا نگارا نے 2 کھلاڑیوں کو آؤٹ کیا ۔فتح کے ساتھ بنگلہ دیش نے سیریز میں وائٹ واش کر دیا۔زمبابوے کی ٹیم کوئی میچ نہ جیت سکی،تیج السلام کو میچ کا بہترین ایوارڈ دیا گیا جبکہ سیریز کا بہترین کھلاڑی مشفق الرحیم کو قرار دیا گیا۔",
"classification": 1, # partially_derived
"domain": 1, # sports
"filename": "0001p.xml",
"headline": "بنگلہ دیش کا زمبابوے کا ون ڈے سیریز میں 5-0 سے وائٹ واش",
"newsdate": "02.12.14",
"newspaper": "daily_waqt",
"number_of_words_with_swr": 265,
"total_number_of_sentences": 13,
"total_number_of_words": 393},
"source": {
"body": "ڈھاکہ ۔ یکم دسمبر (اے پی پی) بنگلہ دیش نے زمبابوے کو ٹیسٹ کے بعد ون ڈے سیریز میں بھی وائٹ واش کر دیا۔ سیریز کے پانچویں اور آخری ون ڈے میچ میں بنگال ٹائیگرز نے زمبابوے کو 5 وکٹوں سے شکست دے دی، مہمان ٹیم پہلے بیٹنگ کرتے ہوئے 128 رنز پر ڈھیر ہوگئی۔ تیج الاسلام نے کیریئر کے پہلے ون ڈے میچ میں ہیٹ ٹرک کرکے نئی تاریخ رقم کر دی، انہوں نے 4 کھلاڑیوں کو آؤٹ کیا۔ جواب میں بنگلہ دیش نے ہدف 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔ محمد اللہ نے 51 رنز کی ناقابل شکست اننگز کھیلی۔ تفصیلات کے مطابق پیر کو شیر بنگلہ نیشنل سٹیڈیم، میرپور میں پانچویں اور آخری ون ڈے میچ میں زمبابوے کے کپتان ایلٹن چگمبورا نے ٹاس جیت کر پہلے بیٹنگ کا فیصلہ کیا جو غلط ثابت ہوا۔ زمبابوے کی پوری ٹیم ڈیبیو ون ڈے کھیلنے والے نوجوان لیفٹ آرم سپنر تیج الاسلام اور شکیب الحسن کی تباہ کن باؤلنگ کے باعث 30 اوورز میں 128 رنز پر ڈھیر ہوگئی۔ ہیملٹن ماساکڈزا 52 اور ووسی سبانڈا 37 رنز کے ساتھ نمایاں رہے، ان کے علاوہ کوئی بھی بلے باز دوہرا ہندسہ عبور نہ کر سکا۔ اپنا پہلا ون ڈے کھیلنے والے تیج الاسلام نے 11 رنز کے عوض 4 وکٹیں حاصل کیں جس میں شاندار ہیٹ ٹرک بھی شامل ہے، اس طرح وہ ڈیبیو میں ہیٹ ٹرک کرنے والے دنیا کے پہلے باؤلر بن گئے ہیں۔ شکیب الحسن نے تین اور زبیر حسین نے دو وکٹیں حاصل کیں۔ جواب میں بنگلہ دیش نے ہدف 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔ محمد اللہ نے 51 رنز کی ناقابل شکست اننگز کھیل کر ٹیم کی فتح میں اہم کردار ادا کیا۔ زمبابوے کی جانب سے ٹینڈائی چتارا نے تین اور تناشے پینگارا نے دو وکٹیں حاصل کیں۔",
"classification": 1, # partially_derived
"domain": 1, # sports
"filename": "0001.xml",
"headline": "بنگال ٹائیگرز نے کمزور زمبابوے کو ٹیسٹ کے بعد ون ڈے سیریز میں بھی وائٹ واش کر دیا، پانچویں اور آخری ون ڈے میچ میں بنگلہ دیش 5 وکٹوں سے فتح یاب، تیج الاسلام نے ڈیبیو ون ڈے میں ہیٹ ٹرک کرکے نئی تاریخ رقم کر دی"
"newsdate": "01.12.14",
"newspaper": "APP",
"number_of_words_with_swr": 245,
"total_number_of_sentences": 15,
"total_number_of_words": 352}}
```
### Data Fields
```source```: The source document
```derived```: The derived document
For each pair of source and derived documents. we have the following fields:
```filename (str)```: Name of the file in dataset
```headline(str)```: Headline of the news item
```body(str)```: Main text of the news item
```total_number_of_words(int)```: Number of words in document
```total_number_of_sentences(int)```: Number of sentences in document
```number_of_words_with_swr(int)```: Number of words after stop word removal
```newspaper(str)```: The newspaper in which the news item was published
```newsdate(str)```: The date on which the news item is published DD.MM.YY
```domain(int)```: The category of news item from this list: "business", "sports", "national", "foreign", "showbiz".
```classification (int)```: Three classes of reuse from this list: Wholly Derived (WD), Partially Derived (PD) and Non Derived (ND)
### Data Splits
One split train with 600 pairs of documents.
The corpus is composed of two main document types: (1) source documents and (2) derived documents. There are total 1200 documents in the corpus: 600 are newsagency articles (source documents) and 600 are newspapers stories (derived documents). The corpus contains in total 275,387 words (tokens8), 21,426 unique words and 10,841 sentences. The average length of a source document is 227 words while for derived documents it is 254 words.
## Dataset Creation
### Curation Rationale
Our main intention was to develop a standard benchmark resource for the evaluation of existing systems available for text reuse detection in general and specifically for Urdu language. To generate a corpus with realistic examples, we opted for the field of journalism. In journalism, the same news story is published in different newspapers in different forms. It is a standard practice followed by all the newspapers (reporters and editors) to reuse (verbatim or modified) a news story released by the news agency.
### Source Data
#### Initial Data Collection and Normalization
The COUNTER corpus consists of news articles (source documents) released by five news agencies in Pakistan i.e. Associated Press of Pakistan (APP), InternationalNews Network (INN), Independent News Pakistan (INP), News Network International (NNI) and South Asian News Agency (SANA). The corresponding news stories (derived documents) were extracted from nine daily published and large circulation national news papers of the All Pakistan Newspapers Society (APNS), who are subscribed to these news agencies.
These include Nawa-e-Waqt, Daily Dunya, Express, Jang, Daily Waqt, Daily Insaf, Daily Aaj, Daily Islam and DailyPakistan. All of them are part of the mainstream national press, long established dailies with total circulation figures of over four million.7News agency texts (source documents) were provided (in electronic form) by the news agencies on a daily basis when they released the news. Newspaper stories (derived documents) were collected by three volunteers over a period of six months (from July to December 2014).National, Foreign, Business, Sports and Showbiz were the domains targeted for data collection.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The corpus has been annotated at the document level with three classes of reuse i.e.Wholly Derived (WD), Partially Derived (PD) and Non Derived (ND).
The derived collection contains documents with various degrees of text reuse. Some of the newspaper stories (derived documents)are rewritten (either verbatim or paraphrased) from the new agencys text (source document) while others have been written by the journalists independently on their own. For the former case, source-derived document pairs are either tagged as Wholly Derived (WD) or Partially Derived (PD) depending on the volume of text reused from the news agencys text for creating the newspaper article while for the latter case, they are tagged as Non Derived (ND) as the journalists have not reused anything from the news agencys text but based on their own observations and findings, developed and documented the story.
The annotations were carried out in three phases: (1) training phase, (2) annotations, (3)conflict resolving. During the training phase, annotators A and B manually annotated 60 document pairs, following a preliminary version of the annotation guidelines. A detailed meeting was carried out afterwards, discussing the problems and disagreements. It was observed that the highest number of disagreements were between PD and ND cases, as both found it difficult to distinguish between these two classes. The reason being that adjusting the threshold where a text is heavily paraphrased or new information added to it that it becomes independently written(ND). Following the discussion, the annotation guidelines were slightly revised, and the first 60 annotations results were saved. In the annotation phase, the remaining540 document pairs were manually examined by the two annotators (A and B). Both were asked to judge, and classify (at document level) whether a document(newspaper story) depending on the volume of text rewritten from the source (news agency article) falls into one of the following categories:remaining540 document pairs were manually examined by the two annotators (A and B). Both were asked to judge, and classify (at document level) whether a document(newspaper story) depending on the volume of text rewritten from the source (news agency article) falls into one of the following categories:
Wholly Derived (WD)The News agency text is the only source for the reused newspaper text, which means it is a verbatim copy of the source. In this case, most of the reused text is word-to-word copy of the source text.Partially Derived (PD)The Newspaper text has been either derived from more than one news agency or most of the text is paraphrased by the editor when rewriting from news agency text source. In this case, most parts of the derived document contain paraphrased text or new facts and figures added by the journalists own findings. Non Derived (ND)The News agency text has not been used in the production of the newspaper text (though words may still co-occur in both documents), it has completely different facts and figures or is heavily paraphrased from the newsagencys copy. In this case, the derived document is independently written and has a lot more new text.
