author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1 class | downloads float64 1 1M ⌀ | gated bool 2 classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2 classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
naem1023 | null | null | null | false | 1 | false | naem1023/augmented-namuwiki | 2022-07-25T12:45:56.000Z | null | false | 9dfd7de60214c80b44de6a81dda0902c12675511 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/naem1023/augmented-namuwiki/resolve/main/README.md | ---
license: apache-2.0
---
|
naem1023 | null | null | null | false | 1 | false | naem1023/augmented-kowiki | 2022-07-25T13:10:57.000Z | null | false | 0364e2cff990bd0fc2e78d963f2d263e9e645a91 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/naem1023/augmented-kowiki/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-ebf1ec50-11735562 | 2022-07-25T11:37:39.000Z | null | false | 1439a395520ae8c2068bad1e1b07b8d5f052b9be | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-ebf1ec50-11735562/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/deberta-v3-base-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/deberta-v3-base-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-e85023ec-11745564 | 2022-07-25T11:40:25.000Z | null | false | 09d0cf6b8b8cf1c47c25270219270ee5b2207921 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e85023ec-11745564/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/tinyroberta-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/tinyroberta-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-e85023ec-11745565 | 2022-07-25T11:42:15.000Z | null | false | 55b56822e4f31bfb149e822c0004ad25ad90fb94 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e85023ec-11745565/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-large-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/roberta-large-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-e85023ec-11745563 | 2022-07-25T11:47:52.000Z | null | false | 15a9bdb8362664a48997e28994c2baf46eaa71f2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e85023ec-11745563/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/deberta-v3-base-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/deberta-v3-base-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-26568076-11755566 | 2022-07-25T11:53:57.000Z | null | false | 87c34d7017c665a0bb76b416bcfb62bfe17a2ae6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-26568076-11755566/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-large-uncased-whole-word-masking-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-large-uncased-whole-word-masking-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-df3d9ae8-11765567 | 2022-07-25T12:13:39.000Z | null | false | de7be88799fc7659e1e51edbcf4a85f37d249e05 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-df3d9ae8-11765567/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/deberta-v3-large-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/deberta-v3-large-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MichelBartels](https://huggingface.co/MichelBartels) for evaluating this model. |
naem1023 | null | null | null | false | 1 | false | naem1023/augmented-concat-100000 | 2022-07-25T14:30:47.000Z | null | false | 6d87e9c21e26481e929329cd82ed9d27c1e7da26 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/naem1023/augmented-concat-100000/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-conll2003-2dc2f6d8-11805572 | 2022-07-25T14:27:10.000Z | null | false | 9d347362dc8663670ef1512728cdaccf282ef29b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:conll2003"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-conll2003-2dc2f6d8-11805572/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: AJGP/bert-finetuned-ner
metrics: []
dataset_name: conll2003
dataset_config: conll2003
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: AJGP/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@hrezaeim](https://huggingface.co/hrezaeim) for evaluating this model. |
hdamghanian | null | null | null | false | 1 | false | hdamghanian/Stock-QA-fa | 2022-07-25T15:16:43.000Z | null | false | 249e666291cd556d0c0c7967ee3cb6967d77b56c | [] | [
"license:mit"
] | https://huggingface.co/datasets/hdamghanian/Stock-QA-fa/resolve/main/README.md | ---
license: mit
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
This dataset is to be served as a reference for QA tasks.
### Languages
Persian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
All annotations are done according to the SQuAD2.0 data format.
### Source Data
#### Initial Data Collection and Normalization
All context and some of questions are retrieved from [Faradars Introductory Course to Stock Market](https://blog.faradars.org/%d8%a2%d9%85%d9%88%d8%b2%d8%b4-%d8%a8%d9%88%d8%b1%d8%b3-%d8%b1%d8%a7%db%8c%da%af%d8%a7%d9%86/).
#### Who are the source language producers?
Persian (farsi)
### Annotations
#### Annotation process
All annotations are done via Deepset Haystack annotation tool.
#### Who are the annotators?
Hesam Damghanian (this HF account)
## 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]
|
cakiki | null | null | null | false | 1 | false | cakiki/ASE_runs | 2022-07-25T18:11:20.000Z | null | false | e56574b7a9a0bb7c86f71d6350a3a3a5e68646b1 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/cakiki/ASE_runs/resolve/main/README.md | ---
license: apache-2.0
---
|
SocialGrep | null | null | Lite version of our Reddit /r/Bitcoin dataset - CSV of all posts & comments to the /r/Bitcoin subreddit over Jun 2022. | false | 38 | false | SocialGrep/reddit-r-bitcoin-data-for-jun-2022 | 2022-07-25T18:22:16.000Z | null | false | 3038d9967602ee1ba85340246bcd49bb52fd3bef | [] | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original"
] | https://huggingface.co/datasets/SocialGrep/reddit-r-bitcoin-data-for-jun-2022/resolve/main/README.md | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
---
# Dataset Card for reddit-r-bitcoin-data-for-jun-2022
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/reddit-r-bitcoin-data-for-jun-2022?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
- **Reddit downloader used:** [https://socialgrep.com/exports](https://socialgrep.com/exports?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
### Dataset Summary
Lite version of our premium [Reddit /r/Bitcoin dataset](https://socialgrep.com/datasets/the-reddit-r-bitcoin-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022) - CSV of all posts & comments to the /r/Bitcoin subreddit over Jun 2022.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Additional Information
### Licensing Information
CC-BY v4.0
|
anzorq | null | null | null | false | 1 | false | anzorq/kbd_lat-835k_ru-3M | 2022-07-25T23:26:41.000Z | null | false | 24f194c5b6ef29eb784ea3508a022ba848beeea4 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/anzorq/kbd_lat-835k_ru-3M/resolve/main/README.md | ---
license: unknown
---
Kbd latin script: 835k lines from a scraped pile
ru: 3M lines from Wiki (OPUS) |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825575 | 2022-07-25T22:33:19.000Z | null | false | 71d820d52a2662dd708036a15374bbbd68ff57b9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:adversarial_qa"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825575/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: deepset/deberta-v3-large-squad2
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/deberta-v3-large-squad2
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825576 | 2022-07-25T22:32:36.000Z | null | false | e53695a23047a407d2999206a03fc82701148a78 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:adversarial_qa"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825576/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: deepset/bert-large-uncased-whole-word-masking-squad2
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-large-uncased-whole-word-masking-squad2
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825574 | 2022-07-25T22:39:49.000Z | null | false | 2803c28d2003a2afff2a01b409ff7cd42fb0fb17 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:adversarial_qa"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825574/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: mbartolo/electra-large-synqa
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mbartolo/electra-large-synqa
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835577 | 2022-07-25T22:38:58.000Z | null | false | 4deeac719a3ff3df9b5866646f38a35bc45e3c0b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835577/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: mbartolo/roberta-large-synqa
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mbartolo/roberta-large-synqa
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo ](https://huggingface.co/mbartolo ) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835578 | 2022-07-25T22:39:01.000Z | null | false | 34c8374562d8b0e8846c1b926bbe84f4aef4dca5 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835578/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: mbartolo/electra-large-synqa
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mbartolo/electra-large-synqa
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo ](https://huggingface.co/mbartolo ) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835579 | 2022-07-25T22:39:01.000Z | null | false | f329c4e36fa98c42ab3d616e01018048364d47e2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835579/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: deepset/roberta-large-squad2
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/roberta-large-squad2
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo ](https://huggingface.co/mbartolo ) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835580 | 2022-07-25T22:39:44.000Z | null | false | 29dd17d42866336178ac700cbb45bce287a38a34 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835580/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: deepset/deberta-v3-base-squad2
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/deberta-v3-base-squad2
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo ](https://huggingface.co/mbartolo ) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-adversarial_qa-8ac5f360-11845582 | 2022-07-25T23:20:32.000Z | null | false | cdfeb9020eb204c2b5b4e28ac3ef7b18a658cb76 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:adversarial_qa"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-adversarial_qa-8ac5f360-11845582/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: mbartolo/roberta-large-synqa-ext
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mbartolo/roberta-large-synqa-ext
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-adversarial_qa-8ac5f360-11845581 | 2022-07-25T23:26:00.000Z | null | false | 8082af326609adb4497e5770cb5c05824349d0ef | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:adversarial_qa"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-adversarial_qa-8ac5f360-11845581/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: mbartolo/roberta-large-synqa
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mbartolo/roberta-large-synqa
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model. |
ghazalehk | null | null | null | false | 1 | false | ghazalehk/propara | 2022-07-26T00:43:05.000Z | null | false | 858940b5f0599852c1a5e3d86261b4b701a61779 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ghazalehk/propara/resolve/main/README.md | ---
license: apache-2.0
---
|
nateraw | null | null | null | false | 1 | false | nateraw/snares | 2022-07-26T01:48:47.000Z | null | false | dc842ba21530077e2385514585f385e671eb9f32 | [] | [
"language:en",
"license:other"
] | https://huggingface.co/datasets/nateraw/snares/resolve/main/README.md | ---
language: en
license: other
---
# Snares
FSD50K subset of just snares.
```
wget -nc https://huggingface.co/datasets/nateraw/snares/resolve/main/snares.csv
wget -nc https://huggingface.co/datasets/nateraw/snares/resolve/main/snares.zip
unzip snares.zip
```
If you unpack as described above, `snares.csv` will have correct filepath to audio file when loaded in as CSV. Here we show with pandas...
```python
import pandas as pd
df = pd.read_csv('snares.csv')
``` |
betterme | null | null | null | false | 1 | false | betterme/goldendata | 2022-07-26T02:05:07.000Z | null | false | e35dab202174c0845fc172f35bfcada0a31bafa4 | [] | [
"license:mit"
] | https://huggingface.co/datasets/betterme/goldendata/resolve/main/README.md | ---
license: mit
---
|
Kamrani | null | null | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | false | 3 | false | Kamrani/en-fa-translation | 2022-07-30T04:13:38.000Z | null | false | cdeb4ea38252c283f5717b007ae8f8d5c5d3c73f | [] | [] | https://huggingface.co/datasets/Kamrani/en-fa-translation/resolve/main/README.md | annotations_creators:
- no-annotation
language:
- en
- fa
language_creators:
- crowdsourced
license:
- other
multilinguality:
- multilingual
pretty_name: en-fa-translation
size_categories:
- 1M<n<10M
source_datasets:
- original
tags: []
task_categories:
- translation
task_ids: [] |
puffy310 | null | null | null | false | 1 | false | puffy310/SimulaPrompts | 2022-07-26T04:52:28.000Z | null | false | b0a347020b300ca529aef033c85d00f5ae42aa3e | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/puffy310/SimulaPrompts/resolve/main/README.md | ---
license: apache-2.0
---
|
muibk | null | @inproceedings{ma-etal-2019-results,
title = {Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges},
author = {Ma, Qingsong and Wei, Johnny and Bojar, Ondřej and Graham, Yvette},
booktitle = {Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)},
month = {aug},
year = {2019},
address = {Florence, Italy},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/W19-5302},
doi = {10.18653/v1/W19-5302},
pages = {62--90}
} | This shared task will examine automatic evaluation metrics for machine translation. We will provide
you with all of the translations produced in the translation task along with the human reference translations.
You will return your automatic metric scores for translations at the system-level and/or at the sentence-level.
We will calculate the system-level and sentence-level correlations of your scores with WMT19 human judgements
once the manual evaluation has been completed. | false | 1 | false | muibk/wmt19_metrics_task | 2022-07-26T10:06:23.000Z | null | false | 8078e27c5ff4c52d5b85572ed45d36c712a3c423 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"language_creators:expert-generated",
"language:de-cs",
"language:de-en",
"language:de-fr",
"language:en-cs",
"language:en-de",
"language:en-fi",
"language:en-gu",
"language:en-kk",
"la... | https://huggingface.co/datasets/muibk/wmt19_metrics_task/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
- machine-generated
- expert-generated
language:
- de-cs
- de-en
- de-fr
- en-cs
- en-de
- en-fi
- en-gu
- en-kk
- en-lt
- en-ru
- en-zh
- fi-en
- fr-de
- gu-en
- kk-en
- lt-en
- ru-en
- zh-en
license:
- unknown
multilinguality:
- translation
paperswithcode_id: null
pretty_name: WMT19 Metrics Shared Task
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- translation
task_ids: []
---
# Dataset Card for WMT19 Metrics Task
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [WMT19 Metrics Shared Task](https://www.statmt.org/wmt19/metrics-task.html)
- **Repository:** [MT Metrics Eval Github Repository](https://github.com/google-research/mt-metrics-eval)
- **Paper:** [Paper](https://aclanthology.org/W19-5302/)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset comprises the following language pairs:
- de-cs
- de-en
- de-fr
- en-cs
- en-de
- en-fi
- en-gu
- en-kk
- en-lt
- en-ru
- en-zh
- fi-en
- fr-de
- gu-en
- kk-en
- lt-en
- ru-en
- zh-en
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/mustaszewski) for adding this dataset.
