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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-ag_news-default-684001-14155939 | 2022-08-29T12:47:36.000Z | null | false | 269ed925eb51425013b692d0ac25ef66f51611d5 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:ag_news"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-ag_news-default-684001-14155939/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ag_news
eval_info:
task: multi_class_classification
model: mrm8488/bert-mini-finetuned-age_news-classification
metrics: []
dataset_name: ag_news
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: mrm8488/bert-mini-finetuned-age_news-classification
* Dataset: ag_news
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-boolq-default-049b58-14205948 | 2022-08-29T14:36:34.000Z | null | false | 01ca83ee3481af6129dca76258ee734f20013aa4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:boolq"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-boolq-default-049b58-14205948/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- boolq
eval_info:
task: natural_language_inference
model: andi611/distilbert-base-uncased-qa-boolq
metrics: []
dataset_name: boolq
dataset_config: default
dataset_split: validation
col_mapping:
text1: question
text2: passage
target: answer
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: andi611/distilbert-base-uncased-qa-boolq
* Dataset: boolq
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-emotion-default-63bd40-14245951 | 2022-08-29T16:05:35.000Z | null | false | b509d87f11b98dee9d10d6f037479b98824e9fbe | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-emotion-default-63bd40-14245951/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: bergum/xtremedistil-emotion
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: bergum/xtremedistil-emotion
* Dataset: emotion
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
munggok | null | \ | false | 1 | false | munggok/KoPI | 2022-08-30T19:42:36.000Z | oscar | false | d288945200ebed82f502b5695f50a7cec61f2e1e | [] | [
"license:cc",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"language:id",
"source_datasets:original",
"task_ids:language-modeling"
] | https://huggingface.co/datasets/munggok/KoPI/resolve/main/README.md | ---
license: cc
annotations_creators:
- no-annotation
language_creators:
- found
multilinguality:
- monolingual
language:
- id
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
paperswithcode_id: oscar
---
KoPI (Korpus Perayapan Indonesia) is Indonesian general corpora for sequence language modelling
Subset of KoPI corpora:
KoPI-CC + KoPI-CC-NEWS + KoPI-Mc4 + KoPI-Wiki + KoPI-Leipzig + KoPI-Paper | |
jonaskoenig | null | null | null | false | null | false | jonaskoenig/future_time_references | 2022-08-29T18:17:36.000Z | null | false | 1d6374d26b730848ac2e01cf6bbca222f6e973f1 | [] | [
"license:mit"
] | https://huggingface.co/datasets/jonaskoenig/future_time_references/resolve/main/README.md | ---
license: mit
---
|
mschi | null | null | null | false | 1 | false | mschi/blogspot_raw | 2022-09-13T08:48:23.000Z | null | false | 062592d41bbc04c0715c50f75184907f2adc70ca | [] | [
"language:en",
"language_creators:other",
"license:mit",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"tags:blogspot",
"tags:blogger",
"tags:texts",
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_categories:text-generati... | https://huggingface.co/datasets/mschi/blogspot_raw/resolve/main/README.md | ---
annotations_creators: []
language:
- en
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: Blogspot_raw_texts
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- blogspot
- blogger
- texts
task_categories:
- text-classification
- text-retrieval
- text-generation
- time-series-forecasting
task_ids: []
---
# Dataset Card for blogspot raw dataset
## 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
This dataset is a corpus of raw blogposts from [blogspot](https://blogger.com) mostly in the English language. It was obtained by scraping corpora of [webarchive](https://archive.org) and [commoncrawl](https://commoncrawl.org).
### Supported Tasks and Leaderboards
The dataset may be used for training language models or serve other research interests.
### Languages
Mostly English language, but some outliers may occur.
## Dataset Structure
[Distribution](https://huggingface.co/datasets/mschi/blogspot_raw/blob/main/blospot_comm_dist.png)
The distribution of the blog posts over time can be viewed at ./blogspot_dist_comm.png
### Data Instances
[More Information Needed]
### Data Fields
text: string
URL: string
date: string
comment: int
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
The dataset was constructed by utilizing the [WARC-dl pipeline](https://github.com/webis-de/web-archive-keras). It was executed on cluster architecture. The corpora of archive.org and commoncrawl.org contain WARC files that contain HTML which gets parsed by the pipeline. The pipeline extracts HTML from the WARC files and applies distributed filtering to efficiently filter for the desired content.
### Source Data
#### Initial Data Collection and Normalization
The corpora "corpus-commoncrawl-main-2022-05" and "corpus-iwo-internet-archive-wide00001" have been searched for the content present in this dataset.
Search terms have been inserted into the preciously mentioned pipeline to filter URLs for "blogspot.com" and characteristic timestamp information contained in the URL (e.g. "/01/2007"). The HTML documents were parsed for specific tags to obtain the timestamps. Further, the data was labeled with the "comment" label if there were some comment markers in the URL, indicating that the retrieved text is from the main text of a blog post or from the comments section. The texts are stored raw and no further processing has been done.
#### Who are the source language producers?
Since [blogspot](https://blogger.com) provides a high-level framework to allow people everywhere in the world to set up and maintain a blog, the producers of the texts may not be further specified.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Texts are raw and unfiltered, thus personal and sensitive information, as well as explicit language, may be present in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
The retrieval of the timestamps from the HTML documents was not 100% accurate, so a small proportion of wrong or nonsense timestamps can be present in the data. Also we can not guarantee the correctness of the timestamps as well as the "comment" labels.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was constructed during the course "Big Data and Language Technologies" of the Text Mining and Retrieval Group, Department of Computer Science at the University of Leipzig.
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@jonaskonig](https://github.com/jonaskonig), [@maschirmer](https://github.com/maschirmer) and [@1BlattPapier](https://github.com/1BlattPapier) for contributing.