#### Who are the annotators?
The annotations were performed by three annotators (A, B and C), who were native Urdu language speakers and experts of paraphrasing mechanisms. All three were graduates, experienced in text annotations and having an advanced Urdu level.
### 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
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset is licensed under the Creative Common Attribution-NonCommercial-ShareAlike 4.0 International License.
[(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
```
@Article{Sharjeel2016,
author="Sharjeel, Muhammad
and Nawab, Rao Muhammad Adeel
and Rayson, Paul",
title="COUNTER: corpus of Urdu news text reuse",
journal="Language Resources and Evaluation",
year="2016",
pages="1--27",
issn="1574-0218",
doi="10.1007/s10579-016-9367-2",
url="http://dx.doi.org/10.1007/s10579-016-9367-2"
}
```
### Contributions
Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset. |
covid_qa_ucsd | 2023-06-01T14:59:47.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"languag... | null | null | @article{ju2020CovidDialog,
title={CovidDialog: Medical Dialogue Datasets about COVID-19},
author={Ju, Zeqian and Chakravorty, Subrato and He, Xuehai and Chen, Shu and Yang, Xingyi and Xie, Pengtao},
journal={ https://github.com/UCSD-AI4H/COVID-Dialogue},
year={2020}
} | null | 1 | 7 | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
language:
- en
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
pretty_name: CovidQaUcsd
dataset_info:
- config_name: en
features:
- name: dialogue_id
dtype: int32
- name: dialogue_url
dtype: string
- name: dialogue_turns
sequence:
- name: speaker
dtype:
class_label:
names:
'0': Patient
'1': Doctor
- name: utterance
dtype: string
splits:
- name: train
num_bytes: 484944
num_examples: 572
download_size: 0
dataset_size: 484944
- config_name: zh
features:
- name: dialogue_id
dtype: int32
- name: dialogue_url
dtype: string
- name: dialogue_turns
sequence:
- name: speaker
dtype:
class_label:
names:
'0': 病人
'1': 医生
- name: utterance
dtype: string
splits:
- name: train
num_bytes: 1352377
num_examples: 1088
download_size: 0
dataset_size: 1352377
config_names:
- en
- zh
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UCSD-AI4H/COVID-Dialogue
- **Repository:** The data is also present in the same [GIT](https://github.com/UCSD-AI4H/COVID-Dialogue) repository
- **Paper:** https://pengtaoxie.github.io/coviddiag.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
COVID-Dialogue-Dataset-English is an English medical dialogue dataset about COVID-19 and other types of pneumonia. Patients who are concerned that they may be infected by COVID-19 or other pneumonia consult doctors and doctors provide advice. There are 603 consultations.
COVID-Dialogue-Dataset-Chinese is a Chinese medical dialogue dataset about COVID-19 and other types of pneumonia. Patients who are concerned that they may be infected by COVID-19 or other pneumonia consult doctors and doctors provide advice. There are 1393 consultations.
The dataset is present as a single text file. COVID-Dialogue-Dataset-Chinese.txt for Chinese and COVID-Dialogue-Dataset-English.txt for English.
### Supported Tasks and Leaderboards
Used for QA tasks. There is also a COVID-19 dialogue generation model available for the Chinese Data. The pre-print and more information is available in [this arxiv pre-print](https://arxiv.org/abs/2005.05442).
### Languages
Monolingual. The datasets are in English (EN) and Chinese (ZH)
## Dataset Structure
### Data Instances
An example of dialogue is:
```
{
'dialogue_id': 602,
'dialogue_url': 'https://www.healthtap.com/member/fg?page=/search/covid',
'dialogue_turns': [{'speaker': 'Patient',
'utterance': 'Can coronavirus symptoms be mild for some people versus severe? For example, could it just involve being very fatigued, low grade fever for a few days and not the extreme symptoms? Or is it always a full blown cold and struggle to breathe?Can coronavirus symptoms be mild for some people versus severe? For example, could it just involve being very fatigued, low grade fever for a few days and not the extreme symptoms? Or is it always a full blown cold and struggle to breathe?'},
{'speaker': 'Doctor',
'utterance': 'In brief: Symptoms vary. Some may have no symptoms at all. Some can be life threatening. Would you like to video or text chat with me?'}]
}
```
The dataset is built from [icliniq.com](https://www.icliniq.com/), [healthcaremagic.com](https://www.healthcaremagic.com/), [healthtap.com](https://www.healthtap.com/) and all copyrights of the data belong to these websites. _(for English)_
The dataset is built from [Haodf.com](https://www.haodf.com/) and all copyrights of the data belong to [Haodf.com](https://www.haodf.com/). _(for Chinese)_
### Data Fields
Each consultation consists of the below:
- ID
- URL
- Description of patient’s medical condition
- Dialogue
- Diagnosis and suggestions (Optional, mostly for Chinese)
For generating the QA only the below fields have been considered:
- ID : Consultatation Identifier (restarts for each file)
- URL: The url link of the extracted conversation
- Dialogue : The conversation between the doctor and the patient.
These are arranged as below in the prepared dataset. Each item will be represented with these parameters.
- "file_name": string - signifies the file from which the conversation was extracted
- "dialogue_id": int32 - the dialogue id
- "dialogue_url": string - url of the conversation
- "dialogue_turns": datasets.Sequence - sequence of dialogues between patient and the doctor.Consists ClassLabel(names=["病人", "医生"]), and "utterance"(string) for each turn. (ClassLable(names=["Patient", "Doctor"]) for english)
### Data Splits
There are no data splits on the original data
## 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
@article{ju2020CovidDialog,
title={CovidDialog: Medical Dialogue Datasets about COVID-19},
author={Ju, Zeqian and Chakravorty, Subrato and He, Xuehai and Chen, Shu and Yang, Xingyi and Xie, Pengtao},
journal={ https://github.com/UCSD-AI4H/COVID-Dialogue},
year={2020}
}
### Contributions
Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset. |
curiosity_dialogs | 2023-01-25T14:28:58.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"co... | null | This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like
geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog
acts, grounding to Wikipedia, and user reactions to messages. | @inproceedings{rodriguez2020curiosity,
title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity},
author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang},
year = 2020,
booktitle = {Empirical Methods in Natural Language Processing}
} | null | 6 | 7 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: curiosity
pretty_name: Curiosity Dataset
tags:
- conversational-curiosity
dataset_info:
features:
- name: messages
sequence:
- name: message
dtype: string
- name: liked
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: sender
dtype:
class_label:
names:
'0': user
'1': assistant
- name: facts
sequence:
- name: fid
dtype: int32
- name: used
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: source
dtype:
class_label:
names:
'0': section
'1': known
'2': random
- name: message_id
dtype: string
- name: dialog_acts
sequence: string
- name: known_entities
sequence: string
- name: focus_entity
dtype: string
- name: dialog_id
dtype: int32
- name: inferred_steps
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: created_time
dtype: int64
- name: aspects
sequence: string
- name: first_aspect
dtype: string
- name: second_aspect
dtype: string
- name: shuffle_facts
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: related_entities
sequence: string
- name: tag
dtype: string
- name: user_id
dtype: int32
- name: assistant_id
dtype: int32
- name: is_annotated
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: user_dialog_rating
dtype: int32
- name: user_other_agent_rating
dtype: int32
- name: assistant_dialog_rating
dtype: int32
- name: assistant_other_agent_rating
dtype: int32
- name: reported
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: annotated
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
config_name: curiosity_dialogs
splits:
- name: train
num_bytes: 37198297
num_examples: 10287
- name: val
num_bytes: 4914487
num_examples: 1287
- name: test
num_bytes: 4915613
num_examples: 1287
- name: test_zero
num_bytes: 4333191
num_examples: 1187
download_size: 92169165
dataset_size: 51361588
---
# Dataset Card for Curiosity Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Curiosity Dataset Homepage](https://www.pedro.ai/curiosity)
- **Repository:** [Curiosity Dataset Repository](https://github.com/facebookresearch/curiosity)
- **Paper:** [ACL Anthology](https://www.aclweb.org/anthology/2020.emnlp-main.655/)
- **Point of Contact:** [Pedro Rodriguez](https://mailhide.io/e/wbfjM)
### Dataset Summary
Curiosity dataset consists of 14K English dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages.