|
Achen | null | null | null | false | 1 | false | Achen/large-test | 2022-07-27T02:39:08.000Z | null | false | 38e21f35965ee9f983839b416930aa3429ac60a5 | [] | [
"license:bsd-2-clause"
] | https://huggingface.co/datasets/Achen/large-test/resolve/main/README.md | ---
license: bsd-2-clause
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855583 | 2022-07-26T08:20:48.000Z | null | false | 53514cd2b8c3bccdde0a61348e5ef76d3a6748a6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855583/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/electra-base-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/electra-base-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855584 | 2022-07-26T08:20:20.000Z | null | false | 696f9a3028e982a43e69283dab450a4be0e0f72e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855584/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/tinybert-6l-768d-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/tinybert-6l-768d-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855585 | 2022-07-26T08:20:49.000Z | null | false | 55b89a23287e3762d16ad2ed49412c4dbb00d49a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855585/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-base-uncased-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-base-uncased-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855586 | 2022-07-26T08:20:27.000Z | null | false | e5c897042bb83fe95d7f687c51d48ed06f2b55a2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855586/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-medium-squad2-distilled
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-medium-squad2-distilled
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855587 | 2022-07-26T08:21:10.000Z | null | false | 425e4ccec0605e663e762c5a088dcc5c6884329b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855587/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/xlm-roberta-base-squad2-distilled
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/xlm-roberta-base-squad2-distilled
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
jordane95 | null | null | null | false | 1 | false | jordane95/msmarco-passage-with-query | 2022-07-26T08:25:24.000Z | null | false | a3eaedbf1567d2f8b47042e495063ebfdd3d4b3f | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/jordane95/msmarco-passage-with-query/resolve/main/README.md | ---
license: afl-3.0
---
|
jordane95 | null | @misc{bajaj2018ms,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu
and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song
and Alina Stoica and Saurabh Tiwary and Tong Wang},
year={2018},
eprint={1611.09268},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | false | 1 | false | jordane95/msmarco-passage-corpus-with-query | 2022-07-27T02:02:45.000Z | null | false | c68710dd4a69eca02807018e4e93a4211b68c86a | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/jordane95/msmarco-passage-corpus-with-query/resolve/main/README.md | ---
license: afl-3.0
---
|
mingz | null | null | null | false | 1 | false | mingz/demo | 2022-07-26T10:55:21.000Z | null | false | 2da7b5117419f7bd56e09b02442fed0c5c2e934a | [] | [] | https://huggingface.co/datasets/mingz/demo/resolve/main/README.md | |
asparius | null | null | null | false | 36 | false | asparius/demirtas-movie | 2022-07-26T11:56:21.000Z | null | false | b9a7ed6dcfa2236fcfd4cc28fd129f5642ddf89d | [] | [
"license:mit"
] | https://huggingface.co/datasets/asparius/demirtas-movie/resolve/main/README.md | ---
license: mit
---
|
frogvre | null | null | null | false | 1 | false | frogvre/lgo1 | 2022-07-26T14:20:16.000Z | null | false | dc2f783401fca4dc3c2fd7dd2b3b54892ca65332 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/frogvre/lgo1/resolve/main/README.md | ---
license: unknown
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-deepset__germanquad-7176bd7d-11875589 | 2022-07-26T14:40:30.000Z | null | false | 0d81b9869910b53d9fac2bddf8d3e2eb2afe8a50 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:deepset/germanquad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-deepset__germanquad-7176bd7d-11875589/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- deepset/germanquad
eval_info:
task: extractive_question_answering
model: deepset/gelectra-base-germanquad
metrics: []
dataset_name: deepset/germanquad
dataset_config: plain_text
dataset_split: test
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/gelectra-base-germanquad
* Dataset: deepset/germanquad
* Config: plain_text
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjlree](https://huggingface.co/sjlree) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-deepset__germanquad-7176bd7d-11875590 | 2022-07-26T14:40:57.000Z | null | false | 0b848ff3c9d5c4d515e9fea94415453bc756d489 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:deepset/germanquad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-deepset__germanquad-7176bd7d-11875590/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- deepset/germanquad
eval_info:
task: extractive_question_answering
model: deepset/gelectra-large-germanquad
metrics: []
dataset_name: deepset/germanquad
dataset_config: plain_text
dataset_split: test
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/gelectra-large-germanquad
* Dataset: deepset/germanquad
* Config: plain_text
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjlree](https://huggingface.co/sjlree) for evaluating this model. |
Achen | null | null | null | false | 1 | false | Achen/voc-test | 2022-07-27T03:24:17.000Z | null | false | 8c73891571e4da2fe888c7e9ed21167402492e59 | [] | [
"license:bsd"
] | https://huggingface.co/datasets/Achen/voc-test/resolve/main/README.md | ---
license: bsd
---
|
frgfm | null | @software{Howard_Imagewoof_2019,
title={Imagewoof: a subset of 10 classes from Imagenet that aren't so easy to classify},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette#imagewoof}
} | Imagewoof is a subset of 10 classes from Imagenet that aren't so
easy to classify, since they're all dog breeds. The breeds are:
Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu,
English foxhound, Rhodesian ridgeback, Dingo, Golden retriever,
Old English sheepdog. | false | 124 | false | frgfm/imagewoof | 2022-07-27T19:47:31.000Z | imagewoof | false | a48b72c4a7735d5c18f580f009fa66fa330af8da | [] | [
"annotations_creators:crowdsourced",
"language:en",
"language_creators:crowdsourced",
"license:apache-2.0",
"size_categories:1K<n<10K",
"source_datasets:extended",
"task_categories:image-classification",
"task_ids:image-classification"
] | https://huggingface.co/datasets/frgfm/imagewoof/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality: []
pretty_name: Imagewoof
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- image-classification
task_ids:
- image-classification
paperswithcode_id: imagewoof
---
# Dataset Card for Imagewoof
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/fastai/imagenette#imagewoof
- **Repository:** https://github.com/fastai/imagenette
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagewoof
### Dataset Summary
A smaller subset of 10 classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary) that aren't so easy to classify, since they're all dog breeds.
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.
### Supported Tasks and Leaderboards
- `image-classification`: The dataset can be used to train a model for Image Classification.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
A data point comprises an image URL and its classification label.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>,
'label': 'Beagle',
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image.
- `label`: the expected class label of the image.
### Data Splits
| |train|validation|
|---------|----:|---------:|
|imagewoof| 9025| 3929|
## Dataset Creation
### Curation Rationale
cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale
### Source Data
#### Initial Data Collection and Normalization
Imagewoof is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
### Annotations
#### Annotation process
cf. https://huggingface.co/datasets/imagenet-1k#annotation-process
#### Who are the annotators?
cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators
### Personal and Sensitive Information
cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information
## Considerations for Using the Data
### Social Impact of Dataset
cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset
### Discussion of Biases
cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases
### Other Known Limitations
cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations
## Additional Information
### Dataset Curators
cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators
and Jeremy Howard
### Licensing Information
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@software{Howard_Imagewoof_2019,
title={Imagewoof: a subset of 10 classes from Imagenet that aren't so easy to classify},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette#imagewoof}
}
```
### Contributions
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).
|
robertmyers | null | null | null | false | 1 | false | robertmyers/convo_base | 2022-07-26T15:56:10.000Z | null | false | 3f80d82f04e37d40b5972c5fcc5bb0e7c7830e76 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/robertmyers/convo_base/resolve/main/README.md | ---
license: afl-3.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-47db8743-11885591 | 2022-07-26T16:38:56.000Z | null | false | a0d9ca0b1c481c4e8b2100bb6eb0457559e3f508 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-47db8743-11885591/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: Graphcore/roberta-base-squad
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Graphcore/roberta-base-squad
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Narayana](https://huggingface.co/Narayana) for evaluating this model. |
naem1023 | null | null | null | false | 1 | false | naem1023/final_aug_2000 | 2022-07-26T17:52:12.000Z | null | false | 7c7d48c7cf5047d41d499131f6e3e5d57fc8abe5 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/naem1023/final_aug_2000/resolve/main/README.md | ---
license: afl-3.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-8f63e3f3-11895592 | 2022-07-26T18:52:05.000Z | null | false | 2eb12757b146d9c1fbfda4e8f8d4a10c520de326 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-8f63e3f3-11895592/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: sshleifer/distilbart-cnn-12-6
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-cnn-12-6
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-8f63e3f3-11895594 | 2022-07-26T18:47:15.000Z | null | false | 9acbc0b433d326333ebec9838d2cfd3dd96e4a6c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-8f63e3f3-11895594/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: philschmid/distilbart-cnn-12-6-samsum
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: philschmid/distilbart-cnn-12-6-samsum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-8f63e3f3-11895593 | 2022-07-26T18:34:27.000Z | null | false | 09f0a5fb1b4b7bb1b18dac3c50ceeeaae00969fe | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-8f63e3f3-11895593/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: sshleifer/distilbart-cnn-6-6
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-cnn-6-6
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
biglam | null | null | null | false | 1 | false | biglam/berlin_state_library_ocr | 2022-08-05T09:36:24.000Z | null | false | a890935d5bd754ddc5b85f56b6f34f6d2bb4abba | [] | [
"annotations_creators:machine-generated",
"language:de",
"language:nl",
"language:en",
"language:fr",
"language:es",
"language_creators:expert-generated",
"license:cc-by-4.0",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"tags:ocr",
"tags:library",
"task_categories:fill-mask",... | https://huggingface.co/datasets/biglam/berlin_state_library_ocr/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language:
- de
- nl
- en
- fr
- es
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: Berlin State Library OCR
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- ocr
- library
task_categories:
- fill-mask
- text-generation
task_ids:
- masked-language-modeling
- language-modeling
---
# Dataset Card for Berlin State Library OCR data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
> The digital collections of the SBB contain 153,942 digitized works from the time period of 1470 to 1945.
> At the time of publication, 28,909 works have been OCR-processed resulting in 4,988,099 full-text pages.
For each page with OCR text, the language has been determined by langid (Lui/Baldwin 2012).
### Supported Tasks and Leaderboards
- `language-modeling`: this dataset has the potential to be used for training language models on historical/OCR'd text. Since it contains OCR confidence, language and date information for many examples, it is also possible to filter this dataset to more closely match the requirements for training data.
-
### Languages
The collection includes material across a large number of languages. The languages of the OCR text have been detected using [langid.py: An Off-the-shelf Language Identification Tool](https://aclanthology.org/P12-3005) (Lui & Baldwin, ACL 2012). The dataset includes a confidence score for the language prediction. **Note:** not all examples may have been successfully matched to the language prediction table from the original data.
The frequency of the top ten languages in the dataset is shown below:
| | frequency |
|----|------------------|
| de | 3.20963e+06 |
| nl | 491322 |
| en | 473496 |
| fr | 216210 |
| es | 68869 |
| lb | 33625 |
| la | 27397 |
| pl | 17458 |
| it | 16012 |
| zh | 11971 |
[More Information Needed]
## Dataset Structure
### Data Instances
Each example represents a single page of OCR'd text.
A single example of the dataset is as follows:
```python
{'aut': 'Doré, Henri',
'date': '1912',
'file name': '00000218.xml',
'language': 'fr',
'language_confidence': 1.0,
'place': 'Chang-hai',
'ppn': '646426230',
'publisher': 'Imprimerie de la Mission Catholique',
'text': "— 338 — Cela fait, on enterre la statuette qu’on vient d’outrager, atten dant la réalisation sur la personne elle-même. C’est l’outrage en effigie. Un deuxième moyen, c’est de représenter l’Esprit Vengeur sous la figure d’un fier-à-bras, armé d’un sabre, ou d’une pique, et de lui confier tout le soin de sa vengeance. On multiplie les incantations et les offrandes en son honneur, pour le porter au paroxysme de la fureur, et inspirer à l’Esprit malin l’idée de l’exécution de ses désirs : en un mot, on fait tout pour faire passer en son cœur la rage de vengeance qui consume le sien propre. C’est une invention diabolique imaginée pour assouvir sa haine sur l’ennemi qu’on a en horreur. Ailleurs, ce n’est qu’une figurine en bois ou en papier, qui est lancée contre l’ennemi; elle se dissimule, ou prend des formes fantastiques pour acomplir son œuvre de vengeance. Qu’on se rappelle la panique qui régna dans la ville de Nan- king ifâ ffl, et ailleurs, l’année où de méchantes gens répandirent le bruit que des hommes de papier volaient en l’air et coupaient les tresses de cheveux des Chinois. Ce fut une véritable terreur, tous étaient affolés, et il y eut à cette occasion de vrais actes de sauvagerie. Voir historiettes sur les envoûtements : Wieger Folk-Lore, N os 50, 128, 157, 158, 159. Corollaire. Les Tao-niu jift fx ou femmes “ Tao-clie'’. A cette super stition peut se rapporter la pratique des magiciennes du Kiang- sou ■n: m, dans les environs de Chang-hai ± m, par exemple. Ces femmes portent constamment avec- elles une statue réputée merveilleuse : elle n’a que quatre ou cinq pouces de hauteur ordinairement. A force de prières, d’incantations, elles finissent par la rendre illuminée, vivante et parlante, ou plutôt piaillarde, car elle ne répond que par des petits cris aigus et répétés aux demandes qu’on lui adressé; elle paraît comme animée, sautille,",
'title': 'Les pratiques superstitieuses',
'wc': [1.0,
0.7266666889,
1.0,
0.9950000048,
0.7059999704,
0.5799999833,
0.7142857313,
0.7250000238,
0.9855555296,
0.6880000234,
0.7099999785,
0.7054545283,
1.0,
0.8125,
0.7950000167,
0.5681818128,
0.5500000119,
0.7900000215,
0.7662500143,
0.8830000162,
0.9359999895,
0.7411110997,
0.7950000167,
0.7962499857,
0.6949999928,
0.8937500119,
0.6299999952,
0.8820000291,
1.0,
0.6781818271,
0.7649999857,
0.437142849,
1.0,
1.0,
0.7416666746,
0.6474999785,
0.8166666627,
0.6825000048,
0.75,
0.7033333182,
0.7599999905,
0.7639999986,
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1.0,
1.0,
0.5466666818,
0.7571428418,
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1.0,
1.0,
0.7099999785,
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0.8588888645,
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1.0,
0.8333333135,
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0.7922222018,
1.0,
1.0,
0.6657142639,
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0.6000000238,
0.9737499952,
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1.0,
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1.0,
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0.4875000119,
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0.6650000215,
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0.5049999952,
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1.0,
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1.0,
0.6800000072,
0.6499999762,
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1.0,
0.6600000262,
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1.0,
1.0,
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0.8824999928,
0.6700000167,
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1.0,
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1.0,
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1.0,
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1.0,
0.7562500238,
1.0,
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0.8500000238,
0.4819999933,
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1.0,
0.8399999738,
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1.0,
0.9474999905,
0.453333348,
0.6575000286,
0.9399999976,
0.6733333468,
0.8042857051,
0.7599999905,
1.0,
0.7355555296,
0.6499999762,
0.7118181586,
1.0,
0.621999979,
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1.0,
0.853333354,
0.6650000215,
0.75,
0.7787500024,
1.0,
0.8840000033,
1.0,
0.851111114,
1.0,
0.9142857194,
1.0,
0.8899999857,
1.0,
0.9024999738,
1.0,
0.6166666746,
0.7533333302,
0.7766666412,
0.6637499928,
1.0,
0.8471428752,
0.7012500167,
0.6600000262,
0.8199999928,
1.0,
0.7766666412,
0.3899999857,
0.7960000038,
0.8050000072,
1.0,
0.8000000119,
0.7620000243,
1.0,
0.7163636088,
0.5699999928,
0.8849999905,
0.6166666746,
0.8799999952,
0.9058333039,
1.0,
0.6866666675,
0.7810000181,
0.3400000036,
0.2599999905,
0.6333333254,
0.6524999738,
0.4875000119,
0.7425000072,
0.75,
0.6863636374,
1.0,
0.8742856979,
0.137500003,
0.2099999934,
0.4199999869,
0.8216666579,
1.0,
0.7563636303,
0.3000000119,
0.8579999804,
0.6679999828,
0.7099999785,
0.7875000238,
0.9499999881,
0.5799999833,
0.9150000215,
0.6600000262,
0.8066666722,
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0.6999999881,
0.7400000095,
0.8066666722,
0.2866666615,
0.6700000167,
0.9225000143,
1.0,
0.7599999905,
0.75,
0.6899999976,
0.3600000143,
0.224999994,
0.5799999833,
0.8874999881,
1.0,
0.8066666722,
0.8985714316,
0.8827272654,
0.8460000157,
0.8880000114,
0.9533333182,
0.7966666818,
0.75,
0.8941666484,
1.0,
0.8450000286,
0.8666666746,
0.9533333182,
0.5883333087,
0.5799999833,
0.6549999714,
0.8600000143,
1.0,
0.7585714459,
0.7114285827,
1.0,
0.8519999981,
0.7250000238,
0.7437499762,
0.6639999747,
0.8939999938,
0.8877778053,
0.7300000191,
1.0,
0.8766666651,
0.8019999862,
0.8928571343,
1.0,
0.853333354,
0.5049999952,
0.5416666865,
0.7963636518,
0.5600000024,
0.8774999976,
0.6299999952,
0.5749999881,
0.8199999928,
0.7766666412,
1.0,
0.9850000143,
0.5674999952,
0.6240000129,
1.0,
0.9485714436,
1.0,
0.8174999952,
0.7919999957,
0.6266666651,
0.7887499928,
0.7825000286,
0.5366666913,
0.65200001,
0.832857132,
0.7488889098]}
```
### Data Fields
- 'file name': filename of the original XML file
- 'text': OCR'd text for that page of the item
- 'wc': the word confidence for each token predicted by the OCR engine
- 'ppn': 'Pica production numbers' an internal ID used by the library. See [](https://doi.org/10.5281/zenodo.2702544) for more details.