|
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-anli-plain_text-c507f2-14355972 | 2022-08-29T20:25:09.000Z | null | false | c8b72f8c242a0d8e052de3041c50c5a5e8f2a38e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:anli"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-anli-plain_text-c507f2-14355972/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- anli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: anli
dataset_config: plain_text
dataset_split: test_r3
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: test_r3
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-anli-plain_text-1f482c-14395973 | 2022-08-29T20:37:44.000Z | null | false | 0aa1d1e2793c68feafc3ea0267ffbdbb6e145bd2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:anli"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-anli-plain_text-1f482c-14395973/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- anli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: anli
dataset_config: plain_text
dataset_split: test_r2
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: test_r2
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-anli-plain_text-dfb10f-14405974 | 2022-08-29T20:37:45.000Z | null | false | 53ffa6b0c5abc115794bc3ac6d4524487cf12499 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:anli"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-anli-plain_text-dfb10f-14405974/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- anli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: anli
dataset_config: plain_text
dataset_split: test_r1
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: test_r1
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-multi_nli-default-68c6a6-14415975 | 2022-08-29T20:51:17.000Z | null | false | 2b52953aaf495435ed9e0a4beeaf3190b7149f09 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:multi_nli"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-multi_nli-default-68c6a6-14415975/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- multi_nli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: multi_nli
dataset_config: default
dataset_split: validation_matched
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: multi_nli
* Config: default
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-multi_nli-default-4a02ee-14425976 | 2022-08-29T20:51:17.000Z | null | false | b6aeba317590bd7a8fb11ba1d41bbcb1788dd388 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:multi_nli"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-multi_nli-default-4a02ee-14425976/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- multi_nli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: multi_nli
dataset_config: default
dataset_split: validation_mismatched
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: multi_nli
* Config: default
* Split: validation_mismatched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MoritzLaurer](https://huggingface.co/MoritzLaurer) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-glue-mrpc-4a87ed-14445977 | 2022-08-30T02:40:01.000Z | null | false | 4d80aed9505bdcfd4f7bfa577c66467fb71db4c2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mrpc-4a87ed-14445977/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: Intel/roberta-base-mrpc
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: validation
col_mapping:
text1: sentence1
text2: sentence2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Intel/roberta-base-mrpc
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xinhe](https://huggingface.co/xinhe) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-mrpc-71a11b-14455978 | 2022-08-30T02:40:01.000Z | null | false | 19ab9a4e0a4ad3dce1adbc4f0e6595d7c9ebc0d9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mrpc-71a11b-14455978/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: Intel/bert-base-uncased-mrpc
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: validation
col_mapping:
text1: sentence1
text2: sentence2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Intel/bert-base-uncased-mrpc
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xinhe](https://huggingface.co/xinhe) for evaluating this model. |
Bingsu | null | null | null | false | 1 | false | Bingsu/national_library_of_korea_book_info | 2022-08-30T08:32:14.000Z | null | false | 64a374c848cde26885e77f50fa7de87d58697d5d | [] | [
"language:ko",
"license:other",
"multilinguality:monolingual",
"size_categories:1M<n<10M"
] | https://huggingface.co/datasets/Bingsu/national_library_of_korea_book_info/resolve/main/README.md | ---
language:
- ko
license:
- other
multilinguality:
- monolingual
pretty_name: national_library_of_korea_book_info
size_categories:
- 1M<n<10M
---
# national_library_of_korea_book_info
## Dataset Description
- **Homepage** [문화 빅데이터 플랫폼](https://www.culture.go.kr/bigdata/user/data_market/detail.do?id=63513d7b-9b87-4ec1-a398-0a18ecc45411)
- **Download Size** 759 MB
- **Generated Size** 2.33 GB
- **Total Size** 3.09 GB
국립중앙도서관에서 배포한, 국립중앙도서관에서 보관중인 도서 정보에 관한 데이터.
### License
other ([KOGL](https://www.kogl.or.kr/info/license.do#05-tab) (Korea Open Government License) Type-1)
- According to above KOGL, user can use public works freely and without fee regardless of its commercial use, and can change or modify to create secondary works when user complies with the terms provided as follows:
<details>
<summary>KOGL Type 1</summary>
1. Source Indication Liability
- Users who use public works shall indicate source or copyright as follows:
- EX : “000(public institution's name)'s public work is used according to KOGL”
- The link shall be provided when online hyperlink for the source website is available.
- Marking shall not be used to misguide the third party that the user is sponsored by public institution or user has a special relationship with public institutions.
2. Use Prohibited Information
- Personal information that is protected by Personal Information Protection Act, Promotion for Information Network Use and Information Protection Act, etc.
- Credit information protected by the Use and Protection of Credit Information Act, etc.
- Military secrets protected by Military Secret Protection Act, etc.
- Information that is the object of other rights such as trademark right, design right, design right or patent right, etc., or that is owned by third party's copyright.
- Other information that is use prohibited information according to other laws.
3. Public Institution's Liability Exemption
- Public institution does not guarantee the accuracy or continued service of public works.
- Public institution and its employees do not have any liability for any kind of damage or disadvantage that may arise by using public works.
4. Effect of Use Term Violation
- The use permission is automatically terminated when user violates any of the KOGL's Use Terms, and the user shall immediately stop using public works.
</details>
## Data Structure
### Data Instance
```python
>>> from datasets import load_dataset
>>>
>>> ds = load_dataset("Bingsu/national_library_of_korea_book_info", split="train")
>>> ds
Dataset({
features: ['isbn13', 'vol', 'title', 'author', 'publisher', 'price', 'img_url', 'description'],
num_rows: 7919278
})
```
```python
>>> ds.features
{'isbn13': Value(dtype='string', id=None),
'vol': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None),
'author': Value(dtype='string', id=None),
'publisher': Value(dtype='string', id=None),
'price': Value(dtype='string', id=None),
'img_url': Value(dtype='string', id=None),
'description': Value(dtype='string', id=None)}
```
or
```python
>>> import pandas as pd
>>>
>>> url = "https://huggingface.co/datasets/Bingsu/national_library_of_korea_book_info/resolve/main/train.csv.gz"
>>> df = pd.read_csv(url, low_memory=False)
```
```python
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7919278 entries, 0 to 7919277
Data columns (total 8 columns):
# Column Dtype
--- ------ -----
0 isbn13 object
1 vol object
2 title object
3 author object
4 publisher object
5 price object
6 img_url object
7 description object
dtypes: object(8)
memory usage: 483.4+ MB
```
### Null data
```python
>>> df.isnull().sum()
isbn13 3277
vol 5933882
title 19662
author 122998
publisher 1007553
price 3096535
img_url 3182882
description 4496194
dtype: int64
```
### Note
```python
>>> df[df["description"].str.contains("[해외주문원서]", regex=False) == True].head()["description"]
10773 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
95542 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
95543 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
96606 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
96678 [해외주문원서] 고객님의 요청으로 수입 주문하는 도서이므로, 주문취소 및 반품이 불...
Name: description, dtype: object
```
|
alexandrainst | null | # @InProceedings{huggingface:dataset,
# title = {ScandiQA: A Scandinavian Question Answering Dataset},
# author={Dan Saattrup Nielsen},
# year={2022}
# }
# | ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish
languages. All samples come from the Natural Questions (NQ) dataset, which is a large
question answering dataset from Google searches. The Scandinavian questions and answers
come from the MKQA dataset, where 10,000 NQ samples were manually translated into,
among others, Danish, Norwegian, and Swedish. However, this did not include a
translated context, hindering the training of extractive question answering models.
We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long
answers" from the NQ dataset, being the paragraph in which the answer was found, or
otherwise we extract the context by locating the paragraphs which have the largest
cosine similarity to the question, and which contains the desired answer.
Further, many answers in the MKQA dataset were "language normalised": for instance, all
date answers were converted to the format "YYYY-MM-DD", meaning that in most cases
these answers are not appearing in any paragraphs. We solve this by extending the MKQA
answers with plausible "answer candidates", being slight perturbations or translations
of the answer.
With the contexts extracted, we translated these to Danish, Swedish and Norwegian using
the DeepL translation service for Danish and Swedish, and the Google Translation
service for Norwegian. After translation we ensured that the Scandinavian answers do
indeed occur in the translated contexts.
As we are filtering the MKQA samples at both the "merging stage" and the "translation
stage", we are not able to fully convert the 10,000 samples to the Scandinavian
languages, and instead get roughly 8,000 samples per language. These have further been
split into a training, validation and test split, with the former two containing
roughly 750 samples. The splits have been created in such a way that the proportion of
samples without an answer is roughly the same in each split. | false | 518 | false | alexandrainst/scandiqa | 2022-11-01T11:12:10.000Z | null | false | c0f82536badc6d25932513fb8a314f167e65d77a | [] | [
"language:da",
"language:sv",
"language:no",
"license:cc-by-sa-4.0",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:mkqa",
"source_datasets:natural_questions",
"task_categories:question-answering",
"task_ids:extractive-qa"
] | https://huggingface.co/datasets/alexandrainst/scandiqa/resolve/main/README.md | ---
pretty_name: ScandiQA
language:
- da
- sv
- no
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- mkqa
- natural_questions
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for ScandiQA
## Dataset Description
- **Repository:** <https://github.com/alexandrainst/scandi-qa>
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
- **Size of downloaded dataset files:** 69 MB
- **Size of the generated dataset:** 67 MB
- **Total amount of disk used:** 136 MB
### Dataset Summary
ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish
languages. All samples come from the Natural Questions (NQ) dataset, which is a large
question answering dataset from Google searches. The Scandinavian questions and answers
come from the MKQA dataset, where 10,000 NQ samples were manually translated into,
among others, Danish, Norwegian, and Swedish. However, this did not include a
translated context, hindering the training of extractive question answering models.