### Supported Tasks and Leaderboards
* `text-generation-other-conversational-curiosity`: The dataset can be used to train a model for Conversational Curiosity, which consists in the testing of the hypothesis that engagement increases when users are presented with facts related to what they know. Success on this task is typically measured by achieving a *high* [Accuracy](https://huggingface.co/metrics/accuracy) and [F1 Score](https://huggingface.co/metrics/f1).
### Languages
The text in the dataset is in English collected by crowd-souring. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point consists of dialogs between an user and an assistant, which is followed by the different attributes of the particular dialog.
An example from the Curiosity Dataset train set looks as follows:
```
{'annotated': 1,
'aspects': ['Media', 'Politics and government'],
'assistant_dialog_rating': 5,
'assistant_id': 341,
'assistant_other_agent_rating': 5,
'created_time': 1571783665,
'dialog_id': 21922,
'first_aspect': 'Media',
'focus_entity': 'Namibia',
'inferred_steps': 1,
'is_annotated': 0,
'known_entities': ['South Africa', 'United Kingdom', 'Portugal'],
'messages': {'dialog_acts': [['request_topic'],
['inform_response'],
['request_aspect'],
['inform_response'],
['request_followup'],
['inform_response'],
['request_aspect', 'feedback_positive'],
['inform_response'],
['request_followup'],
['inform_response'],
[],
[]],
'facts': [{'fid': [], 'source': [], 'used': []},
{'fid': [77870, 77676, 77816, 77814, 77775, 77659, 77877, 77785, 77867],
'source': [0, 1, 2, 2, 0, 2, 0, 1, 1],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 0]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77725, 77870, 77676, 77863, 77814, 77775, 77659, 77877, 77867],
'source': [2, 0, 1, 1, 2, 0, 2, 0, 1],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 0]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77694, 77661, 77863, 77780, 77671, 77704, 77869, 77693, 77877],
'source': [1, 2, 1, 0, 2, 2, 0, 1, 0],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 1]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77816, 77814, 77864, 77659, 77877, 77803, 77738, 77784, 77789],
'source': [2, 2, 0, 2, 0, 1, 1, 0, 1],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 0]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77694, 77776, 77780, 77696, 77707, 77693, 77778, 77702, 77743],
'source': [1, 0, 0, 2, 1, 1, 0, 2, 2],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 0]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77662, 77779, 77742, 77734, 77663, 77777, 77702, 77731, 77778],
'source': [1, 0, 2, 1, 2, 0, 2, 1, 0],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 1]}],
'liked': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'message': ['Hi. I want information about Namibia.',
'Nmbia is a country in southern Africa.',
'Do you have information about the media there?',
'A mentional amount of foriegn',
'What about it?',
"Media and journalists in Namibia are represented by the Namibia chapter of the Media Institute of 'southern Africa and the Editors Forum of Namibia.",
'Interesting! What can you tell me about the politics and government?',
'Namibia formed the Namibian Defence Force, comprising former enemies in a 23-year bush war.',
'Do you have more information about it?',
"With a small army and a fragile economy , the Namibian government's principal foreign policy concern is developing strengthened ties within the Southern African region.",
"That's all I wanted to know. Thank you!",
'My pleasure!'],
'message_id': ['617343895',
'2842515356',
'4240816985',
'520711081',
'1292358002',
'3677078227',
'1563061125',
'1089028270',
'1607063839',
'113037558',
'1197873991',
'1399017322'],
'sender': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]},
'related_entities': ['Western Roman Empire',
'United Kingdom',
'Portuguese language',
'Southern African Development Community',
'South Africa',
'Kalahari Desert',
'Namib Desert',
'League of Nations',
'Afrikaans',
'Sub-Saharan Africa',
'Portugal',
'South-West Africa',
'Warmbad, Namibia',
'German language',
'NBC'],
'reported': 0,
'second_aspect': 'Politics and government',
'shuffle_facts': 1,
'tag': 'round_2',
'user_dialog_rating': 5,
'user_id': 207,
'user_other_agent_rating': 5}
```
### Data Fields
* `messages`: List of dialogs between the user and the assistant and their associated attributes
* `dialog_acts`: List of actions performed in the dialogs
* `facts`: List of facts returned by the assistant
* `fid`: Fact ID
* `source`: Source for the fact
* `used`: Whether facts were used before in the same dialog
* `liked`: List of values indicating whether each dialog was liked
* `message`: List of dialogs (messages) between the user and the assistant
* `message_id`: Message ID
* `sender`: Message author ID (numeric)
* `known_entities`: Rooted facts about entities the user knows
* `focus_entity` : Entity in focus in the dialogs
* `dialog_id `: Dialog ID
* `inferred_steps`: Number of inferred steps
* `created_time`: Time of creation of the dialog
* `aspects`: List of two aspects which the dialog is about
* `first_aspect`: First aspect
* `second_aspect`: Second aspect
* `shuffle_facts`: Whether facts were shuffled
* `related_entities` : List of fifteen related entities to the focus entity
* `tag`: Conversation tag
* `user_id`: User ID
* `assistant_id`: Assistant ID
* `is_annotated`: 0 or 1 (More Information Needed)
* `user_dialog_rating`: 1 - 5 (More Information Needed)
* `user_other_agent_rating`: 1 - 5 (More Information Needed)
* `assistant_dialog_rating`: 1 - 5 (More Information Needed)
* `assistant_other_agent_rating`: 1 - 5 (More Information Needed)
* `reported`: Whether the dialog was reported inappropriate
* `annotated`: 0 or 1 (More Information Needed)
### Data Splits
The data is split into a training, validation, test and test_zero set as per the original dataset split.
| | train | validation | test | test_zero |
|-----------------------|------:|-----------:|-----:|----------:|
| Input dialog examples | 10287 | 1287 | 1287 | 1187 |
## 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
[Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
### Citation Information
```
@inproceedings{rodriguez2020curiosity,
title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity},
author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang},
year = 2020,
booktitle = {Empirical Methods in Natural Language Processing}
}
```
### Contributions
Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset. |
fake_news_english | 2023-05-30T04:42:32.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Fake news has become a major societal issue and a technical challenge for social media companies to identify. This content is difficult to identify because the term "fake news" covers intentionally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addressing the problem requires clear definitions and examples. In this work, we present a dataset of fake news and satire stories that are hand coded, verified, and, in the case of fake news, include rebutting stories. We also include a thematic content analysis of the articles, identifying major themes that include hyperbolic support or condemnation of a gure, conspiracy theories, racist themes, and discrediting of reliable sources. In addition to releasing this dataset for research use, we analyze it and show results based on language that are promising for classification purposes. Overall, our contribution of a dataset and initial analysis are designed to support future work by fake news researchers. | @inproceedings{inproceedings,
author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},
year = {2018},
month = {05},
pages = {17-21},
title = {Fake News vs Satire: A Dataset and Analysis},
doi = {10.1145/3201064.3201100}
} | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Fake News English
dataset_info:
features:
- name: article_number
dtype: int32
- name: url_of_article
dtype: string
- name: fake_or_satire
dtype:
class_label:
names:
'0': Satire
'1': Fake
- name: url_of_rebutting_article
dtype: string
splits:
- name: train
num_bytes: 78078
num_examples: 492
download_size: 3002233
dataset_size: 78078
---
# Dataset Card for Fake News English
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://dl.acm.org/doi/10.1145/3201064.3201100**
- **Repository:** https://github.com/jgolbeck/fakenews/
- **Paper:** https://doi.org/10.1145/3201064.3201100
- **Leaderboard:**
- **Point of Contact:** Jennifer Golbeck (http://www.jengolbeck.com)
### Dataset Summary
This dataset contains URLs of news articles classified as either fake or satire. The articles classified as fake also have the URL of a rebutting article.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
```
{
"article_number": 102 ,
"url_of_article": https://newslo.com/roger-stone-blames-obama-possibility-trump-alzheimers-attacks-president-caused-severe-stress/ ,
"fake_or_satire": 1, # Fake
"url_of_rebutting_article": https://www.snopes.com/fact-check/donald-trumps-intelligence-quotient/
}
```
### Data Fields
- article_number: An integer used as an index for each row
- url_of_article: A string which contains URL of an article to be assessed and classified as either Fake or Satire
- fake_or_satire: A classlabel for the above variable which can take two values- Fake (1) and Satire (0)
- url_of_rebutting_article: A string which contains a URL of the article used to refute the article in question (present - in url_of_article)
### Data Splits
This dataset is not split, only the train split is available.