'language': language predicted by `langid.py` (see above for more details)
-'language_confidence': confidence score given by `langid.py`
- publisher: publisher of the item in which the text appears
- place: place of publication of the item in which the text appears
- date: date of the item in which the text appears
- title: title of the item in which the text appears
- aut: author of the item in which the text appears
[More Information Needed]
### Data Splits
This dataset contains only a single split `train`.
## Dataset Creation
The dataset is created from [OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB)](https://doi.org/10.5281/zenodo.3257041) hosted on Zenodo.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is created from [OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB)](https://doi.org/10.5281/zenodo.3257041) hosted on Zenodo. This dataset includes text content produced through running Optical Character Recognition across 153,942 digitized works held by the Berlin State Library.
The [dataprep.ipynb](https://huggingface.co/datasets/biglam/berlin_state_library_ocr/blob/main/dataprep.ipynb) was used to create this dataset.
To make the dataset more useful for training language models, the following steps were carried out:
- the CSV `xml2csv_alto.csv`, which contains the full text corpus per document page (incl.OCR word confidences) was loaded using the `datasets` library
- this CSV was augmented with language information from `corpus-language.pkl` **note** some examples don't find a match for this. Sometimes this is because a text is blank, but some actual text may be missing predicted language information
- the CSV was further augmented by trying to map the PPN to fields in a metadata download created using [https://github.com/elektrobohemian/StabiHacks/blob/master/oai-analyzer/oai-analyzer.py](https://github.com/elektrobohemian/StabiHacks/blob/master/oai-analyzer/oai-analyzer.py). **note** not all examples are successfully matched to this metadata download.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
This dataset contains machine-produced annotations for:
- the confidence scores the OCR engines used to produce the full-text materials.
- the predicted languages and associated confidence scores produced by `langid.py`
The dataset also contains metadata for the following fields:
- author
- publisher
- the place of publication
- title
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
This dataset contains historical material, potentially including names, addresses etc., but these are not likely to refer to living individuals.
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
As with any historical material, the views and attitudes expressed in some texts will likely diverge from contemporary beliefs. One should consider carefully how this potential bias may become reflected in language models trained on this data.
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Initial data created by: Labusch, Kai; Zellhöfer, David
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```
@dataset{labusch_kai_2019_3257041,
author = {Labusch, Kai and
Zellhöfer, David},
title = {{OCR fulltexts of the Digital Collections of the
Berlin State Library (DC-SBB)}},
month = jun,
year = 2019,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.3257041},
url = {https://doi.org/10.5281/zenodo.3257041}
}
```
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
|
tarteel-ai | null | @article{malhas2020ayatec,
author = {Malhas, Rana and Elsayed, Tamer},
title = {AyaTEC: Building a Reusable Verse-Based Test Collection for Arabic Question Answering on the Holy Qur’an},
year = {2020},
issue_date = {November 2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {19},
number = {6},
issn = {2375-4699},
url = {https://doi.org/10.1145/3400396},
doi = {10.1145/3400396},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {oct},
articleno = {78},
numpages = {21},
keywords = {evaluation, Classical Arabic}
} | The absence of publicly available reusable test collections for Arabic question answering on the Holy Qur’an has impeded the possibility of fairly comparing the performance of systems in that domain. In this article, we introduce AyaTEC, a reusable test collection for verse-based question answering on the Holy Qur’an, which serves as a common experimental testbed for this task. AyaTEC includes 207 questions (with their corresponding 1,762 answers) covering 11 topic categories of the Holy Qur’an that target the information needs of both curious and skeptical users. To the best of our effort, the answers to the questions (each represented as a sequence of verses) in AyaTEC were exhaustive—that is, all qur’anic verses that directly answered the questions were exhaustively extracted and annotated. To facilitate the use of AyaTEC in evaluating the systems designed for that task, we propose several evaluation measures to support the different types of questions and the nature of verse-based answers while integrating the concept of partial matching of answers in the evaluation. | false | 1 | false | tarteel-ai/quranqa | 2022-07-27T02:28:31.000Z | null | false | 88b10b40e3197c83f2995771e057515f584ecd27 | [] | [
"annotations_creators:expert-generated",
"language:ar",
"language_creators:expert-generated",
"license:cc-by-nd-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"source_datasets:original",
"tags:quran",
"tags:qa",
"task_categories:question-answering",
"t... | https://huggingface.co/datasets/tarteel-ai/quranqa/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- ar
language_creators:
- expert-generated
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
pretty_name: Qur'anic Reading Comprehension Dataset
size_categories:
- n<1K
- 1K<n<10K
source_datasets:
- original
tags:
- quran
- qa
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for the Qur'anic Reading Comprehension Dataset (QRCD)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://sites.google.com/view/quran-qa-2022/home
- **Repository:** https://gitlab.com/bigirqu/quranqa/-/tree/main/
- **Paper:** https://dl.acm.org/doi/10.1145/3400396
- **Leaderboard:**
- **Point of Contact:** @piraka9011
### Dataset Summary
The QRCD (Qur'anic Reading Comprehension Dataset) is composed of 1,093 tuples of question-passage pairs that are
coupled with their extracted answers to constitute 1,337 question-passage-answer triplets.
### Supported Tasks and Leaderboards
This task is evaluated as a ranking task.
To give credit to a QA system that may retrieve an answer (not necessarily at the first rank) that does not fully
match one of the gold answers but partially matches it, we use partial Reciprocal Rank (pRR) measure.
It is a variant of the traditional Reciprocal Rank evaluation metric that considers partial matching.
pRR is the official evaluation measure of this shared task.
We will also report Exact Match (EM) and F1@1, which are evaluation metrics applied only on the top predicted answer.
The EM metric is a binary measure that rewards a system only if the top predicted answer exactly matches one of the
gold answers.
Whereas, the F1@1 metric measures the token overlap between the top predicted answer and the best matching gold answer.
To get an overall evaluation score, each of the above measures is averaged over all questions.
### Languages
Qur'anic Arabic
## Dataset Structure
### Data Instances
To simplify the structure of the dataset, each tuple contains one passage, one question and a list that may contain
one or more answers to that question, as shown below:
```json
{
"pq_id": "38:41-44_105",
"passage": "واذكر عبدنا أيوب إذ نادى ربه أني مسني الشيطان بنصب وعذاب. اركض برجلك هذا مغتسل بارد وشراب. ووهبنا له أهله ومثلهم معهم رحمة منا وذكرى لأولي الألباب. وخذ بيدك ضغثا فاضرب به ولا تحنث إنا وجدناه صابرا نعم العبد إنه أواب.",
"surah": 38,
"verses": "41-44",
"question": "من هو النبي المعروف بالصبر؟",
"answers": [
{
"text": "أيوب",
"start_char": 12
}
]
}
```
Each Qur’anic passage in QRCD may have more than one occurrence; and each passage occurrence is paired with a different
question.
Likewise, each question in QRCD may have more than one occurrence; and each question occurrence is paired with a
different Qur’anic passage.
The source of the Qur'anic text in QRCD is the Tanzil project download page, which provides verified versions of the
Holy Qur'an in several scripting styles.
We have chosen the simple-clean text style of Tanzil version 1.0.2.
### Data Fields
* `pq_id`: Sample ID
* `passage`: Context text
* `surah`: Surah number
* `verses`: Verse range
* `question`: Question text
* `answers`: List of answers and their start character
### Data Splits
| **Dataset** | **%** | **# Question-Passage Pairs** | **# Question-Passage-Answer Triplets** |
|-------------|:-----:|:-----------------------------:|:---------------------------------------:|
| Training | 65% | 710 | 861 |
| Development | 10% | 109 | 128 |
| Test | 25% | 274 | 348 |
| All | 100% | 1,093 | 1,337 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The QRCD v1.1 dataset is distributed under the CC-BY-ND 4.0 License https://creativecommons.org/licenses/by-nd/4.0/legalcode
For a human-readable summary of (and not a substitute for) the above CC-BY-ND 4.0 License, please refer to https://creativecommons.org/licenses/by-nd/4.0/
### Citation Information
```
@article{malhas2020ayatec,
author = {Malhas, Rana and Elsayed, Tamer},
title = {AyaTEC: Building a Reusable Verse-Based Test Collection for Arabic Question Answering on the Holy Qur’an},
year = {2020},
issue_date = {November 2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {19},
number = {6},
issn = {2375-4699},
url = {https://doi.org/10.1145/3400396},
doi = {10.1145/3400396},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = {oct},
articleno = {78},
numpages = {21},
keywords = {evaluation, Classical Arabic}
}
```
### Contributions
Thanks to [@piraka9011](https://github.com/piraka9011) for adding this dataset.
|
BirdL | null | null | null | false | 1 | false | BirdL/DallData | 2022-09-28T21:12:02.000Z | null | false | e7d4f3001b1c33740f10caa51c61cd4199e831e0 | [] | [
"license:other",
"size_categories:1K<n<10K",
"task_categories:unconditional-image-generation"
] | https://huggingface.co/datasets/BirdL/DallData/resolve/main/README.md | ---
annotations_creators: []
language: []
language_creators: []
license:
- other
multilinguality: []
pretty_name: DALL-E Latent Space Mapping
size_categories:
- 1K<n<10K
source_datasets: []
tags: []
task_categories:
- unconditional-image-generation
task_ids: []
---
DallData is a non-exhaustive look into DALL-E Mega(1)'s unconditional image generation. This is under the [BirdL-AirL License.](https://huggingface.co/spaces/BirdL/license/)
(1)
```bibtext
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
``` |
biglam | null | @misc{ContentiousContextsCorpus2021,
author = {Cultural AI},
title = {Contentious Contexts Corpus},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/cultural-ai/ConConCor}},
} | This dataset contains extracts from historical Dutch newspapers which have been containing keywords of potentially contentious words (according to present-day sensibilities).
The dataset contains multiple annotations per instance, given the option to quantify agreement scores for annotations. This dataset can be used to track how words and their meanings have changed over time | false | 1 | false | biglam/contentious_contexts | 2022-08-01T17:02:11.000Z | null | false | 794edc666ccae9f296d033a99a826a3f41f34385 | [] | [
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language:nl",
"language_creators:machine-generated",
"license:cc-by-2.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"tags:newspapers",
"tags:historic",
"tags:dutch",
"tag... | https://huggingface.co/datasets/biglam/contentious_contexts/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- nl
language_creators:
- machine-generated
license:
- cc-by-2.0
multilinguality:
- monolingual
pretty_name: Contentious Contexts Corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- newspapers
- historic
- dutch
- problematic
- ConConCor
task_categories:
- text-classification
task_ids:
- sentiment-scoring
- multi-label-classification
---
# Dataset Card for Contentious Contexts Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [ConConCor](https://github.com/cultural-ai/ConConCor)
- **Repository:** [ConConCor](https://github.com/cultural-ai/ConConCor)
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** [Jacco van Ossenbruggen](https://github.com/jrvosse)
**Note** One can also find a Datasheet produced by the creators of this dataset as a [PDF document](https://github.com/cultural-ai/ConConCor/blob/main/Dataset/DataSheet.pdf)
### Dataset Summary
This dataset contains extracts from historical Dutch newspapers containing keywords of potentially contentious words (according to present-day sensibilities). The dataset contains multiple annotations per instance, given the option to quantify agreement scores for annotations. This dataset can be used to track how words and their meanings have changed over time
### Supported Tasks and Leaderboards
- `text-classification`: This dataset can be used for tracking how the meanings of words in different contexts have changed and become contentious over time
### Languages
The text in the dataset is in Dutch. The responses are available in both English and Dutch. Suggestions, where present, are only in Dutch. The associated BCP-47 code is `nl`
## Dataset Structure
### Data Instances
```
{
'extract_id': 'H97',
'text': 'en waardoor het eerste doel wordt voorbijgestreefd om voor den 5D5c5Y 5d-5@5j5g5d5e5Z5V5V5c een speciale eigen werkingssfeer te
scheppen.Intusschen is het',
'target': '5D 5c5Y5d-5@5j5g5d5e5Z5V5V5c',
'annotator_responses_english': [
{'id': 'unknown_2a', 'response': 'Not contentious'},
{'id': 'unknown_2b', 'response': 'Contentious according to current standards'},
{'id': 'unknown_2c', 'response': "I don't know"},
{'id': 'unknown_2d', 'response': 'Contentious according to current standards'},
{'id': 'unknown_2e', 'response': 'Not contentious'},
{'id': 'unknown_2f', 'response': "I don't know"},
{'id': 'unknown_2g', 'response': 'Not contentious'}],
'annotator_responses_dutch': [
{'id': 'unknown_2a', 'response': 'Niet omstreden'},
{'id': 'unknown_2b', 'response': 'Omstreden naar huidige maatstaven'},
{'id': 'unknown_2c', 'response': 'Weet ik niet'},
{'id': 'unknown_2d', 'response': 'Omstreden naar huidige maatstaven'},
{'id': 'unknown_2e', 'response': 'Niet omstreden'},
{'id': 'unknown_2f', 'response': 'Weet ik niet'},
{'id': 'unknown_2g', 'response': 'Niet omstreden'}],
'annotator_suggestions': [
{'id': 'unknown_2a', 'suggestion': ''},
{'id': 'unknown_2b', 'suggestion': 'ander ras nodig'},
{'id': 'unknown_2c', 'suggestion': 'personen van ander ras'},
{'id': 'unknown_2d', 'suggestion': ''},
{'id': 'unknown_2e', 'suggestion': ''},
{'id': 'unknown_2f', 'suggestion': ''},
{'id': 'unknown_2g', 'suggestion': 'ras'}]
}
```
### Data Fields
|extract_id|text|target|annotator_responses_english|annotator_responses_dutch|annotator_suggestions|
|---|---|---|---|---|---|
|Unique identifier|Text|Target phrase or word|Response(translated to English)|Response in Dutch|Suggestions, if present|
### Data Splits
Train: 2720
## Dataset Creation
### Curation Rationale
> Cultural heritage institutions recognise the problem of language use in their collections. The cultural objects in archives, libraries, and museums contain words and phrases that are inappropriate in modern society but were used broadly back in times. Such words can be offensive and discriminative. In our work, we use the term "contentious" to refer to all (potentially) inappropriate or otherwise sensitive words. For example, words suggestive of some (implicit or explicit) bias towards or against something. The National Archives of the Netherlands stated that they "explore the possibility of explaining language that was acceptable and common in the past and providing it with contemporary alternatives", meanwhile "keeping the original descriptions [with contentious words], because they give an idea of the time in which they were made or included in the collection". There is a page on the institution website where people can report "offensive language".