We merged the NQ dataset with the MKQA dataset, and extracted contexts as either "long
answers" from the NQ dataset, being the paragraph in which the answer was found, or
otherwise we extract the context by locating the paragraphs which have the largest
cosine similarity to the question, and which contains the desired answer.
Further, many answers in the MKQA dataset were "language normalised": for instance, all
date answers were converted to the format "YYYY-MM-DD", meaning that in most cases
these answers are not appearing in any paragraphs. We solve this by extending the MKQA
answers with plausible "answer candidates", being slight perturbations or translations
of the answer.
With the contexts extracted, we translated these to Danish, Swedish and Norwegian using
the [DeepL translation service](https://www.deepl.com/pro-api?cta=header-pro-api) for
Danish and Swedish, and the [Google Translation
service](https://cloud.google.com/translate/docs/reference/rest/) for Norwegian. After
translation we ensured that the Scandinavian answers do indeed occur in the translated
contexts.
As we are filtering the MKQA samples at both the "merging stage" and the "translation
stage", we are not able to fully convert the 10,000 samples to the Scandinavian
languages, and instead get roughly 8,000 samples per language. These have further been
split into a training, validation and test split, with the latter two containing
roughly 750 samples. The splits have been created in such a way that the proportion of
samples without an answer is roughly the same in each split.
### Supported Tasks and Leaderboards
Training machine learning models for extractive question answering is the intended task
for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish (`da`), Swedish (`sv`) and Norwegian (`no`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 69 MB
- **Size of the generated dataset:** 67 MB
- **Total amount of disk used:** 136 MB
An example from the `train` split of the `da` subset looks as follows.
```
{
'example_id': 123,
'question': 'Er dette en test?',
'answer': 'Dette er en test',
'answer_start': 0,
'context': 'Dette er en testkontekst.',
'answer_en': 'This is a test',
'answer_start_en': 0,
'context_en': "This is a test context.",
'title_en': 'Train test'
}
```
### Data Fields
The data fields are the same among all splits.
- `example_id`: an `int64` feature.
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `answer_start`: an `int64` feature.
- `context`: a `string` feature.
- `answer_en`: a `string` feature.
- `answer_start_en`: an `int64` feature.
- `context_en`: a `string` feature.
- `title_en`: a `string` feature.
### Data Splits
| name | train | validation | test |
|----------|------:|-----------:|-----:|
| da | 6311 | 749 | 750 |
| sv | 6299 | 750 | 749 |
| no | 6314 | 749 | 750 |
## Dataset Creation
### Curation Rationale
The Scandinavian languages does not have any gold standard question answering dataset.
This is not quite gold standard, but the fact both the questions and answers are all
manually translated, it is a solid silver standard dataset.
### Source Data
The original data was collected from the [MKQA](https://github.com/apple/ml-mkqa/) and
[Natural Questions](https://ai.google.com/research/NaturalQuestions) datasets from
Apple and Google, respectively.
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) curated this dataset.
### Licensing Information
The dataset is licensed under the [CC BY-SA 4.0
license](https://creativecommons.org/licenses/by-sa/4.0/).
|
pokameswaran | null | null | null | false | 4 | false | pokameswaran/ami-6h | 2022-08-31T09:17:59.000Z | null | false | 7b18a94b22ed20a4651164cb34365c94f35441d0 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/pokameswaran/ami-6h/resolve/main/README.md | ---
license: cc-by-4.0
---
|
roskoN | null | \ | \ | false | 10 | false | roskoN/stereoset_german | 2022-08-30T14:53:55.000Z | null | false | d322d49c6234ed7c3fd867ef57a2aed1539a5b20 | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/roskoN/stereoset_german/resolve/main/README.md | ---
license: cc-by-sa-4.0
---
|
demo-org | null | null | null | false | 1 | false | demo-org/diabetes | 2022-08-30T21:08:59.000Z | null | false | 2c1e9e1a4deba071907e637095df2467c0c29472 | [] | [
"language:en",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification"
] | https://huggingface.co/datasets/demo-org/diabetes/resolve/main/README.md | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
paperswithcode_id: null
pretty_name: Diabetes
---
# Dataset Card for Auditor_Review
This file is a copy, the original version is hosted at [data.world](https://data.world/rshah/diabetes) |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-billsum-default-dd3eba-14585981 | 2022-08-31T07:44:21.000Z | null | false | 03a3c90f11ff6485cd4955a23f0a6e07b5158936 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:billsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-billsum-default-dd3eba-14585981/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- billsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2
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-booksum-V11-big_patent-V2
* 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 | null | false | autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625986 | 2022-09-01T10:02:44.000Z | null | false | c2248d5acd8782d3046775ac52db8eb3dad50305 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:billsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625986/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- billsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11
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-large-pubmed-3k-booksum-16384-WIP11
* 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 | null | false | autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625985 | 2022-09-01T04:09:46.000Z | null | false | f4f32ebb0db7da41e075f69405e7e396dd93d2d0 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:billsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625985/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- billsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
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-large-pubmed-3k-booksum-16384-WIP
* 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 | null | false | autoevaluate/autoeval-staging-eval-xsum-default-6f5db0-14615984 | 2022-09-01T13:24:17.000Z | null | false | 7977d7e4d2c8bd3f9da965a99d6057387f58875a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-xsum-default-6f5db0-14615984/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625987 | 2022-09-01T08:04:11.000Z | null | false | 0cd9acdb0ea6acb0442697499b54a323105dc95d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:billsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-billsum-default-3fec5f-14625987/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- billsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
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-large-pubmed-3k-booksum-16384-WIP13
* 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 | null | false | autoevaluate/autoeval-staging-eval-samsum-samsum-f593d1-14645991 | 2022-08-31T01:18:28.000Z | null | false | d51d497dbd52f384789619ba69627cd55541ecd9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-samsum-samsum-f593d1-14645991/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
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-large-pubmed-3k-booksum-16384-WIP11
* Dataset: samsum
* Config: samsum
* 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 | null | false | autoevaluate/autoeval-staging-eval-samsum-samsum-f593d1-14645992 | 2022-08-31T01:33:07.000Z | null | false | 22b0f359dc343c3842ae0b3b25410185a06dc368 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-samsum-samsum-f593d1-14645992/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
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-large-pubmed-3k-booksum-16384-WIP13
* Dataset: samsum
* Config: samsum
* 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. |
pixta-ai | null | null | null | false | null | false | pixta-ai/Plane-images-in-multiple-scenes | 2022-09-05T04:23:05.000Z | null | false | 626afad55214c9e1949031f8a19c13834f5b817f | [] | [] | https://huggingface.co/datasets/pixta-ai/Plane-images-in-multiple-scenes/resolve/main/README.md | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for pixta-ai/Plane-images-in-multiple-scenes
## Dataset Description
- **Homepage:** https://www.pixta.ai/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
4,000 Plane images in multiple scenes, including multiple types of planes disproportionately, the passenger plan are the majorities.