## 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
Golbeck, Jennifer
Everett, Jennine
Falak, Waleed
Gieringer, Carl
Graney, Jack
Hoffman, Kelly
Huth, Lindsay
Ma, Zhenya
Jha, Mayanka
Khan, Misbah
Kori, Varsha
Mauriello, Matthew
Lewis, Elo
Mirano, George
IV, William
Mussenden, Sean
Nelson, Tammie
Mcwillie, Sean
Pant, Akshat
Cheakalos, Paul
### Licensing Information
[More Information Needed]
### Citation Information
@inproceedings{inproceedings,
author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},
year = {2018},
month = {05},
pages = {17-21},
title = {Fake News vs Satire: A Dataset and Analysis},
doi = {10.1145/3201064.3201100}
}
### Contributions
Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
hansards | 2023-04-05T10:07:00.000Z | [
"region:us"
] | null | This release contains 1.3 million pairs of aligned text chunks (sentences or smaller fragments)
from the official records (Hansards) of the 36th Canadian Parliament.
The complete Hansards of the debates in the House and Senate of the 36th Canadian Parliament,
as far as available, were aligned. The corpus was then split into 5 sets of sentence pairs:
training (80% of the sentence pairs), two sets of sentence pairs for testing (5% each), and
two sets of sentence pairs for final evaluation (5% each). The current release consists of the
training and testing sets. The evaluation sets are reserved for future MT evaluation purposes
and currently not available.
Caveats
1. This release contains only sentence pairs. Even though the order of the sentences is the same
as in the original, there may be gaps resulting from many-to-one, many-to-many, or one-to-many
alignments that were filtered out. Therefore, this release may not be suitable for
discourse-related research.
2. Neither the sentence splitting nor the alignments are perfect. In particular, watch out for
pairs that differ considerably in length. You may want to filter these out before you do
any statistical training.
The alignment of the Hansards was performed as part of the ReWrite project under funding
from the DARPA TIDES program. | null | 0 | 7 | ---
paperswithcode_id: null
pretty_name: hansards
dataset_info:
- config_name: senate
features:
- name: fr
dtype: string
- name: en
dtype: string
splits:
- name: test
num_bytes: 5711686
num_examples: 25553
- name: train
num_bytes: 40324278
num_examples: 182135
download_size: 15247360
dataset_size: 46035964
- config_name: house
features:
- name: fr
dtype: string
- name: en
dtype: string
splits:
- name: test
num_bytes: 22906629
num_examples: 122290
- name: train
num_bytes: 191459584
num_examples: 947969
download_size: 67584000
dataset_size: 214366213
---
# Dataset Card for "hansards"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.isi.edu/natural-language/download/hansard/](https://www.isi.edu/natural-language/download/hansard/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 82.83 MB
- **Size of the generated dataset:** 260.40 MB
- **Total amount of disk used:** 343.23 MB
### Dataset Summary
This release contains 1.3 million pairs of aligned text chunks (sentences or smaller fragments)
from the official records (Hansards) of the 36th Canadian Parliament.
The complete Hansards of the debates in the House and Senate of the 36th Canadian Parliament,
as far as available, were aligned. The corpus was then split into 5 sets of sentence pairs:
training (80% of the sentence pairs), two sets of sentence pairs for testing (5% each), and
two sets of sentence pairs for final evaluation (5% each). The current release consists of the
training and testing sets. The evaluation sets are reserved for future MT evaluation purposes
and currently not available.
Caveats
1. This release contains only sentence pairs. Even though the order of the sentences is the same
as in the original, there may be gaps resulting from many-to-one, many-to-many, or one-to-many
alignments that were filtered out. Therefore, this release may not be suitable for
discourse-related research.
2. Neither the sentence splitting nor the alignments are perfect. In particular, watch out for
pairs that differ considerably in length. You may want to filter these out before you do
any statistical training.
The alignment of the Hansards was performed as part of the ReWrite project under funding
from the DARPA TIDES program.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### house
- **Size of downloaded dataset files:** 67.58 MB
- **Size of the generated dataset:** 214.37 MB
- **Total amount of disk used:** 281.95 MB
An example of 'train' looks as follows.
```
{
"en": "Mr. Walt Lastewka (Parliamentary Secretary to Minister of Industry, Lib.):",
"fr": "M. Walt Lastewka (secrétaire parlementaire du ministre de l'Industrie, Lib.):"
}
```
#### senate
- **Size of downloaded dataset files:** 15.25 MB
- **Size of the generated dataset:** 46.03 MB
- **Total amount of disk used:** 61.28 MB
An example of 'train' looks as follows.
```
{
"en": "Mr. Walt Lastewka (Parliamentary Secretary to Minister of Industry, Lib.):",
"fr": "M. Walt Lastewka (secrétaire parlementaire du ministre de l'Industrie, Lib.):"
}
```
### Data Fields
The data fields are the same among all splits.
#### house
- `fr`: a `string` feature.
- `en`: a `string` feature.
#### senate
- `fr`: a `string` feature.
- `en`: a `string` feature.
### Data Splits
| name |train | test |
|------|-----:|-----:|
|house |947969|122290|
|senate|182135| 25553|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | |
kor_sae | 2023-01-25T14:34:03.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"arxiv:1912.00342"... | null | This new dataset is designed to extract intent from non-canonical directives which will help dialog managers
extract intent from user dialog that may have no clear objective or are paraphrased forms of utterances. | @article{cho2019machines,
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1912.00342},
year={2019}
} | null | 2 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
pretty_name: Structured Argument Extraction for Korean
dataset_info:
features:
- name: intent_pair1
dtype: string
- name: intent_pair2
dtype: string
- name: label
dtype:
class_label:
names:
'0': yes/no
'1': alternative
'2': wh- questions
'3': prohibitions
'4': requirements
'5': strong requirements
splits:
- name: train
num_bytes: 2885167
num_examples: 30837
download_size: 2545926
dataset_size: 2885167
---
# Dataset Card for Structured Argument Extraction for Korean
## 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:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
- **Repository:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
- **Paper:** [Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives](https://arxiv.org/abs/1912.00342)
- **Point of Contact:** [Won Ik Cho](wicho@hi.snu.ac.kr)
### Dataset Summary
The Structured Argument Extraction for Korean dataset is a set of question-argument and command-argument pairs with their respective question type label and negativeness label. Often times, agents like Alexa or Siri, encounter conversations without a clear objective from the user. The goal of this dataset is to extract the intent argument of a given utterance pair without a clear directive. This may yield a more robust agent capable of parsing more non-canonical forms of speech.
### Supported Tasks and Leaderboards
* `intent_classification`: The dataset can be trained with a Transformer like [BERT](https://huggingface.co/bert-base-uncased) to classify the intent argument or a question/command pair in Korean, and it's performance can be measured by it's BERTScore.
### Languages
The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
## Dataset Structure
### Data Instances
An example data instance contains a question or command pair and its label:
```
{
"intent_pair1": "내일 오후 다섯시 조별과제 일정 추가해줘"
"intent_pair2": "내일 오후 다섯시 조별과제 일정 추가하기"
"label": 4
}
```
### Data Fields
* `intent_pair1`: a question/command pair
* `intent_pair2`: a corresponding question/command pair
* `label`: determines the intent argument of the pair and can be one of `yes/no` (0), `alternative` (1), `wh- questions` (2), `prohibitions` (3), `requirements` (4) and `strong requirements` (5)
### Data Splits
The corpus contains 30,837 examples.
## Dataset Creation
### Curation Rationale
The Structured Argument Extraction for Korean dataset was curated to help train models extract intent arguments from utterances without a clear objective or when the user uses non-canonical forms of speech. This is especially helpful in Korean because in English, the `Who, what, where, when and why` usually comes in the beginning, but this isn't necessarily the case in the Korean language. So for low-resource languages, this lack of data can be a bottleneck for comprehension performance.
### Source Data
#### Initial Data Collection and Normalization
The corpus was taken from the one constructed by [Cho et al.](https://arxiv.org/abs/1811.04231), a Korean single utterance corpus for identifying directives/non-directives that contains a wide variety of non-canonical directives.
#### Who are the source language producers?
Korean speakers are the source language producers.
### Annotations
#### Annotation process
Utterances were categorized as question or command arguments and then further classified according to their intent argument.
#### Who are the annotators?
The annotation was done by three Korean natives with a background in computational linguistics.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset is curated by Won Ik Cho, Young Ki Moon, Sangwhan Moon, Seok Min Kim and Nam Soo Kim.
### Licensing Information
The dataset is licensed under the CC BY-SA-4.0.
### Citation Information
```
@article{cho2019machines,
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1912.00342},
year={2019}
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. |
opus_finlex | 2022-11-03T16:08:11.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fi",
"language:sv",
"license:unknown",
"region:us"
] | null | The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files. | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | null | 1 | 7 | ---
annotations_creators:
- found
language_creators:
- found
language:
- fi
- sv
license:
- unknown
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusFinlex
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- fi
- sv
config_name: fi-sv
splits:
- name: train
num_bytes: 610550215
num_examples: 3114141
download_size: 153886554
dataset_size: 610550215
---
# Dataset Card for [opus_finlex]
## 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:**[Finlex](http://opus.nlpl.eu/Finlex.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files.