### Source Data
#### Initial Data Collection and Normalization
> The queries were run on OCR'd versions of the Europeana Newspaper collection, as provided by the KB National Library of the Netherlands. We limited our pool to text categorised as "article", thus excluding other types of texts such as advertisements and family notices. We then only focused our sample on the 6 decades between 1890-01-01 and 1941-12-31, as this is the period available in the Europeana newspaper corpus. The dataset represents a stratified sample set over target word, decade, and newspaper issue distribution metadata. For the final set of extracts for annotation, we gave extracts sampling weights proportional to their actual probabilities, as estimated from the initial set of extracts via trigram frequencies, rather than sampling uniformly.
#### Who are the source language producers?
[N/A]
### Annotations
#### Annotation process
> The annotation process included 3 stages: pilot annotation, expert annotation, and crowdsourced annotation on the "Prolific" platform. All stages required the participation of Dutch speakers. The pilot stage was intended for testing the annotation layout, the instructions clarity, the number of sentences provided as context, the survey questions, and the difficulty of the task in general. The Dutch-speaking members of the Cultural AI Lab were asked to test the annotation process and give their feedback anonymously using Google Sheets. Six volunteers contributed to the pilot stage, each annotating the same 40 samples where either a context of 3 or 5 sentences surrounding the term were given. An individual annotation sheet had a table layout with 4 options to choose for every sample
> - 'Omstreden'(Contentious)
> - 'Niet omstreden'(Not contentious)
> - 'Weet ik niet'(I don't know)
> - 'Onleesbare OCR'(Illegible OCR)</br>
2 open fields
> - 'Andere omstreden termen in de context'(Other contentious terms in the context)
> - 'Notities'(Notes)</br>
and the instructions in the header. The rows were the samples with the highlighted words, the tickboxes for every option, and 2 empty cells for the open questions. The obligatory part of the annotation was to select one of the 4 options for every sample. Finding other contentious terms in the given sample, leaving notes, and answering 4 additional open questions at the end of the task were optional. Based on the received feedback and the answers to the open questions in the pilot study, the following decisions were made regarding the next, experts' annotation stage:
> - The annotation layout was built in Google Forms as a questionnaire instead of the table layout in Google Sheets to make the data collection and analysis faster as the number of participants would increase;
> - The context window of 5 sentences per sample was found optimal;
> - The number of samples per annotator was increased to 50;
> - The option 'Omstreden' (Contentious) was changed to 'Omstreden naar huidige maatstaven' ('Contentious according to current standards') to clarify that annotators should judge contentiousness of the word's use in context from today's perspective;
> - The annotation instruction was edited to clarify 2 points: (1) that annotators while judging contentiousness should take into account not only a bolded word but also the context surrounding it, and (2) if a word seems even slightly contentious to an annotator, they should choose the option 'Omstreden naar huidige maatstaven' (Contentious according to current standards);
> - The non-required field for every sample 'Notities' (Notes) was removed as there was an open question at the end of the annotation, where participants could leave their comments;
> - Another open question was added at the end of the annotation asking how much time it took to complete the annotation.
#### Who are the annotators?
Volunteers and Expert annotators
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
## Accessing the annotations
Each example text has multiple annotations. These annotations may not always agree. There are various approaches one could take to calculate agreement, including a majority vote, rating some annotators more highly, or calculating a score based on the 'votes' of annotators. Since there are many ways of doing this, we have not implemented this as part of the dataset loading script.
An example of how one could generate an "OCR quality rating" based on the number of times an annotator labelled an example with `Illegible OCR`:
```python
from collections import Counter
def calculate_ocr_score(example):
annotator_responses = [response['response'] for response in example['annotator_responses_english']]
counts = Counter(annotator_responses)
bad_ocr_ratings = counts.get("Illegible OCR")
if bad_ocr_ratings is None:
bad_ocr_ratings = 0
return round(1 - bad_ocr_ratings/len(annotator_responses),3)
dataset = dataset.map(lambda example: {"ocr_score":calculate_ocr_score(example)})
```
To take the majority vote (or return a tie) based on whether a example is labelled contentious or not:
```python
def most_common_vote(example):
annotator_responses = [response['response'] for response in example['annotator_responses_english']]
counts = Counter(annotator_responses)
contentious_count = counts.get("Contentious according to current standards")
if not contentious_count:
contentious_count = 0
not_contentious_count = counts.get("Not contentious")
if not not_contentious_count:
not_contentious_count = 0
if contentious_count > not_contentious_count:
return "contentious"
if contentious_count < not_contentious_count:
return "not_contentious"
if contentious_count == not_contentious_count:
return "tied"
```
### Social Impact of Dataset
This dataset can be used to see how words change in meaning over time
### Discussion of Biases
> Due to the nature of the project, some examples used in this documentation may be shocking or offensive. They are provided only as an illustration or explanation of the resulting dataset and do not reflect the opinions of the project team or their organisations.
Since this project was explicitly created to help assess bias, it should be used primarily in the context of assess bias, and methods for detecting bias.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Cultural AI](https://github.com/cultural-ai)
### Licensing Information
CC-BY
### Citation Information
```
@misc{ContentiousContextsCorpus2021,
author = {Cultural AI},
title = {Contentious Contexts Corpus},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/cultural-ai/ConConCor}},
}
``` |
autoevaluate | null | null | null | false | 7 | false | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-9ce97676-11915596 | 2022-07-28T05:35:44.000Z | null | false | cde011e595294d34ae7c648fcf788b153e762256 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:kmfoda/booksum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-9ce97676-11915596/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- kmfoda/booksum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9
metrics: []
dataset_name: kmfoda/booksum
dataset_config: kmfoda--booksum
dataset_split: test
col_mapping:
text: chapter
target: summary_text
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-3fbf83bf-11925597 | 2022-07-28T05:57:02.000Z | null | false | 9ed5cb6a383d487c045f685388b32a12a5ad17c6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:kmfoda/booksum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-3fbf83bf-11925597/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- kmfoda/booksum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9
metrics: []
dataset_name: kmfoda/booksum
dataset_config: kmfoda--booksum
dataset_split: test
col_mapping:
text: chapter
target: summary_text
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
anzorq | null | null | null | false | 5 | false | anzorq/kbd_lat-ru | 2022-07-31T02:04:38.000Z | null | false | 7103492b78e16adacd7b2a3216f524265ae3d70c | [] | [
"language:kbd",
"language:ru",
"license:mit",
"tags:translation",
"source_datasets:original",
"multilinguality:multilingual",
"task_categories:translation",
"task_categories:text2text-generation",
"task_ids:translation",
"task_ids:text2text-generation"
] | https://huggingface.co/datasets/anzorq/kbd_lat-ru/resolve/main/README.md | ---
language:
- kbd
- ru
license:
- mit
tags:
- translation
pretty_name: Kbd Ru Translation
source_datasets:
- original
multilinguality:
- multilingual
task_categories:
- translation
- text2text-generation
task_ids:
- translation
- text2text-generation
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-billsum-18299d18-11955600 | 2022-07-27T10:17:44.000Z | null | false | 0342260156e61cc56a6f59314d0d5b036b985a39 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:billsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-billsum-18299d18-11955600/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- billsum
eval_info:
task: summarization
model: pszemraj/led-base-book-summary
metrics: []
dataset_name: billsum
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-base-book-summary
* Dataset: billsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-billsum-a6bd4aa5-11965601 | 2022-07-27T21:10:35.000Z | null | false | 3ab156a12e3f1fecc0271712a0709c4ff979715f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:billsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-billsum-a6bd4aa5-11965601/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- billsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-book-summary
metrics: []
dataset_name: billsum
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-base-16384-book-summary
* Dataset: billsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
hong | null | null | null | false | 1 | false | hong/FLO | 2022-07-27T04:05:59.000Z | null | false | 643d3b34887839055d1a1d41cee511eb2baaac31 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/hong/FLO/resolve/main/README.md | ---
license: afl-3.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-96a02c9c-11975602 | 2022-07-27T10:27:23.000Z | null | false | eae636f52231308429ea7b022850ba84f4cfd02b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-96a02c9c-11975602/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: nlpconnect/roberta-base-squad2-nq
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: nlpconnect/roberta-base-squad2-nq
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ankur310794](https://huggingface.co/ankur310794) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-ef91144d-11985603 | 2022-07-27T10:45:45.000Z | null | false | 201d9a9e3d04b1bc66894808a1699731e3d45c0b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-ef91144d-11985603/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: nlpconnect/roberta-base-squad2-nq
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: nlpconnect/roberta-base-squad2-nq
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ankur310794](https://huggingface.co/ankur310794) for evaluating this model. |
chintagunta85 | null | @article{smith2008overview,
title={Overview of BioCreative II gene mention recognition},
author={Smith, Larry and Tanabe, Lorraine K and nee Ando, Rie Johnson and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M and Ganchev, Kuzman and others},
journal={Genome biology},
volume={9},
number={S2},
pages={S2},
year={2008},
publisher={Springer}
} | Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop.
In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions.
A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721.
Here we present brief descriptions of all the methods used and a statistical analysis of the results.
We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible,
and furthermore that the best result makes use of the lowest scoring submissions.
For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/
The original dataset can be downloaded from: https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-ii-corpus/
This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll | false | 1 | false | chintagunta85/bc2gm_test | 2022-07-28T14:16:43.000Z | null | false | e24270fa1657929a060d81dc258fee812b3905f6 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/chintagunta85/bc2gm_test/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: Bc2GmCorpus
---
# Dataset Card for bc2gm_corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/spyysalo/bc2gm-corpus/)
- **Repository:** [Github](https://github.com/spyysalo/bc2gm-corpus/)
- **Paper:** [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@mahajandiwakar](https://github.com/mahajandiwakar) for adding this dataset.
|
prubach | null | null | null | false | 1 | false | prubach/knotprotSequences | 2022-07-27T14:59:51.000Z | null | false | 3575c59559542b22c2fdebcbfeac364b9b9e017c | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/prubach/knotprotSequences/resolve/main/README.md | ---
license: apache-2.0
---
|
moyix | null | null | null | false | 9 | false | moyix/debian_csrc | 2022-07-27T20:54:47.000Z | null | false | 1bca1af003ec196c15d46b370ee4241b26918666 | [] | [
"license:mit"
] | https://huggingface.co/datasets/moyix/debian_csrc/resolve/main/README.md | ---
license: mit
---
|
benfoley | null | null | null | false | 1 | false | benfoley/test-dataset | 2022-07-27T23:41:15.000Z | null | false | a125fdedddadfc82908c3000165134876eb6a090 | [] | [] | https://huggingface.co/datasets/benfoley/test-dataset/resolve/main/README.md | testing an audio dataset |
oisinoh | null | @ONLINE {beansdata,
author="Makerere AI Lab",
title="Bean disease dataset",
month="January",
year="2020",
url="https://github.com/AI-Lab-Makerere/ibean/"
} | Beans is a dataset of images of beans taken in the field using smartphone
cameras. It consists of 3 classes: 2 disease classes and the healthy class.
Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
by experts from the National Crops Resources Research Institute (NaCRRI) in
Uganda and collected by the Makerere AI research lab. | false | 1 | false | oisinoh/tomatos | 2022-07-28T01:12:09.000Z | null | false | 6af7a842f6fc38d0a5d963fd44deaf1681935819 | [] | [] | https://huggingface.co/datasets/oisinoh/tomatos/resolve/main/README.md | ---
viewer: true
--- |
commanderstrife | null | @inproceedings{kim2004introduction,
title={Introduction to the bio-entity recognition task at JNLPBA},
author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},
booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},
pages={70--75},
year={2004},
organization={Citeseer}
} | The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search
on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts
were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.
Among the classes, 36 terminal classes were used to annotate the GENIA corpus. | false | 1 | false | commanderstrife/jnlpba | 2022-07-28T06:46:36.000Z | null | false | 6d7d0e843d195bae3df7338b261551080ed395f2 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/commanderstrife/jnlpba/resolve/main/README.md | ---
license: apache-2.0
---
|
hong | null | null | null | false | 1 | false | hong/zoosdataset | 2022-07-28T05:21:23.000Z | null | false | 4c31442562033cbc26c7f3d86e5236d082ea6799 | [] | [] | https://huggingface.co/datasets/hong/zoosdataset/resolve/main/README.md | |
Slepp | null | null | null | false | 1 | false | Slepp/train | 2022-07-28T08:18:50.000Z | null | false | 586c8a9acf05865650594e634cb88ef3d4938136 | [] | [] | https://huggingface.co/datasets/Slepp/train/resolve/main/README.md | for trainninf
|
Slepp | null | null | null | false | 1 | false | Slepp/validation | 2022-07-28T08:01:43.000Z | null | false | f6f04d6b8f8df133c3aa570f81b395b0c99b9fe7 | [] | [] | https://huggingface.co/datasets/Slepp/validation/resolve/main/README.md | validation set |
actdan2016 | null | null | null | false | 1 | false | actdan2016/sample1 | 2022-08-29T02:12:39.000Z | redcaps | false | 09013b8be5f523de806f9c21c548d2d6e7d92a02 | [] | [
"arxiv:2111.11431",
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:image-to-text",
"task_ids:image-captioning"
] | https://huggingface.co/datasets/actdan2016/sample1/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: redcaps
pretty_name: RedCaps
---
# Dataset Card for RedCaps
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Information](#dataset-information)
- [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 Information
- **Path** [/home/daniel.baek/public/common/Data](/home/daniel.baek/public/common/Data)
- **Content type** image
- **Tag** sensor, common, ai, dataset
- **Description**
- **Homepage:** [RedCaps homepage](https://redcaps.xyz/)
- **Repository:** [RedCaps repository](https://github.com/redcaps-dataset/redcaps-downloader)
- **Paper:** [RedCaps: web-curated image-text data created by the people, for the people](https://arxiv.org/abs/2111.11431)
- **Leaderboard:**
- **Point of Contact:** [Karan Desai](mailto:kdexd@umich.edu)
### Dataset Summary
RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.
Images and captions from Reddit depict and describe a wide variety of objects and scenes.
The data is collected from a manually curated set of subreddits (350 total),
which give coarse image labels and allow steering of the dataset composition
without labeling individual instances. RedCaps data is created *by the people, for the people* – it contains everyday things that users like to share on social media, for example hobbies (r/crafts) and pets (r/shiba). Captions often contain specific and
fine-grained descriptions (northern cardinal, taj mahal). Subreddit names provide relevant image
labels (r/shiba) even when captions may not (mlem!), and sometimes may group many visually
unrelated images through a common semantic meaning (r/perfectfit).
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("red_caps", "rabbits_2017")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
Some image links point to more than one image. You can process and downloaded those as follows:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import os
import re
import urllib
import PIL.Image
import datasets
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"]))
return batch
def process_image_urls(batch):
processed_batch_image_urls = []
for image_url in batch["image_url"]:
processed_example_image_urls = []
image_url_splits = re.findall(r"http\S+", image_url)
for image_url_split in image_url_splits:
if "imgur" in image_url_split and "," in image_url_split:
for image_url_part in image_url_split.split(","):
if not image_url_part:
continue
image_url_part = image_url_part.strip()
root, ext = os.path.splitext(image_url_part)
if not root.startswith("http"):
root = "http://i.imgur.com/" + root
root = root.split("#")[0]
if not ext:
ext = ".jpg"
ext = re.split(r"[?%]", ext)[0]
image_url_part = root + ext
processed_example_image_urls.append(image_url_part)
else:
processed_example_image_urls.append(image_url_split)
processed_batch_image_urls.append(processed_example_image_urls)
batch["image_url"] = processed_batch_image_urls
return batch
dset = load_dataset("red_caps", "rabbits_2017")
dset = dset.map(process_image_urls, batched=True, num_proc=4)
features = dset["train"].features.copy()
features["image"] = datasets.Sequence(datasets.Image())
num_threads = 20
dset = dset.map(fetch_images, batched=True, batch_size=100, features=features, fn_kwargs={"num_threads": num_threads})
```
Note that in the above code, we use the `datasets.Sequence` feature to represent a list of images for the multi-image links.