Each image contains from 1 to 10 visible planes
For more details, please refer to the link: https://www.pixta.ai/
Or send your inquiries to contact@pixta.ai
### Supported Tasks and Leaderboards
object-detection, computer-vision: The dataset can be used to train or enhance model for object detection.
### Languages
English
### License
Academic & commercial usage |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad-plain_text-d52fee-14655993 | 2022-08-31T06:45:10.000Z | null | false | 44488c9a08a774143dca37c60c28116c766e48fd | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad-plain_text-d52fee-14655993/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: mrp/bert-finetuned-squad
metrics: ['bleu', 'rouge']
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: mrp/bert-finetuned-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 [@saminaminaeheh](https://huggingface.co/saminaminaeheh) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14665994 | 2022-08-31T07:31:19.000Z | null | false | a8b2fb9790419752e26300ce37c9eabc36411bd4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14665994/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: sgugger/glue-mrpc
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: validation
col_mapping:
text1: sentence1
text2: sentence2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: sgugger/glue-mrpc
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14665997 | 2022-08-31T07:33:42.000Z | null | false | 1a3a5ca04db7486f9737e64f16c54c1d2b48fba4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14665997/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: Intel/camembert-base-mrpc
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: validation
col_mapping:
text1: sentence1
text2: sentence2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Intel/camembert-base-mrpc
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14666001 | 2022-08-31T07:36:29.000Z | null | false | 55730ed50204cd1be2d9f3d0f828b34a762f6ae9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mrpc-e15d1b-14666001/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: sgugger/bert-finetuned-mrpc
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: validation
col_mapping:
text1: sentence1
text2: sentence2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: sgugger/bert-finetuned-mrpc
* Dataset: glue
* Config: mrpc
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-qqp-c973af-14676003 | 2022-08-31T07:38:38.000Z | null | false | 09a1805befbcdb794978a12558e99ea3d8dd2cb1 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-qqp-c973af-14676003/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: Alireza1044/mobilebert_qqp
metrics: []
dataset_name: glue
dataset_config: qqp
dataset_split: validation
col_mapping:
text1: question1
text2: question2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Alireza1044/mobilebert_qqp
* Dataset: glue
* Config: qqp
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-qqp-c973af-14676011 | 2022-08-31T07:43:33.000Z | null | false | 0434b76db92af9825be658211a80b3ce2fcb41ba | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-qqp-c973af-14676011/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: gchhablani/bert-base-cased-finetuned-qqp
metrics: []
dataset_name: glue
dataset_config: qqp
dataset_split: validation
col_mapping:
text1: question1
text2: question2
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: gchhablani/bert-base-cased-finetuned-qqp
* Dataset: glue
* Config: qqp
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686015 | 2022-08-31T07:44:58.000Z | null | false | 0b092afb93ac87046ff0da854e0f025408b23915 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686015/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: Alireza1044/mobilebert_mnli
metrics: []
dataset_name: glue
dataset_config: mnli
dataset_split: validation_matched
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Alireza1044/mobilebert_mnli
* Dataset: glue
* Config: mnli
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686017 | 2022-08-31T07:48:14.000Z | null | false | 8d30d6afd086cb75a9a24e114001dcbadd64c5b4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686017/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: Jiva/xlm-roberta-large-it-mnli
metrics: []
dataset_name: glue
dataset_config: mnli
dataset_split: validation_matched
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: Jiva/xlm-roberta-large-it-mnli
* Dataset: glue
* Config: mnli
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686020 | 2022-08-31T07:51:25.000Z | null | false | b55cb6fad539ade72ccb0bf50f7cc661dc764116 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686020/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: nbhimte/tiny-bert-mnli-distilled
metrics: []
dataset_name: glue
dataset_config: mnli
dataset_split: validation_matched
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: nbhimte/tiny-bert-mnli-distilled
* Dataset: glue
* Config: mnli
* Split: validation_matched
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-glue-qnli-1747ab-14696022 | 2022-08-31T07:53:56.000Z | null | false | 2207c72eb1dfd42516e8bb8e8e428a1f15fc0f9e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-qnli-1747ab-14696022/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: JeremiahZ/roberta-base-qnli
metrics: []
dataset_name: glue
dataset_config: qnli
dataset_split: validation
col_mapping:
text1: question
text2: sentence
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: JeremiahZ/roberta-base-qnli
* Dataset: glue
* Config: qnli
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-a741994f-efcd-40c8-8652-be4f42ba26cd-31 | 2022-08-31T08:10:00.000Z | null | false | 7bcd8d67060c921ea89a52433ce80e7dc753784c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-a741994f-efcd-40c8-8652-be4f42ba26cd-31/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: autoevaluate/multi-class-classification
metrics: ['matthews_correlation']
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: autoevaluate/multi-class-classification
* Dataset: emotion
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-glue-qnli-1747ab-14696030 | 2022-08-31T08:10:09.000Z | null | false | 9ad5d61faaa69bf55d889259015496b6d39ea90a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-glue-qnli-1747ab-14696030/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: gchhablani/bert-base-cased-finetuned-qnli
metrics: []
dataset_name: glue
dataset_config: qnli
dataset_split: validation
col_mapping:
text1: question
text2: sentence
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: gchhablani/bert-base-cased-finetuned-qnli
* Dataset: glue
* Config: qnli
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
lucifertrj | null | null | null | false | null | false | lucifertrj/AnimeQuotes | 2022-08-31T11:03:26.000Z | null | false | adc9a8b5f8384baca023be9e41de453cdecb5c01 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/lucifertrj/AnimeQuotes/resolve/main/README.md | ---
license: apache-2.0
---
|
cakiki | null | null | null | false | 1 | false | cakiki/ORCAS | 2022-08-31T11:44:09.000Z | null | false | 17fac3405c9f2fd59b18ef5cbb6f73fede1f3c40 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/cakiki/ORCAS/resolve/main/README.md | ---
license: cc-by-4.0
--- |
khalidalt | null | null | null | false | 1 | false | khalidalt/SANAD | 2022-09-03T19:36:00.000Z | null | false | cc04efc6edd44fc890b7625b82e36e023a353c59 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/khalidalt/SANAD/resolve/main/README.md | ---
license: cc-by-4.0
---
# Dataset Card for SANAD
## 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://data.mendeley.com/datasets/57zpx667y9/2**
### Dataset Summary
SANAD Dataset is a large collection of Arabic news articles that can be used in different Arabic NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular news websites: AlKhaleej, AlArabiya and Akhbarona. All datasets have seven categories [Culture, Finance, Medical, Politics, Religion, Sports and Tech], except AlArabiya which doesn’t have [Religion]. SANAD contains a total number of 190k+ articles.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
license: cc-by-4.0
### Citation Information
```
@article{einea2019sanad,
title={Sanad: Single-label arabic news articles dataset for automatic text categorization},
author={Einea, Omar and Elnagar, Ashraf and Al Debsi, Ridhwan},
journal={Data in brief},
volume={25},
pages={104076},
year={2019},
publisher={Elsevier}
}
```
### Contributions
|
Ramamurthi | null | null | null | false | 1 | false | Ramamurthi/yelp_reviews_encoded_hidden_outputs | 2022-08-31T21:31:04.000Z | null | false | a9ce33acf817e1d68c82a1fd3ab615c3515f0852 | [] | [
"license:mit"
] | https://huggingface.co/datasets/Ramamurthi/yelp_reviews_encoded_hidden_outputs/resolve/main/README.md | ---
license: mit
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906065 | 2022-08-31T21:51:46.000Z | null | false | 78d2052bec6926a380c29fafca8557bced46ad43 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906065/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/tinyroberta-squad2
metrics: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906066 | 2022-08-31T21:52:06.000Z | null | false | ce4204c2bd9b8eb2d0872b9b0ea63f0200030771 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906066/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-base-squad2