### Supported Tasks and Leaderboards
The underlying task is machine translation for language pair Swedish and Finnish.
### Languages
Swedish and Finnish
## 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
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. |
opus_paracrawl | 2023-06-01T14:59:53.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:bg",
"language:ca",
"language:cs",
"language:da",... | null | Parallel corpora from Web Crawls collected in the ParaCrawl project.
42 languages, 43 bitexts
total number of files: 59,996
total number of tokens: 56.11G
total number of sentence fragments: 3.13G | null | null | 5 | 7 | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- is
- it
- km
- ko
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusParaCrawl
dataset_info:
- config_name: el-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- el
- en
splits:
- name: train
num_bytes: 6760375061
num_examples: 21402471
download_size: 2317102846
dataset_size: 6760375061
- config_name: en-ha
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- ha
splits:
- name: train
num_bytes: 4618460
num_examples: 19694
download_size: 1757433
dataset_size: 4618460
- config_name: en-ig
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- ig
splits:
- name: train
num_bytes: 6709030
num_examples: 28829
download_size: 2691716
dataset_size: 6709030
- config_name: en-km
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- km
splits:
- name: train
num_bytes: 31964493
num_examples: 65115
download_size: 9907279
dataset_size: 31964493
- config_name: en-so
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- so
splits:
- name: train
num_bytes: 5791003
num_examples: 14880
download_size: 2227727
dataset_size: 5791003
- config_name: de-pl
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- de
- pl
splits:
- name: train
num_bytes: 298637031
num_examples: 916643
download_size: 106891602
dataset_size: 298637031
- config_name: fr-nl
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- fr
- nl
splits:
- name: train
num_bytes: 862303220
num_examples: 2687673
download_size: 319804705
dataset_size: 862303220
- config_name: en-sw
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- sw
splits:
- name: train
num_bytes: 44264442
num_examples: 132520
download_size: 18611087
dataset_size: 44264442
- config_name: en-tl
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- tl
splits:
- name: train
num_bytes: 82502798
num_examples: 248689
download_size: 32933118
dataset_size: 82502798
- config_name: es-gl
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- es
- gl
splits:
- name: train
num_bytes: 582660901
num_examples: 1879689
download_size: 236696353
dataset_size: 582660901
config_names:
- de-pl
- el-en
- en-ha
- en-ig
- en-km
- en-so
- en-sw
- en-tl
- es-gl
- fr-nl
---
# Dataset Card for OpusParaCrawl
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/ParaCrawl.php
- **Repository:** None
- **Paper:** [ParaCrawl: Web-Scale Acquisition of Parallel Corpora](https://aclanthology.org/2020.acl-main.417/)
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Parallel corpora from Web Crawls collected in the ParaCrawl project.
Tha dataset contains:
- 42 languages, 43 bitexts
- total number of files: 59,996
- total number of tokens: 56.11G
- total number of sentence fragments: 3.13G
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs,
e.g.
```python
dataset = load_dataset("opus_paracrawl", lang1="en", lang2="so")
```
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/ParaCrawl.php
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- is
- it
- km
- ko
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
"el": "Συνεχίστε ευθεία 300 μέτρα μέχρι να καταλήξουμε σε μια σωστή οδός (ul. Gagarina)? Περπατήστε περίπου 300 μέτρα μέχρι να φτάσετε το πρώτο ορθή οδός (ul Khotsa Namsaraeva)?",
"en": "Go straight 300 meters until you come to a proper street (ul. Gagarina); Walk approximately 300 meters until you reach the first proper street (ul Khotsa Namsaraeva);"
}
}
```
### Data Fields
- `id` (`str`): Unique identifier of the parallel sentence for the pair of languages.
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single `train` split.
## 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
- Creative commons CC0 (no rights reserved)
### Citation Information
```bibtex
@inproceedings{banon-etal-2020-paracrawl,
title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora",
author = "Ba{\~n}{\'o}n, Marta and
Chen, Pinzhen and
Haddow, Barry and
Heafield, Kenneth and
Hoang, Hieu and
Espl{\`a}-Gomis, Miquel and
Forcada, Mikel L. and
Kamran, Amir and
Kirefu, Faheem and
Koehn, Philipp and
Ortiz Rojas, Sergio and
Pla Sempere, Leopoldo and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Sarr{\'\i}as, Elsa and
Strelec, Marek and
Thompson, Brian and
Waites, William and
Wiggins, Dion and
Zaragoza, Jaume",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.417",
doi = "10.18653/v1/2020.acl-main.417",
pages = "4555--4567",
}
```
```bibtex
@InProceedings{TIEDEMANN12.463,
author = {Jörg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. |
etalab-ia/piaf | 2022-11-03T16:31:15.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:mit",
"region:us"
] | etalab-ia | Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia. | @InProceedings{keraron-EtAl:2020:LREC,
author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, Frédéric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo},
title = {Project PIAF: Building a Native French Question-Answering Dataset},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
month = {May},
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {5483--5492},
abstract = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},
url = {https://www.aclweb.org/anthology/2020.lrec-1.673}
} | null | 6 | 7 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- fr
language_bcp47:
- fr-FR
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: Piaf
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
config_name: plain_text
splits:
- name: train
num_bytes: 3332905
num_examples: 3835
download_size: 1370384
dataset_size: 3332905
---
# Dataset Card for Piaf
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://piaf.etalab.studio](https://piaf.etalab.studio)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.31 MB
- **Size of the generated dataset:** 3.18 MB
- **Total amount of disk used:** 4.49 MB
### Dataset Summary
Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 1.31 MB
- **Size of the generated dataset:** 3.18 MB
- **Total amount of disk used:** 4.49 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [0],
"text": ["Voici"]
},
"context": "Voici le contexte du premier paragraphe du deuxième article.",
"id": "p140295460356960",
"question": "Suis-je la troisième question ?",
"title": "Jakob Böhme"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train |
|------------|------:|
| plain_text | 3835 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{keraron-EtAl:2020:LREC,
author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, Frédéric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo},
title = {Project PIAF: Building a Native French Question-Answering Dataset},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
month = {May},
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {5483--5492},
abstract = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},
url = {https://www.aclweb.org/anthology/2020.lrec-1.673}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@RachelKer](https://github.com/RachelKer) for adding this dataset. |
proto_qa | 2022-11-03T16:31:01.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
... | null | This dataset is for studying computational models trained to reason about prototypical situations. Using deterministic filtering a sampling from a larger set of all transcriptions was built. It contains 9789 instances where each instance represents a survey question from Family Feud game. Each instance exactly is a question, a set of answers, and a count associated with each answer.
Each line is a json dictionary, in which:
1. question - contains the question (in original and a normalized form)
2. answerstrings - contains the original answers provided by survey respondents (when available), along with the counts for each string. Because the FamilyFeud data has only cluster names rather than strings, those cluster names are included with 0 weight.
3. answer-clusters - lists clusters, with the count of each cluster and the strings included in that cluster. Each cluster is given a unique ID that can be linked to in the assessment files. | @InProceedings{huggingface:dataset,
title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning},
authors={Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum},
year={2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/iesl/protoqa-data}},
} | null | 1 | 7 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: protoqa
pretty_name: ProtoQA
dataset_info:
- config_name: proto_qa
features:
- name: normalized-question
dtype: string
- name: question
dtype: string
- name: answer-clusters
sequence:
- name: count
dtype: int32
- name: clusterid
dtype: string
- name: answers
sequence: string
- name: answerstrings
sequence: string
- name: totalcount
dtype: int32
- name: id
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3943484
num_examples: 8782
- name: validation
num_bytes: 472121
num_examples: 980
download_size: 7352932
dataset_size: 4415605
- config_name: proto_qa_cs
features:
- name: normalized-question
dtype: string
- name: question
dtype: string
- name: answers-cleaned
sequence:
- name: count
dtype: int32
- name: clusterid
dtype: string
- name: answers
sequence: string
- name: answerstrings
sequence: string
- name: totalcount
dtype: int32
- name: id
dtype: string
- name: source
dtype: string
splits:
- name: validation
num_bytes: 84466
num_examples: 52
download_size: 115704
dataset_size: 84466
- config_name: proto_qa_cs_assessments
features:
- name: question
dtype: string
- name: assessments
sequence: string
splits:
- name: validation
num_bytes: 12473
num_examples: 52
download_size: 24755
dataset_size: 12473
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Interactive Demo:** [Interactive demo](http://protoqa.com)
- **Repository:** [proto_qa repository](https://github.com/iesl/protoqa-data)
- **Paper:** [proto_qa paper](https://arxiv.org/pdf/2005.00771.pdf)
- **Point of Contact:** [Michael Boratko](mailto:mboratko@cs.umass.edu)
[Xiang Lorraine Li](mailto:xiangl@cs.umass.edu)
[Tim O’Gorman](mailto:togorman@cs.umass.edu)
[Rajarshi Das](mailto:rajarshi@cs.umass.edu)
[Dan Le](mailto:dhle@cs.umass.edu)
[Andrew McCallum](mailto:mccallum@cs.umass.edu)
### Dataset Summary
This dataset is for studying computational models trained to reason about prototypical situations. It is anticipated that still would not lead to usage in a downstream task, but as a way of studying the knowledge (and biases) of prototypical situations already contained in pre-trained models. The data it is partially based on (Family Feud).