### Supported Tasks and Leaderboards
From the paper:
> We have used our dataset to train deep neural networks that perform image captioning, and
that learn transferable visual representations for a variety of downstream visual recognition tasks
(image classification, object detection, instance segmentation).
> We anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks,
such as image or text retrieval or text-to-image synthesis.
### Languages
All of the subreddits in RedCaps use English as their primary language.
## Dataset Structure
### Data Instances
Each instance in RedCaps represents a single Reddit image post:
```
{
'image_id': 'bpzj7r',
'author': 'djasz1',
'image_url': 'https://i.redd.it/ho0wntksivy21.jpg',
'raw_caption': 'Found on a friend’s property in the Keys FL. She is now happily living in my house.',
'caption': 'found on a friend's property in the keys fl. she is now happily living in my house.', 'subreddit': 3,
'score': 72,
'created_utc': datetime.datetime(2019, 5, 18, 1, 36, 41),
'permalink': '/r/airplants/comments/bpzj7r/found_on_a_friends_property_in_the_keys_fl_she_is/', 'crosspost_parents': None
}
```
### Data Fields
- `image_id`: Unique alphanumeric ID of the image post (assigned by Reddit).
- `author`: Reddit username of the image post author.
- `image_url`: Static URL for downloading the image associated with the post.
- `raw_caption`: Textual description of the image, written by the post author.
- `caption`: Cleaned version of "raw_caption" by us (see Q35).
- `subreddit`: Name of subreddit where the post was submitted.
- `score`: Net upvotes (discounting downvotes) received by the image post. This field is equal to `None` if the image post is a crosspost.
- `created_utc`: Integer time epoch (in UTC) when the post was submitted to Reddit.
- `permalink`: Partial URL of the Reddit post (https://reddit.com/<permalink>).
- `crosspost_parents`: List of parent posts. This field is optional.
### Data Splits
All the data is contained in training set. The training set has nearly 12M (12,011,111) instances.
From the paper:
> We intend our dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Hence, all instances in our dataset would be used for training while
the validation split is derived from downstream task(s). If users require a validation split, we
recommend sampling it such that it follows the same subreddit distribution as entire dataset.
## Dataset Creation
### Curation Rationale
From the paper:
> Large datasets of image-text pairs are widely used for pre-training generic representations
that transfer to a variety of downstream vision and vision-and-language tasks. Existing public
datasets of this kind were curated from search engine results (SBU Captions [1]) or HTML
alt-text from arbitrary web pages (Conceptual Captions [2, 31]). They performed complex
data filtering to deal with noisy web data. Due to aggressive filtering, their data collection is
inefficient and diversity is artificially supressed. We argue that the quality of data depends on
its source, and the human intent behind its creation. In this work, we explore Reddit – a social
media platform, for curating high quality data. We introduce RedCaps – a large dataset of
12M image-text pairs from Reddit. While we expect the use-cases of RedCaps to be similar to
existing datasets, we discuss how Reddit as a data source leads to fast and lightweight collection,
better data quality, lets us easily steer the data distribution, and facilitates ethically responsible data curation.
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> **Data Collection Pipeline**
Reddit’s uniform structure allows us to parallelize data collection as independent tasks – each task
involves collecting posts submitted to a single subreddit in one year. Our collection pipeline has three steps: (1) subreddit selection, (2) image post filtering, and (3) caption cleaning.
**Step 1**. Subreddit selection: We collect data from a manually curated set of subreddits. Subreddits
have their own rules, community norms, and moderators so curating subreddits allows us to steer the
dataset’s composition without annotating individual instances. We select subreddits with a high volume of images posts, where images tend to be photographs (rather than memes, drawings, screenshots,
etc) and post titles tend to describe image content (rather than making jokes, political commentary,
etc). We do not select any NSFW, banned, or quarantined subreddits. We want to minimize the
number of people that appear in RedCaps, so we omit subreddits whose primary purpose is to share or
comment on images of people (such as celebrity pics or user selfies). We choose subreddits focused on
general photography (r/pics, r/itookapicture), animals (r/axolotls, r/birdsofprey, r/dachshund),
plants (r/roses, r/succulents), objects (r/classiccars, r/trains, r/mechanicalkeyboards), food
(r/steak, r/macarons), scenery (r/cityporn1
, r/desertporn), or activities (r/carpentry, r/kayaking).
In total we collect data from 350 subreddits; the full list can be found in Appendix A.
**Step 2**. Image post filtering: We use Pushshift [41] and Reddit [42, 43] APIs to download all image
posts submitted to our selected subreddits from 2008–2020. Posts are collected at least six months
after their creation to let upvotes stabilize. We only collect posts with images hosted on three domains:
Reddit (i.redd.it), Imgur (i.imgur.com), and Flickr (staticflickr.com). Some image posts contain
multiple images (gallery posts) – in this case we only collect the first image and associate it with
the caption. We discard posts with < 2 upvotes to avoid unappealing content, and we discard posts
marked NSFW (by their authors or subreddit moderators) to avoid pornographic or disturbing content.
**Step 3**. Caption cleaning: We expect Reddit post titles to be less noisy than other large-scale
sources of image captions such as alt-text [2, 31], so we apply minimal text cleaning. We lowercase
captions and use ftfy [44] to remove character accents, emojis, and non-latin characters, following
[29, 35, 36]. Then we apply simple pattern matching to discard all sub-strings enclosed in brackets
((.*), [.*]). These sub-strings usually give non-semantic information: original content tags [oc],
image resolutions (800x600 px), camera specs (shot with iPhone), self-promotion [Instagram:
@user], and other references (link in comments). Finally, like [31] we replace social media
handles (words starting with ‘@’) with a [USR] token to protect user privacy and reduce redundancy.
Due to such filtering, ≈12K (0.1%) captions in our dataset are empty strings. We do not discard them,
as subreddit names alone provide meaningful supervision. Unlike CC-3M or CC-12M that discard
captions without nouns or that don’t overlap image tags, we do not discard any instances in this step.
Through this pipeline, we collect 13.4M instances from 350 subreddits. Our collection pipeline is
less resource-intensive than existing datasets – we do not require webpage crawlers, search engines,
or large databases of indexed webpages. RedCaps is easily extensible in the future by selecting more
subreddits and collecting posts from future years. Next, we perform additional filtering to mitigate
user privacy risks and harmful stereotypes in RedCaps, resulting in final size of 12M instances.
#### Who are the source language producers?
Reddit is the singular data source for RedCaps.
### Annotations
#### Annotation process
The dataset is built using fully automatic data collection pipeline which doesn't require any human annotators.
#### Who are the annotators?
The annotation process doesn't require any human annotators.
### Personal and Sensitive Information
From the paper:
> **Does the dataset relate to people?**
The dataset pertains to people in that people wrote the captions and posted images to Reddit
that we curate in RedCaps. We made specific design choices while curating RedCaps to avoid
large quantities of images containing people:
(a) We collect data from manually curated subreddits in which most contain primarily pertains
to animals, objects, places, or activities. We exclude all subreddits whose primary purpose
is to share and describe images of people (such as celebrity photos or user selfies).
(b) We use an off-the-shelf face detector to find and remove images with potential presence of
human faces. We manually checked 50K random images in RedCaps (Q16) and found 79
images with identifiable human faces – the entire dataset may have ≈19K (0.15%) images
with identifiable people. Refer Section 2.2 in the main paper.
> **Is it possible to identify one or more natural persons, either directly or indirectly (i.e., in
combination with other data) from the dataset?**
Yes, all instances in RedCaps include Reddit usernames of their post authors. This could be
used to look up the Reddit user profile, and some Reddit users may have identifying information
in their profiles. Some images may contain human faces which could be identified by
appearance. However, note that all this information is already public on Reddit, and searching it
in RedCaps is no easier than searching directly on Reddit.
> **Were the individuals in question notified about the data collection?**
No. Reddit users are anonymous by default, and are not required to share their personal contact
information (email, phone numbers, etc.). Hence, the only way to notify the authors of RedCaps
image posts is by sending them private messages on Reddit. This is practically difficult to do
manually, and will be classified as spam and blocked by Reddit if attempted to programmatically
send a templated message to millions of users.
> **Did the individuals in question consent to the collection and use of their data?**
Users did not explicitly consent to the use of their data in our dataset. However, by uploading
their data on Reddit, they consent that it would appear on the Reddit plaform and will be
accessible via the official Reddit API (which we use to collect RedCaps).
> **If consent was obtained, were the consenting individuals provided with a mechanism to
revoke their consent in the future or for certain uses?**
Users have full control over the presence of their data in our dataset. If users wish to revoke
their consent, they can delete the underlying Reddit post – it will be automatically removed
dfrom RedCaps since we distributed images as URLs. Moreover, we provide an opt-out request
form on our dataset website for anybody to request removal of an individual instance if it is
potentially harmful (e.g. NSFW, violates privacy, harmful stereotypes, etc.).
## Considerations for Using the Data
### Social Impact of Dataset
From the paper:
> **Has an analysis of the potential impact of the dataset and its use on data subjects (e.g.,
a data protection impact analysis) been conducted?**
No.
### Discussion of Biases
From the paper:
> **Harmful Stereotypes**: Another concern with
Reddit data is that images or language may represent harmful stereotypes about gender, race, or other
characteristics of people [48, 49, 51]. We select only non-NSFW subreddits with active moderation
for collecting data. This stands in contrast to less curated uses of Reddit data, such as GPT-2 [35]
whose training data includes at least 63K documents from banned or quarantined subreddits which
may contain toxic language [53]. We attempt to further reduce harmful stereotypes in two ways:
> * **NSFW images**: We use the InceptionV3 [54] model from [55] to filter images detected as porn or hentai with confidence ≥ 0.9. Similar to face filtering, we estimated precision of our filtering and estimated amount of missed detections, shown in Table 1. The model detects 87K images with low
precision (∼1%) – most detections are non-NSFW images with pink and beige hues.
> * **Potentially derogatory language**: We filter instances whose captions contain words or phrases from a common blocklist [56]. It is important to note that such coarse filtering might suppress language from marginalized groups reclaiming slurs [51]; however, as RedCaps is not intended to describe people, we believe this is a pragmatic tradeoff to avoid propagating harmful labels.
> **Reddit demographics**: Reddit’s user demographics are not representative of the population at large.
Compared to US adults, Reddit users skew male (69% vs 49%), young (58% 18-29 years old vs
22%), college educated (36% vs 28%), and politically liberal (41% vs 25%) [57]. Reddit users
are predominantly white (63%) [57], and 49% of desktop traffic to Reddit comes from the United
States [58]. All of the subreddits in RedCaps use English as their primary language. Taken together,
these demographic biases likely also bias the types of objects and places that appear in images on
Reddit, and the language used to describe these images. We do not offer explicit countermeasures to
these biases, but users of RedCaps should keep in mind that size doesn’t guarantee diversity [51].
Subtler issues may also exist, such as imbalanced representation of demographic groups [59] or
gender bias in object co-occurrence [60] or language [61]. These are hard to control in internet
data, so we release RedCaps with explicit instructions on suitable use-cases; specifically requesting models not be trained to identify people, or make decisions that impact people. We document these instructions and other terms-of-use in a datasheet [45], provided in Appendix G.
> **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**
The scale of RedCaps means that we are unable to verify the contents of all images and
captions. However we have tried to minimize the possibility that RedCaps contains data that
might be offensive, insulting, threatening, or might cause anxiety via the following mitigations:
(a) We manually curate the set of subreddits from which to collect data; we only chose
subreddits that are not marked NSFW and which generally contain non-offensive content.
(b) Within our curated subreddits, we did not include any posts marked NSFW.
(c) We removed all instances whose captions contained any of the 400 potentially offensive
words or phrases. Refer Section 2.2 in the main paper.
(d) We remove all instances whose images were flagged NSFW by an off-the-shelf detector.
We manually checked 50K random images in RedCaps and found one image containing
nudity (exposed buttocks; no identifiable face). Refer Section 2.2 in the main paper
> **Does the dataset identify any subpopulations (e.g., by age, gender)?**
RedCaps does not explicitly identify any subpopulations. Since some images contain people
and captions are free-form natural language written by Reddit users, it is possible that some
captions may identify people appearing in individual images as part of a subpopulation.
> **Were any ethical review processes conducted (e.g., by an institutional review board)?**
We did not conduct a formal ethical review process via institutional review boards. However,
as described in Section 2.2 of the main paper and Q16 we employed several filtering mechanisms
to try and remove instances that could be problematic.
### Other Known Limitations
From the paper:
> **Are there any errors, sources of noise, or redundancies in the dataset?**
RedCaps is noisy by design since image-text pairs on the internet are noisy and unstructured.
Some instances may also have duplicate images and captions – Reddit users may have shared
the same image post in multiple subreddits. Such redundancies constitute a very small fraction
of the dataset, and should have almost no effect in training large-scale models.
> **Does the dataset contain data that might be considered confidential (e.g., data that is
protected by legal privilege or by doctor-patient confidentiality, data that includes the
content of individuals non-public communications)?**
No, the subreddits included in RedCaps do not cover topics that may be considered confidential. All posts were publicly shared on Reddit prior to inclusion in RedCaps.
## Additional Information
### Dataset Curators
From the paper:
> Four researchers at the University of Michigan (affiliated as of 2021) have created RedCaps:
Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson.
### Licensing Information
The image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (https://www.reddit.com/wiki/
api-terms) and users must comply with Reddit User Agreeement, Content Policy,
and Privacy Policy – all accessible at https://www.redditinc.com/policies.
From the paper:
> RedCaps should only be used for non-commercial research. RedCaps should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of RedCaps are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.
### Citation Information
```bibtex
@misc{desai2021redcaps,
title={RedCaps: web-curated image-text data created by the people, for the people},
author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},
year={2021},
eprint={2111.11431},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
gigant | null | null | null | false | 34 | false | gigant/oldbookillustrations_2 | 2022-08-03T17:35:37.000Z | null | false | e3d786d9d384232e7961c6303a9b5dba95ed8758 | [] | [
"annotations_creators:expert-generated",
"language:en",
"language:fr",
"language:de",
"language_creators:expert-generated",
"license:cc-by-nc-4.0",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"tags:lam",
"tags:1800-1900",
"task_categories:text-to-ima... | https://huggingface.co/datasets/gigant/oldbookillustrations_2/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
- fr
- de
language_creators:
- expert-generated
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: Old Book Illustrations
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- lam
- 1800-1900
task_categories:
- text-to-image
- image-to-text
- image-to-image
task_ids:
- image-captioning
---
# Dataset Card for Old Book Illustrations
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://www.oldbookillustrations.com/)**
### Dataset Summary
The Old Book Illustrations contains 4172 illustrations scanned from old books, this collection was collected & curated by the team of the website [Old Book Illustrations](https://www.oldbookillustrations.com/).