metrics: ['bertscore']
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-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906067 | 2022-08-31T21:53:49.000Z | null | false | 101220450c4e9337566488a595372390246937c9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906067/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-large-squad2
metrics: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906068 | 2022-08-31T21:55:28.000Z | null | false | 72b9520267fa0633669d76cdf4968d6c25521b96 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906068/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/xlm-roberta-base-squad2
metrics: ['bertscore']
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
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906069 | 2022-08-31T21:57:53.000Z | null | false | cca945ceb6b114937af9e69853666dc3d12ef1c0 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906069/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/xlm-roberta-large-squad2
metrics: ['bertscore']
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-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906070 | 2022-08-31T21:57:24.000Z | null | false | b66f3c90f539de1eb33ae4b3b6e84c86e67d644a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906070/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-base-squad2-covid
metrics: ['bertscore']
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-base-squad2-covid
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906071 | 2022-08-31T21:58:49.000Z | null | false | ec55bc782a252819ffe12f8097640286e5130157 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906071/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-base-squad2-distilled
metrics: ['bertscore']
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-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906072 | 2022-08-31T22:01:20.000Z | null | false | 85b74f86f553a969c7d22d22ee177c07739ede2f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906072/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: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916074 | 2022-08-31T22:01:33.000Z | null | false | 6441cc0b487b62b88a44999da1d1a6df5051db1d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916074/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-base-cased-squad2
metrics: ['bertscore']
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-cased-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906073 | 2022-08-31T22:02:14.000Z | null | false | 9e7039c7a58178ec63a3938b449bbd35ebf912df | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906073/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepakvk/roberta-base-squad2-finetuned-squad
metrics: ['bertscore']
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: deepakvk/roberta-base-squad2-finetuned-squad
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916077 | 2022-08-31T22:04:31.000Z | null | false | 30825b4b2d8e9b5671ec15a8218bdda56f470b0b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916077/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: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916076 | 2022-08-31T22:06:09.000Z | null | false | ac5b4b0694f05ab94ed402208b645204dbc7f685 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916076/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: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916078 | 2022-08-31T22:10:07.000Z | null | false | 70eb6800ed3b65b6ef9c1b424928669979a9e322 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916078/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: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916080 | 2022-08-31T22:13:11.000Z | null | false | 4ae1a5e50013521e0d49bacbc0e4759230b2e0c7 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916080/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: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916075 | 2022-08-31T22:11:47.000Z | null | false | 2455dc91a08af79fa79ed41e9a60ceec159629c0 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916075/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: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916079 | 2022-08-31T22:14:24.000Z | null | false | e3290585c7c08b65826dbf628bb64eb9e3d60e92 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916079/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: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916081 | 2022-08-31T22:14:00.000Z | null | false | 6403e178c742dcd7c2b572e9e4df8f33577eb62d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916081/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/electra-base-squad2
metrics: ['bertscore']
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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916082 | 2022-08-31T22:15:10.000Z | null | false | 6ec84a0ec5da70e845deca75ffa6141a28839907 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916082/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/minilm-uncased-squad2
metrics: ['bertscore']
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/minilm-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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
evaluate | null | @inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
} | GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems. | false | 1 | false | evaluate/glue-ci | 2022-09-15T20:12:43.000Z | glue | false | ba06dc05a1b91c497f489bfa9793acdfb4ce06ec | [] | [
"annotations_creators:other",
"language_creators:other",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference... | https://huggingface.co/datasets/evaluate/glue-ci/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-scoring
- sentiment-classification
- text-classification-other-coreference-nli
- text-classification-other-paraphrase-identification
- text-classification-other-qa-nli
- text-scoring
paperswithcode_id: glue
pretty_name: GLUE (General Language Understanding Evaluation benchmark)
train-eval-index:
- config: cola
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
- config: sst2
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
- config: mrpc
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: qqp
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question1: text1
question2: text2
label: target
- config: stsb
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: mnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation_matched
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: mnli_mismatched
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: mnli_matched
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: qnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question: text1
sentence: text2
label: target
- config: rte
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: wnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
configs:
- ax
- cola
- mnli
- mnli_matched
- mnli_mismatched
- mrpc
- qnli
- qqp
- rte
- sst2
- stsb
- wnli
---
# Dataset Card for GLUE
## Table of Contents
- [Dataset Card for GLUE](#dataset-card-for-glue)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [ax](#ax)
- [cola](#cola)
- [mnli](#mnli)
- [mnli_matched](#mnli_matched)
- [mnli_mismatched](#mnli_mismatched)
- [mrpc](#mrpc)
- [qnli](#qnli)
- [qqp](#qqp)
- [rte](#rte)
- [sst2](#sst2)
- [stsb](#stsb)
- [wnli](#wnli)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [ax](#ax-1)
- [cola](#cola-1)
- [mnli](#mnli-1)
- [mnli_matched](#mnli_matched-1)
- [mnli_mismatched](#mnli_mismatched-1)
- [mrpc](#mrpc-1)
- [qnli](#qnli-1)
- [qqp](#qqp-1)
- [rte](#rte-1)
- [sst2](#sst2-1)
- [stsb](#stsb-1)
- [wnli](#wnli-1)
- [Data Fields](#data-fields)
- [ax](#ax-2)
- [cola](#cola-2)
- [mnli](#mnli-2)
- [mnli_matched](#mnli_matched-2)
- [mnli_mismatched](#mnli_mismatched-2)
- [mrpc](#mrpc-2)
- [qnli](#qnli-2)
- [qqp](#qqp-2)
- [rte](#rte-2)
- [sst2](#sst2-2)
- [stsb](#stsb-2)
- [wnli](#wnli-2)
- [Data Splits](#data-splits)
- [ax](#ax-3)
- [cola](#cola-3)
- [mnli](#mnli-3)
- [mnli_matched](#mnli_matched-3)
- [mnli_mismatched](#mnli_mismatched-3)
- [mrpc](#mrpc-3)
- [qnli](#qnli-3)
- [qqp](#qqp-3)
- [rte](#rte-3)
- [sst2](#sst2-3)
- [stsb](#stsb-3)
- [wnli](#wnli-3)
- [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://nyu-mll.github.io/CoLA/](https://nyu-mll.github.io/CoLA/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 955.33 MB
- **Size of the generated dataset:** 229.68 MB
- **Total amount of disk used:** 1185.01 MB
### Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
### Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found [at this address](https://gluebenchmark.com/). It comprises the following tasks:
#### ax
A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.
#### cola
The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.
#### mnli
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
#### mnli_matched
The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mnli_mismatched
The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mrpc
The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
#### qnli
The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.
#### qqp
The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.
#### rte
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.
#### sst2
The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.
#### stsb
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.