Using deterministic filtering a sampling from a larger set of all transcriptions was built. Scraped data was acquired through fan transcriptions at [family feud](https://www.familyfeudinfo.com) and [family feud friends](http://familyfeudfriends.arjdesigns.com/); crowdsourced data was acquired with FigureEight (now Appen)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English
## Dataset Structure
### Data Instances
**What do the instances that comprise the dataset represent?**<br>
Each represents a survey question from Family Feud game and reported answer clusters
**How many instances are there in total?**<br>
9789 instances
**What data does each instance consist of?**<br>
Each instance is a question, a set of answers, and a count associated with each answer.
### Data Fields
**Data Files**<br>
Each line is a json dictionary, in which:<br>
**question** contains the question (in original and a normalized form)<br>
**answerstrings** contains the original answers provided by survey respondents (when available), along with the counts for each string. Because the FamilyFeud data has only cluster names rather than strings, those cluster names are included with 0 weight.<br>
**answer-clusters** list of clusters, with the count of each cluster and the strings included in that cluster. Each cluster is given a unique ID that can be linked to in the assessment files.
The simplified configuration includes:
- `question`: contains the original question
- `normalized-question`: contains the question in normalized form
- `totalcount`: unique identifier of the comment (can be used to look up the entry in the raw dataset)
- `id`: unique identifier of the commen
- `source`: unique identifier of the commen
- `answerstrings`: unique identifier of the commen
- `answer-clusters | answers-cleaned`: list clusters of:
* `clusterid`: Each cluster is given a unique ID that can be linked to in the assessment files
* `count`: the count of each cluster
* `answers`: the strings included in that cluster
In addition to the above, there is crowdsourced assessments file. The config "proto_qa_cs_assessments" provides mappings from additional human and model answers to clusters, to evaluate different assessment methods.
**Assessment files**<br>
The file **data/dev/crowdsource_dev.assessments.jsonl** contains mappings from additional human and model answers to clusters, to evaluate different assessment methods.
Each line contains:<br>
* `question`: contains the ID of the question
* `assessments`: maps individual strings to one of three options, either the answer cluster id, "invalid" if the answer is judged to be bad, or "valid_new_cluster" if the answer is valid but does not match any existing clusters.
### Data Splits
* proto_qa `Train` : 8781 instances for training or fine-tuning scraped from Family Feud fan sites (see paper). Scraped data has answer clusters with sizes, but only has a single string per cluster (corresponding to the original cluster name
* proto_qa `Validation` : 979 instances sampled from the same Family Feud data, for use in model validation and development.
* proto_qa_cs `Validation` :: 51 questions collected with exhaustive answer collection and manual clustering, matching the details of the eval test set (roughly 100 human answers per question)
**data/dev/crowdsource_dev.assessments.jsonl**: assessment file (format described above) for study of assessment methods.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
**How was the data associated with each instance acquired?**<br>
Scraped data was acquired through fan transcriptions at https://www.familyfeudinfo.com and http://familyfeudfriends.arjdesigns.com/ ; crowdsourced data was acquired with FigureEight (now Appen)
**If the dataset is a sample from a larger set, what was the sampling strategy?**<br>
Deterministic filtering was used (noted elsewhere), but no probabilistic sampling was used.
**Who was involved in the data collection process (e.g., students,crowdworkers , contractors) and how were they compensated?**<br>
Crowdworkers were used in the evalaution dataset. Time per task was calculated and per-task cost was set to attempt to provide a living wage
**Over what timeframe was the data collected?**<br>
Crowdsource answers were collected between Fall of 2018 and Spring of 2019. Scraped data covers question-answer pairs collected since the origin of the show in 1976
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
**Was any preprocessing/cleaning/labeling of the data done?**<br>
Obvious typos in the crowdsourced answer set were corrected
#### Who are the annotators?
The original question-answer pairs were generated by surveys of US English-speakers in a period from 1976 to present day. Crowd-sourced evaluation was constrained geographically to US English speakers but not otherwise constrained. Additional demographic data was not collected.
### Personal and Sensitive Information
**Does the dataset contain data that might be considered sensitive in any way?**<br>
As the questions address prototypical/stereotypical activities, models trained on more offensive material (such as large language models) may provide offensive answers to such questions. While we had found a few questions which we worried would actually encourage models to provide offensive answers, we cannot guarantee that the data is clean of such questions. Even a perfectly innocent version of this dataset would be encouraging models to express generalizations about situations, and therefore may provoke offensive material that is oontained in language models
**Does the dataset contain data that might be considered confidential?**<br>
The data does not concern individuals and thus does not contain any information to identify persons. Crowdsourced answers do not provide any user identifiers.
## Considerations for Using the Data
### Social Impact of Dataset
**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**<br>
Not egregiously so (questions are all designed to be shown on television or replications thereof),
### Discussion of Biases
**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?**
<br>All original questions were written with US television audiences in mind, and therefore characterize prototypical situations with a specific lens. Any usages which deploy this to actually model prototypical situations globally will carry that bias.
**Are there tasks for which the dataset should not be used?**
<br>We caution regarding free-form use of this dataset for interactive "commonsense question answering" purposes without more study of the biases and stereotypes learned by such models.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The listed authors are maintaining/supporting the dataset. They pledge to help support issues, but cannot guarantee long-term support
### Licensing Information
The Proto_qa dataset is licensed under the [Creative Commons Attribution 4.0 International](https://github.com/iesl/protoqa-data/blob/master/LICENSE)
### Citation Information
```
@InProceedings{
huggingface:dataset,
title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning},
authors = {Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {https://github.com/iesl/protoqa-data},
}
```
### Contributions
Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset. |
qanta | 2023-04-05T13:37:09.000Z | [
"task_categories:question-answering",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"quizbowl",
"arxiv:1904.04792",
"region:us"
] | null | The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. | @article{Rodriguez2019QuizbowlTC,
title={Quizbowl: The Case for Incremental Question Answering},
author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
journal={ArXiv},
year={2019},
volume={abs/1904.04792}
} | null | 3 | 7 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: Quizbowl
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: quizbowl
tags:
- quizbowl
dataset_info:
features:
- name: id
dtype: string
- name: qanta_id
dtype: int32
- name: proto_id
dtype: string
- name: qdb_id
dtype: int32
- name: dataset
dtype: string
- name: text
dtype: string
- name: full_question
dtype: string
- name: first_sentence
dtype: string
- name: char_idx
dtype: int32
- name: sentence_idx
dtype: int32
- name: tokenizations
sequence:
sequence: int32
length: 2
- name: answer
dtype: string
- name: page
dtype: string
- name: raw_answer
dtype: string
- name: fold
dtype: string
- name: gameplay
dtype: bool
- name: category
dtype: string
- name: subcategory
dtype: string
- name: tournament
dtype: string
- name: difficulty
dtype: string
- name: year
dtype: int32
config_name: mode=first,char_skip=25
splits:
- name: adversarial
num_bytes: 1258844
num_examples: 1145
- name: buzzdev
num_bytes: 1553636
num_examples: 1161
- name: buzztest
num_bytes: 2653425
num_examples: 1953
- name: buzztrain
num_bytes: 19699736
num_examples: 16706
- name: guessdev
num_bytes: 1414882
num_examples: 1055
- name: guesstest
num_bytes: 2997123
num_examples: 2151
- name: guesstrain
num_bytes: 117599750
num_examples: 96221
download_size: 170754918
dataset_size: 147177396
---
# Dataset Card for "qanta"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://www.qanta.org/](http://www.qanta.org/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792)
- **Point of Contact:** [Jordan Boyd-Graber](mailto:jbg@umiacs.umd.edu)
- **Size of downloaded dataset files:** 170.75 MB
- **Size of the generated dataset:** 147.18 MB
- **Total amount of disk used:** 317.93 MB
### Dataset Summary
The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### mode=first,char_skip=25
- **Size of downloaded dataset files:** 170.75 MB
- **Size of the generated dataset:** 147.18 MB
- **Total amount of disk used:** 317.93 MB
An example of 'guessdev' looks as follows.