The webmaster of Old Book Illustrations kindly allowed us to scrap these information in order to create this dataset for the [BigLAM initiative](https://huggingface.co/biglam).
### Languages
The captions and descriptions are mostly in English but can contain some sentences from other languages such as French or German.
For instance you can find this description that contains a French sentence:
>The caption reads in the original French: Vue de l’aqueduc de Salones qui conduisait l’eau à Spalatro.
## Dataset Structure
Each row contains information gathered from the page of an illustration on the website [Old Book Illustrations](https://www.oldbookillustrations.com/). As of July 2022, there are 4172 illustrations in this dataset.
### Data Fields
* `rawscan`: the image as originally scanned from the book, without further processing
* `1600px`: the cleaned image, resized to a width of 1600 pixels (height can vary)
* `info_url`: URL to the illustration page on oldbookillustrations.com
* `ìnfo_src`: URL to an icon-sized version of the image
* `info_alt`: short description of the image
* `artist_name`: artist name
* `artist_date`: birth date of the artist
* `artist_countries`: list of the countries the artist is from
* `book_title`: original title of the book the illustration is extracted from
* `book_authors`: list of the authors of the book
* `book_publishers`: list of the publishers of the book
* `openlibrary-url`: URL to the openlibrary entry for the book
* `tags`: list of keywords for this illustration on oldbookillustrations.com
* `illustration_source_name`: list of the sources for this illustration
* `illustration_source_url`: list of the URL for these sources
* `illustration_subject`: category of the subject represented in the illustration
* `illustration_format`: category of the format of the illustration
* `image_title`: title of the image
* `image_caption`: caption of the image. Seems to be the caption that appears next to the image in the book, translated to English if in another language
* `image_description`: longer description of the image. If there is one, it also quotes the caption in the original language
* `rawscan_url`: URL to the rawscan image on oldbookillustration.com
* `1600px_url`: URL to the cleaned image on oldbookillustration.com
## Dataset Creation
### Curation Rationale
This collection was collected & curated by the team of the website [Old Book Illustrations](https://www.oldbookillustrations.com/).
This version contains all the data that was available on the website as of July 2022, but the website is being actively maintained so if you want more old book illustrations, make sure to check [Old Book Illustrations](https://www.oldbookillustrations.com/).
### Source Data
#### Initial Data Collection and Normalization
Initial data is gathered from the website [Old Book Illustrations](https://www.oldbookillustrations.com/). The sources of the illustration scans are specified for each entry in the columns `illustration_source_name` and `illustration_source_url`.
### Personal and Sensitive Information
The Old Book Illustrations' Terms and conditions reads:
>OBI [Old Book Illustrations] explores the art of book illustrations within boundaries defined by time and age, not by subject, treatment, or intent. This means that some illustrations might be deemed offensive, disturbing, misleading, or otherwise objectionable. We do not endorse views or opinions the Illustrations may express, neither do we guarantee that the information conveyed by any Illustration is accurate.
## Considerations for Using the Data
### Discussion of Biases
The Old Book Illustrations' Terms and conditions reads:
>OBI [Old Book Illustrations] explores the art of book illustrations within boundaries defined by time and age, not by subject, treatment, or intent. This means that some illustrations might be deemed offensive, disturbing, misleading, or otherwise objectionable. We do not endorse views or opinions the Illustrations may express, neither do we guarantee that the information conveyed by any Illustration is accurate.
## Additional Information
### Dataset Curators
The Old Book Illustrations collection is curated and maintained by the team of the [Old Book Illustrations website](https://www.oldbookillustrations.com/).
### Licensing Information
[Old Book Illustrations](https://www.oldbookillustrations.com/) website reads:
>We don’t limit the use of the illustrations available on our site, but we accept no responsibility regarding any problem, legal or otherwise, which might result from such use. More specifically, we leave it up to users to make sure that their project complies with the copyright laws of their country of residence. Text content (descriptions, translations, etc.) is published under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The Old Book Illustrations webmaster mentioned that most images are public domain in the US and Europe, but there can be some exceptions. An example are the illustrations from [*Early poems of William Morris*](https://www.oldbookillustrations.com/titles/early-poems-of-william-morris/) as the illustrator died 1955, so her work is not public domain in Europe as of 2022, or [*Under the hill*](https://www.oldbookillustrations.com/titles/under-the-hill/) which was published in the US in 1928 and therefore is not public domain there.
### Citation Information
```bibtex
@misc{old book illustrations_2007,
url={https://www.oldbookillustrations.com/},
journal={Old Book Illustrations}, year={2007}}
```
### Contributions
Thanks to [@gigant](https://huggingface.co/gigant) ([@giganttheo](https://github.com/giganttheo)) for adding this dataset. |
okite97 | null | null | null | false | 60 | false | okite97/news-data | 2022-08-25T10:36:01.000Z | null | false | 2c53f4b94137892d96c3bc4272028c3354c640a7 | [] | [
"annotations_creators:other",
"language:en",
"language_creators:found",
"license:afl-3.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:topic-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/okite97/news-data/resolve/main/README.md | ---
annotations_creators:
- other
language:
- 'en'
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: News Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- text-classification
task_ids:
- topic-classification
- multi-class-classification
---
# Dataset Card for news-data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Dataset Curators](#dataset-curators)
### Dataset Summary
The News Dataset is an English-language dataset containing just over 4k unique news articles scrapped from AriseTv- One of the most popular news television in Nigeria.
### Supported Tasks and Leaderboards
It supports news article classification into different categories.
### Languages
English
## Dataset Structure
### Data Instances
'''
{'Title': 'Nigeria: APC Yet to Zone Party Positions Ahead of Convention'
'Excerpt': 'The leadership of the All Progressives Congress (APC), has denied reports that it had zoned some party positions ahead of'
'Category': 'politics'
'labels': 2}
'''
### Data Fields
* Title: a string containing the title of a news title as shown
* Excerpt: a string containing a short extract from the body of the news
* Category: a string that tells the category of an example (string label)
* labels: integer telling the class of an example (label)
### Data Splits
| Dataset Split | Number of instances in split |
| ----------- | ----------- |
| Train | 4,594 |
| Paragraph | 811 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The code for the dataset creation at *https://github.com/chimaobi-okite/NLP-Projects-Competitions/blob/main/NewsCategorization/Data/NewsDataScraping.ipynb*. The examples were scrapped from
<https://www.arise.tv/>
### Annotations
#### Annotation process
The annotation is based on the news category in the [arisetv](https://www.arise.tv) website
#### Who are the annotators?
Journalists at arisetv
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that can classify news articles into categories.
This task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.
### Discussion of Biases
This data is biased towards news happenings in Nigeria but the model built using it can as well classify news from other parts of the world
with a slight degradation in performance.
### Dataset Curators
The dataset is created by people at arise but was scrapped by [@github-chimaobi-okite](https://github.com/chimaobi-okite/)
|
Toygar | null | null | null | false | 4 | false | Toygar/turkish-offensive-language-detection | 2022-10-21T12:14:32.000Z | null | false | 456e0e150f62c719cc837db79e50d5448b0c0bd7 | [] | [
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language:tr",
"license:cc-by-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"task_categories:text-classification",
"tags:offensive-language-classification"
] | https://huggingface.co/datasets/Toygar/turkish-offensive-language-detection/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
language:
- tr
license:
- cc-by-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
task_ids: []
pretty_name: Turkish Offensive Language Detection Dataset
tags:
- offensive-language-classification
---
# Dataset Summary
This dataset is enhanced version of existing offensive language studies. Existing studies are highly imbalanced, and solving this problem is too costly. To solve this, we proposed contextual data mining method for dataset augmentation. Our method is basically prevent us from retrieving random tweets and label individually. We can directly access almost exact hate related tweets and label them directly without any further human interaction in order to solve imbalanced label problem.
In addition, existing studies *(can be found at Reference section)* are merged to create even more comprehensive and robust dataset for Turkish offensive language detection task.
The file train.csv contains 42,398, test.csv contains 8,851, valid.csv contains 1,756 annotated tweets.
# Dataset Structure
A binary dataset with with (0) Not Offensive and (1) Offensive tweets.
### Task and Labels
Offensive language identification:
- (0) Not Offensive - Tweet does not contain offense or profanity.
- (1) Offensive - Tweet contains offensive language or a targeted (veiled or direct) offense
### Data Splits
| | train | test | dev |
|------:|:------|:-----|:-----|
| 0 (Not Offensive) | 22,589 | 4,436 | 1,402 |
| 1 (Offensive) | 19,809 | 4,415 | 354 |
### Citation Information
```
BibTeX will be provided after publication.
UBMK 2022 Paper: "Linguistic-based Data Augmentation Approach for Offensive Language Detection"
```
### Paper codes
https://github.com/toygarr/lingda
# References
We merged open-source offensive language dataset studies in Turkish to increase contextuality with existing data even more, before our method is applied.
- https://huggingface.co/datasets/offenseval2020_tr
- https://github.com/imayda/turkish-hate-speech-dataset-2
- https://www.kaggle.com/datasets/kbulutozler/5k-turkish-tweets-with-incivil-content
|
biglam | null | null | null | false | 1 | false | biglam/archives_parlementaires_revolution_francaise | 2022-09-05T11:53:04.000Z | null | false | 734a6f81948727f4a41a98aaac68a8dc7cd86cd8 | [] | [
"license:cc-by-4.0",
"language:fr"
] | https://huggingface.co/datasets/biglam/archives_parlementaires_revolution_francaise/resolve/main/README.md | ---
license: cc-by-4.0
language: fr
---
|
DFKI-SLT | null | @inproceedings{lauscher2018b,
title = {An argument-annotated corpus of scientific publications},
booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
publisher = {Association for Computational Linguistics},
author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
address = {Brussels, Belgium},
year = {2018},
pages = {40–46}
} | The SciArg dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing
fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific
publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of
scientific writing. | false | 1 | false | DFKI-SLT/sciarg | 2022-07-28T14:04:31.000Z | null | false | 15ba2479192e7cf974e4e295a7d721a650c06f03 | [] | [
"annotations_creators:expert-generated",
"language:en",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:dr inventor corpus",
"tags:argument mining",
"tags:scientific text",
"tags:relation extraction",
"tags:argumentative discourse u... | https://huggingface.co/datasets/DFKI-SLT/sciarg/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license: []
multilinguality:
- monolingual
pretty_name: SciArg
size_categories:
- 1K<n<10K
source_datasets:
- dr inventor corpus
tags:
- argument mining
- scientific text
- relation extraction
- argumentative discourse unit recognition
task_categories:
- token-classification
task_ids: []
---
# Dataset Card for "sciarg"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/anlausch/ArguminSci](https://github.com/anlausch/ArguminSci)
- **Repository:** [https://github.com/anlausch/ArguminSci](https://github.com/anlausch/ArguminSci)
- **Paper:** [An argument-annotated corpus of scientific publications](https://aclanthology.org/W18-5206.pdf)
- **Leaderboard:** [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)
### Dataset Summary
The SciArg dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing
fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific
publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of
scientific writing.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `document_id`: the base file name, e.g. "A28"
- `text`: the parsed text of the scientific publication in the XML format
- `text_bound_annotations`: span annotations that mark argumentative discourse units (ADUs). Each entry has the following fields: `offsets`, `text`, `type`, and `id`.
- `relations`: binary relation annotations that mark the argumentative relations that hold between a head and a tail ADU. Each entry has the following fields: `id`, `head`, `tail`, and `type` where `head` and `tail` each have the fields: `ref_id` and `role`.
### Data Splits
The dataset consists of a single `train` split that has 40 documents.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{lauscher2018b,
title = {An argument-annotated corpus of scientific publications},
booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
publisher = {Association for Computational Linguistics},
author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
address = {Brussels, Belgium},
year = {2018},
pages = {40–46}
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
vincentclaes | null | null | null | false | 1 | false | vincentclaes/emoji-predictor | 2022-09-20T14:38:38.000Z | null | false | 0af1841a59d37a07091ea69bce12947558fa4d55 | [] | [] | https://huggingface.co/datasets/vincentclaes/emoji-predictor/resolve/main/README.md | # Emoji Predictor
Dataset consists of raw tweets as text and an emoji as the label.
original dataset: https://huggingface.co/datasets/AlekseyDorkin/extended_tweet_emojis
- Fine-tuned model: https://huggingface.co/vincentclaes/emoji-predictor
- Try the model here: https://huggingface.co/spaces/vincentclaes/emoji-predictor |
ChristophSchuhmann | null | null | null | false | 1 | false | ChristophSchuhmann/LAION-5B-EN-Aesthetics-Subset_above_5.0 | 2022-07-28T16:08:42.000Z | null | false | 5794e0a3cecf4fd9a213b8077255cc792dbf4c17 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ChristophSchuhmann/LAION-5B-EN-Aesthetics-Subset_above_5.0/resolve/main/README.md | ---
license: apache-2.0
---
|
ChristophSchuhmann | null | null | null | false | 1 | false | ChristophSchuhmann/LAION-5B-EN-Aesthetics-Subset_above_6 | 2022-07-28T16:09:21.000Z | null | false | ab6c512f3f9f5573805b1246a2d9e79a9e9bf070 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ChristophSchuhmann/LAION-5B-EN-Aesthetics-Subset_above_6/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-ce219d86-12025605 | 2022-07-28T21:06:06.000Z | null | false | e81ff8291dc22db23b272e9a5c393d322e530891 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:Blaise-g/SumPubmed"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-ce219d86-12025605/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/SumPubmed
eval_info:
task: summarization
model: Blaise-g/led_finetuned_sumpubmed
metrics: ['bertscore']
dataset_name: Blaise-g/SumPubmed
dataset_config: Blaise-g--SumPubmed
dataset_split: test
col_mapping:
text: text
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Blaise-g/led_finetuned_sumpubmed
* Dataset: Blaise-g/SumPubmed
* Config: Blaise-g--SumPubmed
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-ca1f103f-12035606 | 2022-07-28T20:34:23.000Z | null | false | 49bca9d76447b7dbe452b2a8a4426155c28df4ba | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-ca1f103f-12035606/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: nbroad/longt5-base-global-mediasum
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: nbroad/longt5-base-global-mediasum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-20a28003-12045607 | 2022-07-28T20:27:48.000Z | null | false | 7b01ec427ea3d0e879e4e26ca3cdfa5ce6526ca9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-20a28003-12045607/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: nbroad/longt5-base-global-mediasum
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: nbroad/longt5-base-global-mediasum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
alkzar90 | null | null | null | false | 2 | false | alkzar90/croupier-mtg-dataset | 2022-08-02T01:41:48.000Z | null | false | 399ed23149edf1be91a18fd8e60e3fea25262dfc | [] | [
"annotations_creators:found",
"license:apache-2.0",
"size_categories:1K<n<10K",
"source_datasets:original",
"tags:mgt",
"tags:magic-card-game",
"tags:creature-dataset",
"task_categories:image-classification",
"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/alkzar90/croupier-mtg-dataset/resolve/main/README.md | ---
annotations_creators:
- found
language: []
language_creators: []
license:
- apache-2.0
multilinguality: []
pretty_name: 'Croupier: a Magic the Gathering creatures dataset'
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- mgt
- magic-card-game
- creature-dataset
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
## Dataset Description
- **Homepage:** the [Gatherer](https://gatherer.wizards.com/Pages/)
- **Repository:** https://github.com/alcazar90/croupier-mtg-dataset
### Dataset Summary
A card images dataset of 4 types of creatures from Magic the Gathering card game: elf, goblin, knight, and zombie.