#### wnli
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).
### Languages
The language data in GLUE is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
#### ax
- **Size of downloaded dataset files:** 0.21 MB
- **Size of the generated dataset:** 0.23 MB
- **Total amount of disk used:** 0.44 MB
An example of 'test' looks as follows.
```
{
"premise": "The cat sat on the mat.",
"hypothesis": "The cat did not sit on the mat.",
"label": -1,
"idx: 0
}
```
#### cola
- **Size of downloaded dataset files:** 0.36 MB
- **Size of the generated dataset:** 0.58 MB
- **Total amount of disk used:** 0.94 MB
An example of 'train' looks as follows.
```
{
"sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
"label": 1,
"id": 0
}
```
#### mnli
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 78.65 MB
- **Total amount of disk used:** 376.95 MB
An example of 'train' looks as follows.
```
{
"premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
"hypothesis": "Product and geography are what make cream skimming work.",
"label": 1,
"idx": 0
}
```
#### mnli_matched
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 3.52 MB
- **Total amount of disk used:** 301.82 MB
An example of 'test' looks as follows.
```
{
"premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
"hypothesis": "Hierbas is a name worth looking out for.",
"label": -1,
"idx": 0
}
```
#### mnli_mismatched
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 3.73 MB
- **Total amount of disk used:** 302.02 MB
An example of 'test' looks as follows.
```
{
"premise": "What have you decided, what are you going to do?",
"hypothesis": "So what's your decision?,
"label": -1,
"idx": 0
}
```
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
#### ax
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### cola
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `unacceptable` (0), `acceptable` (1).
- `idx`: a `int32` feature.
#### mnli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_matched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_mismatched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Splits
#### ax
| |test|
|---|---:|
|ax |1104|
#### cola
| |train|validation|test|
|----|----:|---------:|---:|
|cola| 8551| 1043|1063|
#### mnli
| |train |validation_matched|validation_mismatched|test_matched|test_mismatched|
|----|-----:|-----------------:|--------------------:|-----------:|--------------:|
|mnli|392702| 9815| 9832| 9796| 9847|
#### mnli_matched
| |validation|test|
|------------|---------:|---:|
|mnli_matched| 9815|9796|
#### mnli_mismatched
| |validation|test|
|---------------|---------:|---:|
|mnli_mismatched| 9832|9847|
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
journal={arXiv preprint arXiv:1805.12471},
year={2018}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```
### Contributions
Thanks to [@patpizio](https://github.com/patpizio), [@jeswan](https://github.com/jeswan), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
|
EricPeter | null | null | null | false | 1 | false | EricPeter/comments | 2022-08-31T22:49:02.000Z | null | false | 7146c03d31dcc036af4e2b78631a3ba1bd10b883 | [] | [
"license:cc0-1.0"
] | https://huggingface.co/datasets/EricPeter/comments/resolve/main/README.md | ---
license: cc0-1.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c9381c-14936084 | 2022-08-31T23:16:12.000Z | null | false | fdf89d9ab61732bcb253768750a35dcf7bba9a9e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c9381c-14936084/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: ptnv-s/biobert_squad2_cased-finetuned-squad
metrics: ['bertscore']
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: ptnv-s/biobert_squad2_cased-finetuned-squad
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c9381c-14936085 | 2022-08-31T23:49:22.000Z | null | false | f73936e33d1c4ee021cb17b21e16ffff0ca95b80 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-c9381c-14936085/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: gerardozq/biobert_v1.1_pubmed-finetuned-squad
metrics: ['bertscore']
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: gerardozq/biobert_v1.1_pubmed-finetuned-squad
* 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 [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-cnn_dailymail-3.0.0-d7ce16-14946086 | 2022-09-01T01:06:48.000Z | null | false | d5bf79983aff9a4a44953c5edf97a05393c8ab58 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-cnn_dailymail-3.0.0-d7ce16-14946086/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: ['mse']
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: facebook/bart-large-cnn
* 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 [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model. |
Chr0my | null | null | null | false | 2 | false | Chr0my/Epidemic_sounds | 2022-09-01T01:19:57.000Z | null | false | 8591fdc2d9f94cfcd336feedb3002b0fdbc1f3d8 | [] | [
"license:mit"
] | https://huggingface.co/datasets/Chr0my/Epidemic_sounds/resolve/main/README.md | ---
license: mit
---
|
ElKulako | null | null | null | false | 1 | false | ElKulako/cryptobert-posttrain | 2022-09-01T04:22:42.000Z | null | false | 19c35918209a49548c54478695bbe6b8f0dc758e | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/ElKulako/cryptobert-posttrain/resolve/main/README.md | ---
license: afl-3.0
---
This is the dataset used to post-train the [BERTweet](https://huggingface.co/cardiffnlp/twitter-roberta-base) language model on a Masked Language Modeling (MLM) task, resulting in the [CryptoBERT](https://huggingface.co/ElKulako/cryptobert) language model.
The dataset contains 3.207 million unique posts from the language domain of cryptocurrency-related social media text.
The dataset contains 1.865 million StockTwits posts, 496 thousand tweets, 172 thousand Reddit comments and 664 thousand Telegram messages. |
nanom | null | @dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
} | null | false | 1 | false | nanom/spanish3bwc | 2022-09-05T21:03:49.000Z | null | false | 2eb9b928c3a7cd9a92918353e0453b2c9d1a512a | [] | [
"language:es",
"multilinguality:monolingual",
"license:mit"
] | https://huggingface.co/datasets/nanom/spanish3bwc/resolve/main/README.md | ---
language:
- 'es'
multilinguality:
- monolingual
pretty_name: "Unannotated Spanish 3 Billion Words Corpora"
license:
- mit
---
# Dataset Card for Unannotated Spanish 3 Billion Words Corpora
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Source Data](#source-data)
- [Data Subset](#data-subset)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/josecannete/spanish-corpora
- **Paper:** https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf
### Dataset Summary
* Number of lines: 300904000 (300M)
* Number of tokens: 2996016962 (3B)
* Number of chars: 18431160978 (18.4B)
### Languages
* Spanish
### Source Data
* Available to download here: [Zenodo](https://doi.org/10.5281/zenodo.3247731)
### Data Subset
* Spanish Wikis: Wich include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (https://github.com/josecannete/wikiextractorforBERT) using the wikis dump of 20/04/2019.