```
This example was too long and was cropped:
{
"answer": "Apollo_program",
"category": "History",
"char_idx": -1,
"dataset": "quizdb.org",
"difficulty": "easy_college",
"first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"",
"fold": "guessdev",
"full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...",
"gameplay": false,
"id": "127028-first",
"page": "Apollo_program",
"proto_id": "",
"qanta_id": 127028,
"qdb_id": 126689,
"raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]",
"sentence_idx": -1,
"subcategory": "American",
"text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"",
"tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]],
"tournament": "ACF Fall",
"year": 2016
}
```
### Data Fields
The data fields are the same among all splits.
#### mode=first,char_skip=25
- `id`: a `string` feature.
- `qanta_id`: a `int32` feature.
- `proto_id`: a `string` feature.
- `qdb_id`: a `int32` feature.
- `dataset`: a `string` feature.
- `text`: a `string` feature.
- `full_question`: a `string` feature.
- `first_sentence`: a `string` feature.
- `char_idx`: a `int32` feature.
- `sentence_idx`: a `int32` feature.
- `tokenizations`: a dictionary feature containing:
- `feature`: a `int32` feature.
- `answer`: a `string` feature.
- `page`: a `string` feature.
- `raw_answer`: a `string` feature.
- `fold`: a `string` feature.
- `gameplay`: a `bool` feature.
- `category`: a `string` feature.
- `subcategory`: a `string` feature.
- `tournament`: a `string` feature.
- `difficulty`: a `string` feature.
- `year`: a `int32` feature.
### Data Splits
| name |adversarial|buzzdev|buzztrain|guessdev|guesstrain|buzztest|guesstest|
|-----------------------|----------:|------:|--------:|-------:|---------:|-------:|--------:|
|mode=first,char_skip=25| 1145| 1161| 16706| 1055| 96221| 1953| 2151|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Rodriguez2019QuizbowlTC,
title={Quizbowl: The Case for Incremental Question Answering},
author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
journal={ArXiv},
year={2019},
volume={abs/1904.04792}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. |
tamilmixsentiment | 2023-06-16T13:07:45.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ta",
"license:unknown",
"regio... | null | The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. Train: 11,335 Validation: 1,260 and Test: 3,149. This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon. The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between. | @inproceedings{chakravarthi-etal-2020-corpus,
title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text",
author = "Chakravarthi, Bharathi Raja and
Muralidaran, Vigneshwaran and
Priyadharshini, Ruba and
McCrae, John Philip",
booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources association",
url = "https://www.aclweb.org/anthology/2020.sltu-1.28",
pages = "202--210",
abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.",
language = "English",
ISBN = "979-10-95546-35-1",
} | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- ta
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Tamilmixsentiment
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Positive
'1': Negative
'2': Mixed_feelings
'3': unknown_state
'4': not-Tamil
splits:
- name: train
num_bytes: 790132
num_examples: 11335
- name: validation
num_bytes: 89618
num_examples: 1260
- name: test
num_bytes: 218764
num_examples: 3149
download_size: 1150792
dataset_size: 1098514
---
# Dataset Card for Tamilmixsentiment
## 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:** [Tamilmixsentiment Homepage](https://dravidian-codemix.github.io/2020/index.html)
- **Repository:** [Tamilmixsentiment repository](https://dravidian-codemix.github.io/2020/datasets.html)
- **Paper:** [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://www.aclweb.org/anthology/2020.sltu-1.28/)
- **Leaderboard:** [Rank list](https://drive.google.com/file/d/1Mf8-No-63koGRwdF13RrO01NAFBlNmI0/view?usp=sharing)
- **Point of Contact:** [Bharathi Raja Chakravarthi](mailto:bharathiraja.akr@gmail.com)
### Dataset Summary
The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated with sentiment polarity at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.
### Supported Tasks and Leaderboards
To identify sentiment polarity of the code-mixed dataset of comments/posts in Tamil-English collected from social media.
### Languages
Tamil-English code-switched. The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between.
## Dataset Structure
### Data Instances
An example from the Tamilmixsentiment train set looks as follows:
```
text label
Trailer late ah parthavanga like podunga Positive
```
### Data Fields
- `text`: Tamil-English code-mixed comment.
- `label`: list of the possible sentiments "Positive", "Negative", "Mixed_feelings", "unknown_state", "not-Tamil"
### Data Splits
The entire dataset of 15,744 sentences was randomly shuffled and split into three parts as follows:
| | train | validation | test |
|------------------------------|------:|-----------:|-----:|
| Tamilmixsentiment | 11335 | 1260 | 3149 |
## Dataset Creation
### Curation Rationale
Sentiment analysis has become important in social media research (Yang and Eisenstein, 2017). Until recently these applications were created for high-resourced languages which analysed monolingual utterances. But social media in multilingual communities contains more code-mixed text. Code-mixing is common among speakers in a bilingual speech community. As English is seen as the language of prestige and education, the influence of lexicon, connectives and phrases from English language is common in spoken Tamil. Tamil has little annotated data for code-mixed scenarios. An annotated corpus developed for monolingual data cannot deal with code-mixed usage and therefore it fails to yield good results due to mixture of languages at different levels of linguistic analysis. Therefore this dataset of code-mixed Tamil-English sentiment annotated corpus is created.
### Source Data
#### Initial Data Collection and Normalization
The data was scraped from Youtube. In total 184,573 sentences for Tamil from YouTube comments from the trailers of a movies released in 2019. Many of the them contained sentences
that were either entirely written in English or code-mixed Tamil-English or fully written in Tamil. So we filtered out a non-code-mixed corpus based on language identification
at comment level using the langdetect library. The comment is written fully in Tamil or English, we discarded that comment since monolingual resources are available for these languages. We also identified if the sentences were written in other languages such as Hindi, Malayalam, Urdu, Telugu, and Kannada. We preprocessed the comments by removing the emoticons and applying a sentence
length filter. We want to create a code-mixed corpus of reasonable size with sentences that have fairly defined sentiments which will be useful for future research. Thus our filter removed sentences with less than five words and more than 15 words after cleaning the data. In the end we got 15,744 Tanglish sentences.
#### Who are the source language producers?
Youtube users
### Annotations
#### Annotation process
Three steps complete the annotation setup. First, each sentence was annotated by two people. In the second step, the data were collected if both of them agreed. In the case of conflict, a third person annotated the sentence. In the third step, if all the three of them did not agree, then two more annotators annotated the sentences.
#### Who are the annotators?
Eleven volunteers were involved in the process. All of them were native speakers of Tamil with diversity in gender, educational level and medium of instruction in their school education.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{chakravarthi-etal-2020-corpus,
title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text",
author = "Chakravarthi, Bharathi Raja and
Muralidaran, Vigneshwaran and
Priyadharshini, Ruba and
McCrae, John Philip",
booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources association",
url = "https://www.aclweb.org/anthology/2020.sltu-1.28",
pages = "202--210",
abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.",
language = "English",
ISBN = "979-10-95546-35-1",
}
```
### Contributions
Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset. |
turkish_shrinked_ner | 2023-01-25T14:54:44.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-turkish_ner",
"language:tr",
"license:cc-by-4.0",
... | null | Shrinked version (48 entity type) of the turkish_ner.
Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle | \ | null | 1 | 7 | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- tr
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-turkish_ner
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TurkishShrinkedNer
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academic
'2': I-academic
'3': B-academic_person
'4': I-academic_person
'5': B-aircraft
'6': I-aircraft
'7': B-album_person
'8': I-album_person
'9': B-anatomy
'10': I-anatomy
'11': B-animal
'12': I-animal
'13': B-architect_person
'14': I-architect_person
'15': B-capital
'16': I-capital
'17': B-chemical
'18': I-chemical
'19': B-clothes
'20': I-clothes
'21': B-country
'22': I-country
'23': B-culture
'24': I-culture
'25': B-currency
'26': I-currency
'27': B-date
'28': I-date
'29': B-food
'30': I-food
'31': B-genre
'32': I-genre
'33': B-government
'34': I-government
'35': B-government_person
'36': I-government_person
'37': B-language
'38': I-language
'39': B-location
'40': I-location
'41': B-material
'42': I-material
'43': B-measure
'44': I-measure
'45': B-medical
'46': I-medical
'47': B-military
'48': I-military
'49': B-military_person
'50': I-military_person
'51': B-nation
'52': I-nation
'53': B-newspaper
'54': I-newspaper
'55': B-organization
'56': I-organization
'57': B-organization_person
'58': I-organization_person
'59': B-person
'60': I-person
'61': B-production_art_music
'62': I-production_art_music
'63': B-production_art_music_person
'64': I-production_art_music_person
'65': B-quantity
'66': I-quantity
'67': B-religion
'68': I-religion
'69': B-science
'70': I-science
'71': B-shape
'72': I-shape
'73': B-ship
'74': I-ship
'75': B-software
'76': I-software
'77': B-space
'78': I-space
'79': B-space_person
'80': I-space_person
'81': B-sport
'82': I-sport
'83': B-sport_name
'84': I-sport_name
'85': B-sport_person
'86': I-sport_person
'87': B-structure
'88': I-structure
'89': B-subject
'90': I-subject
'91': B-tech
'92': I-tech
'93': B-train
'94': I-train
'95': B-vehicle
'96': I-vehicle
splits:
- name: train
num_bytes: 200728389
num_examples: 614515
download_size: 0
dataset_size: 200728389
---
# Dataset Card for turkish_shrinked_ner
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** https://www.kaggle.com/behcetsenturk
### Dataset Summary
Shrinked processed version (48 entity type) of the turkish_ner.
Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Turkish
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
There's only the training set.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Behcet Senturk
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
[Needs More Information]
### Contributions
Thanks to [@bhctsntrk](https://github.com/bhctsntrk) for adding this dataset. |
ARKseal/YFCC14M_subset_webdataset | 2021-11-27T22:47:47.000Z | [
"region:us"
] | ARKseal | null | null | null | 0 | 7 | Entry not found |
Atsushi/fungi_indexed_mycological_papers_japanese | 2023-10-08T21:33:33.000Z | [
"annotations_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ja",
"license:cc-by-4.0",
"region:us"
] | Atsushi | null | null | null | 0 | 7 | ---
annotations_creators:
- other
language:
- ja
license:
- cc-by-4.0
multilinguality:
- monolingual
source_datasets:
- original
size_categories:
- 1K<n<10K
---
fungi_indexed_mycological_papers_japanese
大菌輪「論文3行まとめ」データセット
最終更新日：2023/10/9（R3-11041まで）
====
### Languages
Japanese
This dataset is available in Japanese only.
# 概要
Atsushi Nakajima（中島淳志）が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け（インデキシング）した情報を提供しています。
本データセットは、「論文3行まとめ」のコンテンツに含まれる各論文の3行抄録、タグ（索引）、掲載種一覧、比較種一覧をまとめたものです。
「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。
また、本データセットを可視化したWebアプリを[Observableで公開](https://tinyurl.com/2tvryz8u)しています。
## 関連データセット
「識別形質まとめ」
[Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese)
「Trait Circusデータセット」（統制形質）
[Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database)
## 各カラムの説明
* R3ID … 大菌輪「論文3行まとめ」のIDです。
* ja_title_provisional_translate（仮訳和文題名） … 作成者が翻訳したタイトルです。一部、日本語の原題があるものはそれをそのまま使用しています。
* original_title（原文題名）
* published_year（出版年）
* journal_title（雑誌名）
* source（文献リンク） … 各情報の 出典（文献）のURLです。
* daikinrin_url … 大菌輪「論文3行まとめ」のURLです。
* tags … 作成者が論文を全文読んだ上で独自に付与した索引です。カンマ+半角空白区切りです。形態形質、宿主/基質、実験器具/実験手法/試薬、地理的分布、生理/生化学などを幅広く索引しています。
* R3summary_1 … 3行抄録の「1行目」です。
* R3summary_2 … 3行抄録の「2行目」です。
* R3summary_3 … 3行抄録の「3行目」です。
* species_reported（報告種一覧） … 当該論文内で掲載された種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。記号の意味は以下の通りです。
* ★＝新種（新亜種・新品種・新変種）
* ■＝ 新産種
* ▲＝新組み合わせ
* ◆＝新学名
* ●＝新階級
* （無印）＝その他
* species_compared（比較種一覧） … いずれかの報告種と論文中で何らかの比較がなされた種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。詳細は「識別形質まとめ」データセット（[Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese)）を参照してください。
* taxon_reported（分類群一覧） … 報告種に対応する上位分類群をまとめたものです。カンマ+半角空白区切りです。MycoBankの情報を基に付与していますが、最新でない可能性があります。 |
KBLab/sucx3_ner | 2022-10-25T06:13:36.000Z | [
"task_categories:other",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:sv",
"license:cc-by-4.0",
"structure-predic... | KBLab | The dataset is a conversion of the venerable SUC 3.0 dataset into the
huggingface ecosystem. The original dataset does not contain an official
train-dev-test split, which is introduced here; the tag distribution for the
NER tags between the three splits is mostly the same.
The dataset has three different types of tagsets: manually annotated POS,
manually annotated NER, and automatically annotated NER. For the
automatically annotated NER tags, only sentences were chosen, where the
automatic and manual annotations would match (with their respective
categories).
Additionally we provide remixes of the same data with some or all sentences
being lowercased. | @article{gustafson2006documentation,
title={Documentation of the Stockholm-Ume{\aa} Corpus},
author={Gustafson-Capkov{\'a}, Sofia and Hartmann, Britt},
journal={Stockholm University: Department of Linguistics},
year={2006}
} | null | 5 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- sv
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids:
- named-entity-recognition
- part-of-speech
pretty_name: sucx3_ner
tags:
- structure-prediction
---
# Dataset Card for _SUCX 3.0 - NER_
## Dataset Description
- **Homepage:** [https://spraakbanken.gu.se/en/resources/suc3](https://spraakbanken.gu.se/en/resources/suc3)
- **Repository:** [https://github.com/kb-labb/sucx3_ner](https://github.com/kb-labb/sucx3_ner)
- **Paper:** [SUC 2.0 manual](http://spraakbanken.gu.se/parole/Docs/SUC2.0-manual.pdf)
- **Point of Contact:**
### Dataset Summary
The dataset is a conversion of the venerable SUC 3.0 dataset into the
huggingface ecosystem.
The original dataset does not contain an official train-dev-test split, which is
introduced here; the tag distribution for the NER tags between the three splits
is mostly the same.
The dataset has three different types of tagsets: manually annotated POS,
manually annotated NER, and automatically annotated NER.
For the automatically annotated NER tags, only sentences were chosen, where the
automatic and manual annotations would match (with their respective categories).
Additionally we provide remixes of the same data with some or all sentences
being lowercased.
### Supported Tasks and Leaderboards
- Part-of-Speech tagging
- Named-Entity-Recognition
### Languages
Swedish
## Dataset Structure
### Data Remixes
- `original_tags` contain the manual NER annotations
- `lower` the whole dataset uncased
- `lower_mix` some of the dataset uncased
- `lower_both` every instance both cased and uncased
- `simple_tags` contain the automatic NER annotations
- `lower` the whole dataset uncased
- `lower_mix` some of the dataset uncased
- `lower_both` every instance both cased and uncased
### Data Instances
For each instance, there is an `id`, with an optional `_lower` suffix to mark
that it has been modified, a `tokens` list of strings containing tokens, a
`pos_tags` list of strings containing POS-tags, and a `ner_tags` list of strings
containing NER-tags.
```json
{"id": "e24d782c-e2475603_lower",
"tokens": ["-", "dels", "har", "vi", "inget", "index", "att", "g\u00e5", "efter", ",", "vi", "kr\u00e4ver", "allts\u00e5", "ers\u00e4ttning", "i", "40-talets", "penningv\u00e4rde", "."],
"pos_tags": ["MID", "KN", "VB", "PN", "DT", "NN", "IE", "VB", "PP", "MID", "PN", "VB", "AB", "NN", "PP", "NN", "NN", "MAD"],
"ner_tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}
```
### Data Fields
- `id`: a string containing the sentence-id
- `tokens`: a list of strings containing the sentence's tokens
- `pos_tags`: a list of strings containing the tokens' POS annotations
- `ner_tags`: a list of strings containing the tokens' NER annotations
### Data Splits
| Dataset Split | Size Percentage of Total Dataset Size | Number of Instances for the Original Tags |
| ------------- | ------------------------------------- | ----------------------------------------- |
| train | 64% | 46\,026 |
| dev | 16% | 11\,506 |
| test | 20% | 14\,383 |
The `simple_tags` remix has fewer instances due to the requirement to match
tags.
## Dataset Creation
See the [original webpage](https://spraakbanken.gu.se/en/resources/suc3)
## Additional Information
### Dataset Curators
[Språkbanken](sb-info@svenska.gu.se)
### Licensing Information
CC BY 4.0 (attribution)
### Citation Information
[SUC 2.0 manual](http://spraakbanken.gu.se/parole/Docs/SUC2.0-manual.pdf)
### Contributions
Thanks to [@robinqrtz](https://github.com/robinqrtz) for adding this dataset.
|
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