## Dataset Creation
All card information from Magic the Gathering card game is public available from the
[Gatherer]( https://gatherer.wizards.com/Pages/) website, the official Magic Card Database. The dataset is just
a subset selection of 4 kind of creatures from the game. |
OATML-Markslab | null | null | null | false | 19,343 | false | OATML-Markslab/ProteinGym | 2022-07-29T00:12:02.000Z | null | false | 4075aa679683f3071d527283819637f3446ca488 | [] | [
"arxiv:2205.13760"
] | https://huggingface.co/datasets/OATML-Markslab/ProteinGym/resolve/main/README.md | ## ProteinGym benchmarks overview
ProteinGym is an extensive set of Deep Mutational Scanning (DMS) assays curated to enable thorough comparisons of various mutation effect predictors indifferent regimes. It is comprised of two benchmarks: 1) a substitution benchmark which consists of the experimental characterisation of ∼1.5M missense variants across 87 DMS assays 2) an indel benchmark that includes ∼300k mutants across 7 DMS assays.
Each processed file in each benchmark corresponds to a single DMS assay, and contains the following three variables:
1) mutant (str):
- for the substitution benchmark, it describes the set of substitutions to apply on the reference sequence to obtain the mutated sequence (eg., A1P:D2N implies the amino acid 'A' at position 1 should be replaced by 'P', and 'D' at position 2 should be replaced by 'N')
- for the indel benchmark, it corresponds to the full mutated sequence
2) DMS_score (float): corresponds to the experimental measurement in the DMS assay. Across all assays, the higher the DMS_score value, the higher the fitness of the mutated protein
3) DMS_score_bin (int): indicates whether the DMS_score is above the fitness cutoff (1 is fit, 0 is not fit)
Additionally, we provide two reference files (ProteinGym_reference_file_substitutions.csv and ProteinGym_reference_file_indels.csv) that give further details on each assay and contain in particular:
- The UniProt_ID of the corresponding protein, along with taxon and MSA depth category
- The target sequence (target_seq) used in the assay
- Details on how the DMS_score was created from the raw files and how it was binarized
## Reference
If you use ProteinGym in your work, please cite the following paper:
```
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D.S., Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
```
## Links
- Pre-print: https://arxiv.org/abs/2205.13760
- Code: https://github.com/OATML-Markslab/Tranception |
ICML2022 | null | null | null | false | 1 | false | ICML2022/ProteinGym | 2022-07-29T00:19:31.000Z | null | false | e936ae69e3c70ff651d47889a389de6f596863b2 | [] | [
"arxiv:2205.13760"
] | https://huggingface.co/datasets/ICML2022/ProteinGym/resolve/main/README.md | ## ProteinGym benchmarks overview
ProteinGym is an extensive set of Deep Mutational Scanning (DMS) assays curated to enable thorough comparisons of various mutation effect predictors indifferent regimes. It is comprised of two benchmarks: 1) a substitution benchmark which consists of the experimental characterisation of ∼1.5M missense variants across 87 DMS assays 2) an indel benchmark that includes ∼300k mutants across 7 DMS assays.
Each processed file in each benchmark corresponds to a single DMS assay, and contains the following three variables:
1) mutant (str):
- for the substitution benchmark, it describes the set of substitutions to apply on the reference sequence to obtain the mutated sequence (eg., A1P:D2N implies the amino acid 'A' at position 1 should be replaced by 'P', and 'D' at position 2 should be replaced by 'N')
- for the indel benchmark, it corresponds to the full mutated sequence
2) DMS_score (float): corresponds to the experimental measurement in the DMS assay. Across all assays, the higher the DMS_score value, the higher the fitness of the mutated protein
3) DMS_score_bin (int): indicates whether the DMS_score is above the fitness cutoff (1 is fit, 0 is not fit)
Additionally, we provide two reference files (ProteinGym_reference_file_substitutions.csv and ProteinGym_reference_file_indels.csv) that give further details on each assay and contain in particular:
- The UniProt_ID of the corresponding protein, along with taxon and MSA depth category
- The target sequence (target_seq) used in the assay
- Details on how the DMS_score was created from the raw files and how it was binarized
## Reference
If you use ProteinGym in your work, please cite the following paper:
```
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D.S., Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
```
## Links
- Pre-print: https://arxiv.org/abs/2205.13760
- Code: https://github.com/OATML-Markslab/Tranception
|
biglam | null | @dataset{clerice_thibault_2022_6827706,
author = {Clérice, Thibault},
title = {YALTAi: Tabular Dataset},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.6827706},
url = {https://doi.org/10.5281/zenodo.6827706}
} | Yalt AI Tabular Dataset | false | 3 | false | biglam/yalta_ai_tabular_dataset | 2022-10-23T21:56:38.000Z | null | false | 65d7baf884b0ca8c02ad1f678b83904ccc1d2062 | [] | [
"arxiv:2207.11230",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"license:cc-by-4.0",
"size_categories:n<1K",
"tags:manuscripts",
"tags:LAM",
"task_categories:object-detection"
] | https://huggingface.co/datasets/biglam/yalta_ai_tabular_dataset/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language: []
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality: []
pretty_name: YALTAi Tabular Dataset
size_categories:
- n<1K
source_datasets: []
tags:
- manuscripts
- LAM
task_categories:
- object-detection
task_ids: []
---
# YALTAi Tabular Dataset
## Table of Contents
- [YALTAi Tabular Dataset](#YALTAi-Tabular-Dataset)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://doi.org/10.5281/zenodo.6827706](https://doi.org/10.5281/zenodo.6827706)
- **Paper:** [https://arxiv.org/abs/2207.11230](https://arxiv.org/abs/2207.11230)
### Dataset Summary
This dataset contains a subset of data used in the paper [You Actually Look Twice At it (YALTAi): using an object detectionapproach instead of region segmentation within the Kraken engine](https://arxiv.org/abs/2207.11230). This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset covers pages with tabular information with the following objects "Header", "Col", "Marginal", "text".
### Supported Tasks and Leaderboards
- `object-detection`: This dataset can be used to train a model for object-detection on historic document images.
## Dataset Structure
This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.
- The first configuration, `YOLO`, uses the data's original format.
- The second configuration converts the YOLO format into a format which is closer to the `COCO` annotation format. This is done to make it easier to work with the `feature_extractor`s from the `Transformers` models for object detection, which expect data to be in a COCO style format.
### Data Instances
An example instance from the COCO config:
```
{'height': 2944,
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FA413CDA210>,
'image_id': 0,
'objects': [{'area': 435956,
'bbox': [0.0, 244.0, 1493.0, 292.0],
'category_id': 0,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 88234,
'bbox': [305.0, 127.0, 562.0, 157.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 5244,
'bbox': [1416.0, 196.0, 92.0, 57.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 5720,
'bbox': [1681.0, 182.0, 88.0, 65.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 374085,
'bbox': [0.0, 540.0, 163.0, 2295.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 577599,
'bbox': [104.0, 537.0, 253.0, 2283.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 598670,
'bbox': [304.0, 533.0, 262.0, 2285.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 56,
'bbox': [284.0, 539.0, 8.0, 7.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 1868412,
'bbox': [498.0, 513.0, 812.0, 2301.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 307800,
'bbox': [1250.0, 512.0, 135.0, 2280.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 494109,
'bbox': [1330.0, 503.0, 217.0, 2277.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 52,
'bbox': [1734.0, 1013.0, 4.0, 13.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 90666,
'bbox': [0.0, 1151.0, 54.0, 1679.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []}],
'width': 2064}
```
An example instance from the YOLO config:
``` python
{'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FAA140F2450>,
'objects': {'bbox': [[747, 390, 1493, 292],
[586, 206, 562, 157],
[1463, 225, 92, 57],
[1725, 215, 88, 65],
[80, 1688, 163, 2295],
[231, 1678, 253, 2283],
[435, 1675, 262, 2285],
[288, 543, 8, 7],
[905, 1663, 812, 2301],
[1318, 1653, 135, 2280],
[1439, 1642, 217, 2277],
[1737, 1019, 4, 13],
[26, 1991, 54, 1679]],
'label': [0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]}}
```
### Data Fields
The fields for the YOLO config:
- `image`: the image
- `objects`: the annotations which consist of:
- `bbox`: a list of bounding boxes for the image
- `label`: a list of labels for this image
The fields for the COCO config:
- `height`: height of the image
- `width`: width of the image
- `image`: image
- `image_id`: id for the image
- `objects`: annotations in COCO format, consisting of a list containing dictionaries with the following keys:
- `bbox`: bounding boxes for the images
- `category_id`: a label for the image
- `image_id`: id for the image
- `iscrowd`: COCO `iscrowd` flag
- `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
### Data Splits
The dataset contains a train, validation and test split with the following numbers per split:
| | train | validation | test |
|----------|-------|------------|------|
| examples | 196 | 22 | 135 |
## Dataset Creation
> [this] dataset was produced using a single source, the Lectaurep Repertoires dataset [Rostaing et al., 2021], which served as a basis for only the training and development split. The testset is composed of original data, from various documents, from the 17th century up to the early 20th with a single soldier war report. The test set is voluntarily very different and out of domain with column borders that are not drawn nor printed in certain cases, layout in some kind of masonry layout. p.8
.
### Curation Rationale
This dataset was created to produce a simplified version of the [Lectaurep Repertoires dataset](https://github.com/HTR-United/lectaurep-repertoires), which was found to contain:
> around 16 different ways to describe columns, from Col1 to Col7, the case-different col1-col7 and finally ColPair and ColOdd, which we all reduced to Col p.8
### Source Data
#### Initial Data Collection and Normalization
The LECTAUREP (LECTure Automatique de REPertoires) project, which began in 2018, is a joint initiative of the Minutier central des notaires de Paris, the National Archives and the
Minutier central des notaires de Paris of the National Archives, the [ALMAnaCH (Automatic Language Modeling and Analysis & Computational Humanities)](https://www.inria.fr/en/almanach) team at Inria and the EPHE (Ecole Pratique des Hautes Etudes), in partnership with the Ministry of Culture.
> The lectaurep-bronod corpus brings together 100 pages from the repertoire of Maître Louis Bronod (1719-1765), notary in Paris from December 13, 1719 to July 23, 1765. The pages concerned were written during the years 1742 to 1745.
#### Who are the source language producers?
[More information needed]
### Annotations
| | Train | Dev | Test | Total | Average area | Median area |
|----------|-------|-----|------|-------|--------------|-------------|
| Col | 724 | 105 | 829 | 1658 | 9.32 | 6.33 |
| Header | 103 | 15 | 42 | 160 | 6.78 | 7.10 |
| Marginal | 60 | 8 | 0 | 68 | 0.70 | 0.71 |
| Text | 13 | 5 | 0 | 18 | 0.01 | 0.00 |
| | | | - | | | |
#### Annotation process
[More information needed]
#### Who are the annotators?
[More information needed]
### Personal and Sensitive Information
This data does not contain information relating to living individuals.
## Considerations for Using the Data
### Social Impact of Dataset
A growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.
### Discussion of Biases
Historical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed.
### Other Known Limitations
[More information needed]
## Additional Information
### Dataset Curators
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```
@dataset{clerice_thibault_2022_6827706,
author = {Clérice, Thibault},
title = {YALTAi: Tabular Dataset},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.6827706},
url = {https://doi.org/10.5281/zenodo.6827706}
}
```
[](https://doi.org/10.5281/zenodo.6827706)
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
|
crazyofapple | null | null | null | false | 1 | false | crazyofapple/CME-Chinese | 2022-07-29T07:39:55.000Z | null | false | 3ab203bc05d2e413b5d7ac87c5329a18bb0539a9 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/crazyofapple/CME-Chinese/resolve/main/README.md | ---
license: apache-2.0
---
|
PaddlePaddle | null | null | Duconv is a chinese conversation dataset, designed to evaluate the dialogue models. | false | 7 | false | PaddlePaddle/duconv | 2022-07-29T11:44:00.000Z | null | false | 2080deae0c89256bb023ad321b453dec5971b61a | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/PaddlePaddle/duconv/resolve/main/README.md | ---
license: apache-2.0
---
|
awacke1 | null | null | null | false | 1 | false | awacke1/DNA-Aaron-C-Wacker-Open-Source-Genome-Project | 2022-07-29T16:50:05.000Z | null | false | a50258122840d6603aa487849c3bbc60514998fd | [] | [
"license:mit"
] | https://huggingface.co/datasets/awacke1/DNA-Aaron-C-Wacker-Open-Source-Genome-Project/resolve/main/README.md | ---
license: mit
---
|
pinecone | null | null | null | false | 1 | false | pinecone/dl-doc-search | 2022-07-29T18:39:12.000Z | null | false | 17a4a3f0eec731d9559d68707b3ce65bffc4bcf5 | [] | [] | https://huggingface.co/datasets/pinecone/dl-doc-search/resolve/main/README.md | language:
- en
language_creators:
- found
multilinguality:
- monolingual
pretty_name: hello
size_categories:
- '100K<n<1M |
LiptaphX | null | null | null | false | 1 | false | LiptaphX/deneme | 2022-07-29T21:33:01.000Z | null | false | 56834ba511d9eea394d1441de14c7da21bb23113 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/LiptaphX/deneme/resolve/main/README.md | ---
license: afl-3.0
---
|
carbon225 | null | null | null | false | 1 | false | carbon225/lichess-elite | 2022-07-31T19:41:07.000Z | null | false | 467c261e5016e4eede158b8f6cea7e0cbdb3f1ab | [] | [
"license:cc0-1.0"
] | https://huggingface.co/datasets/carbon225/lichess-elite/resolve/main/README.md | ---
license: cc0-1.0
---
|
thocheat | null | null | null | false | 1 | false | thocheat/vlsp | 2022-08-01T08:39:05.000Z | null | false | 285490f2389cc194eb763409721ef3cf6d8fb075 | [] | [
"license:other"
] | https://huggingface.co/datasets/thocheat/vlsp/resolve/main/README.md | ---
license: other
---
|
Yehor | null | null | null | false | 1 | false | Yehor/voa-uk-transcriptions | 2022-09-10T10:07:34.000Z | null | false | ec4e46722c866c0e0bf1ad561b7bb8a4a5068995 | [] | [
"language:uk",
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Yehor/voa-uk-transcriptions/resolve/main/README.md | ---
language:
- uk
license: cc-by-4.0
---
This repository contains transcriptions with other metadata for the VOA Ukrainian dataset (~398h).