* ParaCrawl: Spanish portion of ParaCrawl (http://opus.nlpl.eu/ParaCrawl.php)
* EUBookshop: Spanish portion of EUBookshop (http://opus.nlpl.eu/EUbookshop.php)
* MultiUN: Spanish portion of MultiUN (http://opus.nlpl.eu/MultiUN.php)
* OpenSubtitles: Spanish portion of OpenSubtitles2018 (http://opus.nlpl.eu/OpenSubtitles-v2018.php)
* DGC: Spanish portion of DGT (http://opus.nlpl.eu/DGT.php)
* DOGC: Spanish portion of DOGC (http://opus.nlpl.eu/DOGC.php)
* ECB: Spanish portion of ECB (http://opus.nlpl.eu/ECB.php)
* EMEA: Spanish portion of EMEA (http://opus.nlpl.eu/EMEA.php)
* Europarl: Spanish portion of Europarl (http://opus.nlpl.eu/Europarl.php)
* GlobalVoices: Spanish portion of GlobalVoices (http://opus.nlpl.eu/GlobalVoices.php)
* JRC: Spanish portion of JRC (http://opus.nlpl.eu/JRC-Acquis.php)
* News-Commentary11: Spanish portion of NCv11 (http://opus.nlpl.eu/News-Commentary-v11.php)
* TED: Spanish portion of TED (http://opus.nlpl.eu/TED2013.php)
* UN: Spanish portion of UN (http://opus.nlpl.eu/UN.php)
## Additional Information
### Licensing Information
* [MIT Licence](https://github.com/josecannete/spanish-corpora/blob/master/LICENSE)
### Citation Information
```
@dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
}
@inproceedings{CaneteCFP2020,
title={Spanish Pre-Trained BERT Model and Evaluation Data},
author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge},
booktitle={PML4DC at ICLR 2020},
year={2020}
}
``` |
Exterus | null | null | null | false | 1 | false | Exterus/Language | 2022-09-01T12:33:41.000Z | null | false | 6c66817025509e853c1c7f3ea268f9fed96e240c | [] | [
"license:other"
] | https://huggingface.co/datasets/Exterus/Language/resolve/main/README.md | ---
license: other
---
|
mteb | null | @article{muennighoff2022mteb,
doi = {10.48550/ARXIV.2210.07316},
url = {https://arxiv.org/abs/2210.07316},
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
} | Results on MTEB | false | 847 | false | mteb/results | 2022-11-05T16:41:14.000Z | null | false | 5739c153726064a641b8f92526059e633a0841bd | [] | [
"benchmark:mteb",
"type:evaluation",
"submission_name:MTEB"
] | https://huggingface.co/datasets/mteb/results/resolve/main/README.md | ---
benchmark: mteb
type: evaluation
submission_name: MTEB
--- |
climatebert | null | null | null | false | 16 | false | climatebert/environmental_claims | 2022-09-02T09:12:00.000Z | null | false | 7752b2a4fa0fcfe4529e1ff76d6be5db2c8637ce | [] | [
"arxiv:2209.00507",
"license:cc-by-nc-sa-4.0",
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:text-classification"
] | https://huggingface.co/datasets/climatebert/environmental_claims/resolve/main/README.md | ---
license: cc-by-nc-sa-4.0
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-classification
pretty_name: EnvironmentalClaims
---
# Dataset Card for environmental_claims
## Dataset Description
- **Homepage:** [climatebert.ai](https://climatebert.ai)
- **Repository:**
- **Paper:** [arxiv.org/abs/2209.00507](https://arxiv.org/abs/2209.00507)
- **Leaderboard:**
- **Point of Contact:** [Dominik Stammbach](mailto:dominsta@ethz.ch)
### Dataset Summary
We introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies.
### Supported Tasks and Leaderboards
The dataset supports a binary classification task of whether a given sentence is an environmental claim or not.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
```
{
"text": "It will enable E.ON to acquire and leverage a comprehensive understanding of the transfor- mation of the energy system and the interplay between the individual submarkets in regional and local energy supply sys- tems.",
"label": 0
}
```
### Data Fields
- text: a sentence extracted from corporate annual reports, sustainability reports and earning calls transcripts
- label: the label (0 -> no environmental claim, 1 -> environmental claim)
### Data Splits
The dataset is split into:
- train: 2,400
- validation: 300
- test: 300
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Our dataset contains environmental claims by firms, often in the financial domain. We collect text from corporate annual reports, sustainability reports, and earning calls transcripts.
For more information regarding our sample selection, please refer to Appendix B of our paper, which is provided for [citation](#citation-information).
#### Who are the source language producers?
Mainly large listed companies.
### Annotations
#### Annotation process
For more information on our annotation process and annotation guidelines, please refer to Appendix C of our paper, which is provided for [citation](#citation-information).
#### Who are the annotators?
The authors and students at University of Zurich with majors in finance and sustainable finance.
### Personal and Sensitive Information
Since our text sources contain public information, no personal and sensitive information should be included.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- Dominik Stammbach
- Nicolas Webersinke
- Julia Anna Bingler
- Mathias Kraus
- Markus Leippold
### Licensing Information
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
If you are interested in commercial use of the dataset, please contact the ClimateBert team at [hello@climatebert.ai](mailto:hello@climatebert.ai).
### Citation Information
```bibtex
@misc{stammbach2022environmentalclaims,
title = {A Dataset for Detecting Real-World Environmental Claims},
author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus},
year = {2022},
doi = {10.48550/ARXIV.2209.00507},
url = {https://arxiv.org/abs/2209.00507},
publisher = {arXiv},
}
```
### Contributions
Thanks to [@webersni](https://github.com/webersni) for adding this dataset. |
cardiffnlp | null | @inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
} | [TweetTopic](https://arxiv.org/abs/2209.09824) | false | 326 | false | cardiffnlp/tweet_topic_multi | 2022-09-30T11:53:20.000Z | null | false | 8b720cddf2bde9b9201225d2675a467cb3d9e6d7 | [] | [
"arxiv:2209.09824",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1k<10K",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/resolve/main/README.md | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: TweetTopicSingle
---
# Dataset Card for "cardiffnlp/tweet_topic_multi"
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824)
- **Dataset:** Tweet Topic Dataset
- **Domain:** Twitter
- **Number of Class:** 19
### Dataset Summary
This is the official repository of TweetTopic (["Twitter Topic Classification
, COLING main conference 2022"](https://arxiv.org/abs/2209.09824)), a topic classification dataset on Twitter with 19 labels.
Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
See [cardiffnlp/tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single) for single label version of TweetTopic.
The tweet collection used in TweetTopic is same as what used in [TweetNER7](https://huggingface.co/datasets/tner/tweetner7).
The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.
### Preprocessing
We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
For verified usernames, we replace its display name (or account name) with symbols `{@}`.
For example, a tweet
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from @herbiehancock
via @bluenoterecords link below:
http://bluenote.lnk.to/AlbumOfTheWeek
```
is transformed into the following text.
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from {@herbiehancock@}
via {@bluenoterecords@} link below: {{URL}}
```
A simple function to format tweet follows below.
```python
import re
from urlextract import URLExtract
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
target_format = format_tweet(target)
print(target_format)
'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
```
### Data Splits
| split | number of texts | description |
|:------------------------|-----:|------:|
| test_2020 | 573 | test dataset from September 2019 to August 2020 |
| test_2021 | 1679 | test dataset from September 2020 to August 2021 |
| train_2020 | 4585 | training dataset from September 2019 to August 2020 |
| train_2021 | 1505 | training dataset from September 2020 to August 2021 |
| train_all | 6090 | combined training dataset of `train_2020` and `train_2021` |
| validation_2020 | 573 | validation dataset from September 2019 to August 2020 |
| validation_2021 | 188 | validation dataset from September 2020 to August 2021 |
| train_random | 4564 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
| validation_random | 573 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
| test_coling2022_random | 5536 | random split used in the COLING 2022 paper |
| train_coling2022_random | 5731 | random split used in the COLING 2022 paper |
| test_coling2022 | 5536 | temporal split used in the COLING 2022 paper |
| train_coling2022 | 5731 | temporal split used in the COLING 2022 paper |
For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `train_coling2022` and `test_coling2022` for temporal-shift, and `train_coling2022_random` and `test_coling2022_random` fir random split (the coling2022 split does not have validation set).