Usage:
```python
from datasets import load_dataset
ds = load_dataset('Yehor/voa-uk-transcriptions', split='train')
for row in ds:
print(row['text'])
```
|
JetsonEarth | null | null | null | false | 1 | false | JetsonEarth/jet_funsd | 2022-07-30T14:49:35.000Z | null | false | 1c0214d65571139d86b310eadb2e6615be0df374 | [] | [] | https://huggingface.co/datasets/JetsonEarth/jet_funsd/resolve/main/README.md | FUNSD dataset |
JetsonEarth | null | null | null | false | 1 | false | JetsonEarth/jetson_funsd | 2022-07-30T15:28:55.000Z | null | false | 50b19f4267f1528ffa926fe0112935d5bdf17597 | [] | [] | https://huggingface.co/datasets/JetsonEarth/jetson_funsd/resolve/main/README.md | FUNSD |
jordiae | null | @inproceedings{10.1145/3520312.3534867,
author = {Armengol-Estap\'{e}, Jordi and Woodruff, Jackson and Brauckmann, Alexander and Magalh\~{a}es, Jos\'{e} Wesley de Souza and O'Boyle, Michael F. P.},
title = {ExeBench: An ML-Scale Dataset of Executable C Functions},
year = {2022},
isbn = {9781450392730},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3520312.3534867},
doi = {10.1145/3520312.3534867},
abstract = {Machine-learning promises to transform compilation and software engineering, yet is frequently limited by the scope of available datasets. In particular, there is a lack of runnable, real-world datasets required for a range of tasks ranging from neural program synthesis to machine learning-guided program optimization. We introduce a new dataset, ExeBench, which attempts to address this. It tackles two key issues with real-world code: references to external types and functions and scalable generation of IO examples. ExeBench is the first publicly available dataset that pairs real-world C code taken from GitHub with IO examples that allow these programs to be run. We develop a toolchain that scrapes GitHub, analyzes the code, and generates runnable snippets of code. We analyze our benchmark suite using several metrics, and show it is representative of real-world code. ExeBench contains 4.5M compilable and 700k executable C functions. This scale of executable, real functions will enable the next generation of machine learning-based programming tasks.},
booktitle = {Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming},
pages = {50–59},
numpages = {10},
keywords = {Code Dataset, Program Synthesis, Mining Software Repositories, C, Machine Learning for Code, Compilers},
location = {San Diego, CA, USA},
series = {MAPS 2022}
} | An ML-scale dataset of executable C functions | false | 228 | false | jordiae/exebench | 2022-10-10T11:35:30.000Z | null | false | b8a9882475b1a71dc05f4d4bf292bc7a60d3f175 | [] | [] | https://huggingface.co/datasets/jordiae/exebench/resolve/main/README.md | # ExeBench: an ML-scale dataset of executable C functions
ExeBench is a dataset of millions of C functions paired with dependencies and metadatada such that at least a subset of it can be executed with IO pairs. It is mainly inteded for machine learning applications but it is application-agnostic enough to have other usages.
Please read the paper for more information: https://dl.acm.org/doi/abs/10.1145/3520312.3534867.
Please see `examples/` in https://github.com/jordiae/exebench for examples.
## Usage
### Option 1: Using the helpers in this repo
```
git clone https://github.com/jordiae/exebench.git
cd exebench/
python -m venv venv
source venv/bin/activate
pip install -r requirements_examples.txt
PYTHONPATH="${PYTHONPATH}:${pwd}" python examples/basic.py
```
### Option 2: Directly using the Hugginface Datasets library
```
!pip install datasets zstandard
# Load dataset split. In this case, synthetic test split
dataset = load_dataset('jordiae/exebench', split='test_synth')
for e in dataset:
...
```
### Option 3: Directly download the dataset
Take a look at the files at: https://huggingface.co/datasets/jordiae/exebench/tree/main
The dataset consist of directories compressed with TAR. Inside each TAR, there is a series of jsonline files compressed with zstandard.
## Statistics and versions
This release corresponds to ExeBench v1.01, a version with some improvements with respect to the original one presented in the paper. The statistics and studies presented in the paper remain consistent with respect to the new ones. The final splits of the new version consist of the following functions:
```
train_not_compilable: 2.357M
train_synth_compilable: 2.308373M
train_real_compilable: 0.675074M
train_synth_simple_io: 0.550116M
train_real_simple_io: 0.043769M
train_synth_rich_io: 0.097250M
valid_synth: 5k
valid_real: 2.133k
test_synth: 5k
test_real: 2.134k
```
The original dataset (v1.00) with the exact same data studied in the paper can be accessed on request at: https://huggingface.co/datasets/jordiae/exebench_legacy (please reach out for access)
## License
All C functions keep the original license as per their original Github repository (available in the metadata). All ExeBench contributions (I/O examples, boilerplate to run functions, etc) are released with an MIT license.
## Citation
```
@inproceedings{10.1145/3520312.3534867,
author = {Armengol-Estap\'{e}, Jordi and Woodruff, Jackson and Brauckmann, Alexander and Magalh\~{a}es, Jos\'{e} Wesley de Souza and O'Boyle, Michael F. P.},
title = {ExeBench: An ML-Scale Dataset of Executable C Functions},
year = {2022},
isbn = {9781450392730},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3520312.3534867},
doi = {10.1145/3520312.3534867},
abstract = {Machine-learning promises to transform compilation and software engineering, yet is frequently limited by the scope of available datasets. In particular, there is a lack of runnable, real-world datasets required for a range of tasks ranging from neural program synthesis to machine learning-guided program optimization. We introduce a new dataset, ExeBench, which attempts to address this. It tackles two key issues with real-world code: references to external types and functions and scalable generation of IO examples. ExeBench is the first publicly available dataset that pairs real-world C code taken from GitHub with IO examples that allow these programs to be run. We develop a toolchain that scrapes GitHub, analyzes the code, and generates runnable snippets of code. We analyze our benchmark suite using several metrics, and show it is representative of real-world code. ExeBench contains 4.5M compilable and 700k executable C functions. This scale of executable, real functions will enable the next generation of machine learning-based programming tasks.},
booktitle = {Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming},
pages = {50–59},
numpages = {10},
keywords = {Code Dataset, Program Synthesis, Mining Software Repositories, C, Machine Learning for Code, Compilers},
location = {San Diego, CA, USA},
series = {MAPS 2022}
}
```
## Credits
We thank Anghabench authors for their type inference-based synthetic dependencies generation for C functions. This software, Psyche-C, can be found at: https://github.com/ltcmelo/psychec
## Contact
```
jordi.armengol.estape at ed.ac.uk
``` |
bigscience | null | @misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. | false | null | false | bigscience/xP3all | 2022-11-04T01:56:31.000Z | null | false | 867224acc89ef9d5dafa13194fb26b21adc4f4b1 | [] | [
"arxiv:2211.01786",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"language:fon",
"language:fr",
"lang... | https://huggingface.co/datasets/bigscience/xP3all/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: xP3
size_categories:
- 100M<n<1B
task_categories:
- other
---
# Dataset Card for xP3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigscience-workshop/xmtf
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co)
### Dataset Summary
> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility.
- **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3))
- **xP3 Dataset Family:**
<table>
<tr>
<th>Name</th>
<th>Explanation</th>
<th>Example models</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
<td>xP3 + our evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
<td></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
</tr>
</table>
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?",
"targets": "Yes"
}
```
### Data Fields
The data fields are the same among all splits:
- `inputs`: the natural language input fed to the model
- `targets`: the natural language target that the model has to generate
### Data Splits
The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage.
|Language|Kilobytes|%|Samples|%|
|--------|------:|-:|---:|-:|
|tw|106288|0.11|265071|0.33|
|bm|107056|0.11|265180|0.33|
|ak|108096|0.11|265071|0.33|
|ca|110608|0.11|271191|0.33|
|eu|113008|0.11|281199|0.35|
|fon|113072|0.11|265063|0.33|
|st|114080|0.11|265063|0.33|
|ki|115040|0.12|265180|0.33|
|tum|116032|0.12|265063|0.33|
|wo|122560|0.12|365063|0.45|
|ln|126304|0.13|365060|0.45|
|as|156256|0.16|265063|0.33|
|or|161472|0.16|265063|0.33|
|kn|165456|0.17|265063|0.33|
|ml|175040|0.18|265864|0.33|
|rn|192992|0.19|318189|0.39|
|nso|229712|0.23|915051|1.13|
|tn|235536|0.24|915054|1.13|
|lg|235936|0.24|915021|1.13|
|rw|249360|0.25|915043|1.13|
|ts|250256|0.25|915044|1.13|
|sn|252496|0.25|865056|1.07|
|xh|254672|0.26|915058|1.13|
|zu|263712|0.26|915061|1.13|
|ny|272128|0.27|915063|1.13|
|ig|325232|0.33|950097|1.17|
|yo|352784|0.35|918416|1.13|
|ne|393680|0.39|315754|0.39|
|pa|523248|0.52|339210|0.42|
|gu|560688|0.56|347499|0.43|
|sw|566656|0.57|1130481|1.4|
|mr|666240|0.67|417269|0.52|
|bn|832720|0.83|428843|0.53|
|ta|926912|0.93|415433|0.51|
|te|1343232|1.35|584590|0.72|
|ur|1918272|1.92|855756|1.06|
|vi|3102512|3.11|1672106|2.07|
|code|4330752|4.34|2707724|3.34|
|hi|4403568|4.41|1554667|1.92|
|zh|4599440|4.61|3589234|4.43|
|id|4612256|4.62|2643418|3.27|
|ar|4683456|4.69|2160181|2.67|
|fr|6591120|6.6|5316403|6.57|
|pt|6886800|6.9|3752156|4.63|
|es|8587920|8.6|5413205|6.69|
|en|39252528|39.33|32740750|40.44|
|total|99807184|100.0|80956089|100.0|
## Dataset Creation
### Source Data
#### Training datasets
- Code Miscellaneous
- [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex)
- [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus)
- [GreatCode](https://huggingface.co/datasets/great_code)
- [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes)
- Closed-book QA
- [Hotpot QA](https://huggingface.co/datasets/hotpot_qa)
- [Trivia QA](https://huggingface.co/datasets/trivia_qa)
- [Web Questions](https://huggingface.co/datasets/web_questions)
- [Wiki QA](https://huggingface.co/datasets/wiki_qa)
- Extractive QA
- [Adversarial QA](https://huggingface.co/datasets/adversarial_qa)
- [CMRC2018](https://huggingface.co/datasets/cmrc2018)
- [DRCD](https://huggingface.co/datasets/clue)
- [DuoRC](https://huggingface.co/datasets/duorc)
- [MLQA](https://huggingface.co/datasets/mlqa)
- [Quoref](https://huggingface.co/datasets/quoref)
- [ReCoRD](https://huggingface.co/datasets/super_glue)
- [ROPES](https://huggingface.co/datasets/ropes)
- [SQuAD v2](https://huggingface.co/datasets/squad_v2)
- [xQuAD](https://huggingface.co/datasets/xquad)
- TyDI QA
- [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary)
- [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp)
- Multiple-Choice QA
- [ARC](https://huggingface.co/datasets/ai2_arc)
- [C3](https://huggingface.co/datasets/c3)
- [CoS-E](https://huggingface.co/datasets/cos_e)
- [Cosmos](https://huggingface.co/datasets/cosmos)
- [DREAM](https://huggingface.co/datasets/dream)
- [MultiRC](https://huggingface.co/datasets/super_glue)
- [OpenBookQA](https://huggingface.co/datasets/openbookqa)
- [PiQA](https://huggingface.co/datasets/piqa)
- [QUAIL](https://huggingface.co/datasets/quail)
- [QuaRel](https://huggingface.co/datasets/quarel)
- [QuaRTz](https://huggingface.co/datasets/quartz)
- [QASC](https://huggingface.co/datasets/qasc)
- [RACE](https://huggingface.co/datasets/race)
- [SciQ](https://huggingface.co/datasets/sciq)
- [Social IQA](https://huggingface.co/datasets/social_i_qa)
- [Wiki Hop](https://huggingface.co/datasets/wiki_hop)
- [WiQA](https://huggingface.co/datasets/wiqa)
- Paraphrase Identification
- [MRPC](https://huggingface.co/datasets/super_glue)
- [PAWS](https://huggingface.co/datasets/paws)
- [PAWS-X](https://huggingface.co/datasets/paws-x)
- [QQP](https://huggingface.co/datasets/qqp)
- Program Synthesis
- [APPS](https://huggingface.co/datasets/codeparrot/apps)
- [CodeContests](https://huggingface.co/datasets/teven/code_contests)
- [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs)
- [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp)
- [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search)
- [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code)
- Structure-to-text
- [Common Gen](https://huggingface.co/datasets/common_gen)
- [Wiki Bio](https://huggingface.co/datasets/wiki_bio)
- Sentiment
- [Amazon](https://huggingface.co/datasets/amazon_polarity)
- [App Reviews](https://huggingface.co/datasets/app_reviews)
- [IMDB](https://huggingface.co/datasets/imdb)
- [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes)
- [Yelp](https://huggingface.co/datasets/yelp_review_full)
- Simplification
- [BiSECT](https://huggingface.co/datasets/GEM/BiSECT)
- Summarization
- [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail)
- [Gigaword](https://huggingface.co/datasets/gigaword)
- [MultiNews](https://huggingface.co/datasets/multi_news)
- [SamSum](https://huggingface.co/datasets/samsum)
- [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua)
- [XLSum](https://huggingface.co/datasets/GEM/xlsum)
- [XSum](https://huggingface.co/datasets/xsum)
- Topic Classification
- [AG News](https://huggingface.co/datasets/ag_news)
- [DBPedia](https://huggingface.co/datasets/dbpedia_14)
- [TNEWS](https://huggingface.co/datasets/clue)
- [TREC](https://huggingface.co/datasets/trec)
- [CSL](https://huggingface.co/datasets/clue)
- Translation
- [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200)
- [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt)
- Word Sense disambiguation
- [WiC](https://huggingface.co/datasets/super_glue)
- [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic)
#### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for HumanEval)
- Natural Language Inference
- [ANLI](https://huggingface.co/datasets/anli)
- [CB](https://huggingface.co/datasets/super_glue)
- [RTE](https://huggingface.co/datasets/super_glue)
- [XNLI](https://huggingface.co/datasets/xnli)
- Coreference Resolution
- [Winogrande](https://huggingface.co/datasets/winogrande)
- [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd)
- Program Synthesis
- [HumanEval](https://huggingface.co/datasets/openai_humaneval)
- Sentence Completion
- [COPA](https://huggingface.co/datasets/super_glue)
- [Story Cloze](https://huggingface.co/datasets/story_cloze)
- [XCOPA](https://huggingface.co/datasets/xcopa)
- [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze)
#### Additional [xP3all](https://huggingface.co/datasets/bigscience/xP3all) datasets
- Coreference Resolution
- - [WSC (Fixed)](https://huggingface.co/datasets/super_glue)
- Sentence Completion
- [HellaSwag](https://huggingface.co/datasets/hellaswag)
- Translation
- [MultiEurlex](https://huggingface.co/datasets/multi_eurlex)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset. |
alvations | null | null | null | false | 1 | false | alvations/greg-eval | 2022-07-31T21:42:32.000Z | null | false | 8eaa388a192aa57a7f0d34a8b3757c6a3d14b712 | [] | [
"license:cc0-1.0"
] | https://huggingface.co/datasets/alvations/greg-eval/resolve/main/README.md | ---
license: cc0-1.0
---
|
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