### Models
| model | training data | F1 | F1 (macro) | Accuracy |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|---------:|-------------:|-----------:|
| [cardiffnlp/roberta-large-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-multi-all) | all (2020 + 2021) | 0.763104 | 0.620257 | 0.536629 |
| [cardiffnlp/roberta-base-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-multi-all) | all (2020 + 2021) | 0.751814 | 0.600782 | 0.531864 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-all) | all (2020 + 2021) | 0.762513 | 0.603533 | 0.547945 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all) | all (2020 + 2021) | 0.759917 | 0.59901 | 0.536033 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all) | all (2020 + 2021) | 0.764767 | 0.618702 | 0.548541 |
| [cardiffnlp/roberta-large-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-multi-2020) | 2020 only | 0.732366 | 0.579456 | 0.493746 |
| [cardiffnlp/roberta-base-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-multi-2020) | 2020 only | 0.725229 | 0.561261 | 0.499107 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020) | 2020 only | 0.73671 | 0.565624 | 0.513401 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020) | 2020 only | 0.729446 | 0.534799 | 0.50268 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-2020) | 2020 only | 0.731106 | 0.532141 | 0.509827 |
Model fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py).
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```python
{
"date": "2021-03-07",
"text": "The latest The Movie theater Daily! {{URL}} Thanks to {{USERNAME}} {{USERNAME}} {{USERNAME}} #lunchtimeread #amc1000",
"id": "1368464923370676231",
"label": [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"label_name": ["film_tv_&_video"]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_multi/raw/main/dataset/label.multi.json).
```python
{
"arts_&_culture": 0,
"business_&_entrepreneurs": 1,
"celebrity_&_pop_culture": 2,
"diaries_&_daily_life": 3,
"family": 4,
"fashion_&_style": 5,
"film_tv_&_video": 6,
"fitness_&_health": 7,
"food_&_dining": 8,
"gaming": 9,
"learning_&_educational": 10,
"music": 11,
"news_&_social_concern": 12,
"other_hobbies": 13,
"relationships": 14,
"science_&_technology": 15,
"sports": 16,
"travel_&_adventure": 17,
"youth_&_student_life": 18
}
```
### Citation Information
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
``` |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-emotion-default-139135-14996090 | 2022-09-01T15:39:48.000Z | null | false | 2e11493c1b92c66b3d718b39d13d21c0bcbab1ba | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-emotion-default-139135-14996090/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: bhadresh-savani/roberta-base-emotion
metrics: ['roc_auc', 'mae']
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: bhadresh-savani/roberta-base-emotion
* Dataset: emotion
* 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 [@gmoney](https://huggingface.co/gmoney) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-emotion-default-139135-14996091 | 2022-09-01T15:39:53.000Z | null | false | aff1661b05d3101c728c5383a9c84111d2e1349f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-emotion-default-139135-14996091/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: ericntay/bert-finetuned-emotion
metrics: ['roc_auc', 'mae']
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: ericntay/bert-finetuned-emotion
* Dataset: emotion
* 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 [@gmoney](https://huggingface.co/gmoney) for evaluating this model. |
BAJIRAO | null | null | null | false | 1 | false | BAJIRAO/spam_data | 2022-09-01T20:08:50.000Z | null | false | 532014b9d4a1dd5c658db790758698c0810d9793 | [] | [] | https://huggingface.co/datasets/BAJIRAO/spam_data/resolve/main/README.md | |
zeroshot | null | null | null | false | 6 | false | zeroshot/twitter-financial-news-sentiment | 2022-09-07T18:49:28.000Z | null | false | b02e6b2a4decd7514b454f91e35399ab9631c9a7 | [] | [
"annotations_creators:other",
"language:en",
"language_creators:other",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"tags:twitter",
"tags:finance",
"tags:markets",
"tags:stocks",
"tags:wallstreet",
"tags:quant",
"tags:hedgefunds",... | https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment/resolve/main/README.md | ---
annotations_creators:
- other
language:
- en
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: twitter financial news
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- twitter
- finance
- markets
- stocks
- wallstreet
- quant
- hedgefunds
- markets
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
### Dataset Description
The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.
1. The dataset holds 11,932 documents annotated with 3 labels:
```python
sentiments = {
"LABEL_0": "Bearish",
"LABEL_1": "Bullish",
"LABEL_2": "Neutral"
}
```
The data was collected using the Twitter API. The current dataset supports the multi-class classification task.
### Task: Sentiment Analysis
# Data Splits
There are 2 splits: train and validation. Below are the statistics:
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 9,938 |
| Validation | 2,486 |
# Licensing Information
The Twitter Financial Dataset (sentiment) version 1.0.0 is released under the MIT License. |
Lubub | null | null | null | false | 1 | false | Lubub/locutorxxinews | 2022-09-01T23:56:34.000Z | null | false | bacd60959e6e00287ef74c0ebf49fba20dce61b9 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/Lubub/locutorxxinews/resolve/main/README.md | ---
license: apache-2.0
---
|
Lubub | null | null | null | false | 1 | false | Lubub/testexxi | 2022-09-02T00:05:12.000Z | null | false | dc06182a52cd5bbb6d30a5e2e62a1406dec583dc | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/Lubub/testexxi/resolve/main/README.md | ---
license: apache-2.0
---
|
cardiffnlp | null | @inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
} | [TweetTopic](https://arxiv.org/abs/2209.09824) | false | 15 | false | cardiffnlp/tweet_topic_single | 2022-09-30T21:03:35.000Z | null | false | cb817076c21a18ec36b1fcfab365b2647b0fe43e | [] | [
"arxiv:2209.09824",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1k<10K",
"task_categories:text-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/resolve/main/README.md | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: TweetTopicSingle
---
# Dataset Card for "cardiffnlp/tweet_topic_single"
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824)
- **Dataset:** Tweet Topic Dataset
- **Domain:** Twitter
- **Number of Class:** 6
### Dataset Summary
This is the official repository of TweetTopic (["Twitter Topic Classification
, COLING main conference 2022"](https://arxiv.org/abs/2209.09824)), a topic classification dataset on Twitter with 6 labels.
Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
See [cardiffnlp/tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) for multi label version of TweetTopic.
The tweet collection used in TweetTopic is same as what used in [TweetNER7](https://huggingface.co/datasets/tner/tweetner7).
The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.
### Preprocessing
We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
For verified usernames, we replace its display name (or account name) with symbols `{@}`.
For example, a tweet
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from @herbiehancock
via @bluenoterecords link below:
http://bluenote.lnk.to/AlbumOfTheWeek
```
is transformed into the following text.
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from {@herbiehancock@}
via {@bluenoterecords@} link below: {{URL}}
```
A simple function to format tweet follows below.
```python
import re
from urlextract import URLExtract
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
target_format = format_tweet(target)
print(target_format)
'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
```
### Data Splits
| split | number of texts | description |
|:------------------------|-----:|------:|
| test_2020 | 376 | test dataset from September 2019 to August 2020 |
| test_2021 | 1693 | test dataset from September 2020 to August 2021 |
| train_2020 | 2858 | training dataset from September 2019 to August 2020 |
| train_2021 | 1516 | training dataset from September 2020 to August 2021 |
| train_all | 4374 | combined training dataset of `train_2020` and `train_2021` |
| validation_2020 | 352 | validation dataset from September 2019 to August 2020 |
| validation_2021 | 189 | validation dataset from September 2020 to August 2021 |
| train_random | 2830 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
| validation_random | 354 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
| test_coling2022_random | 3399 | random split used in the COLING 2022 paper |
| train_coling2022_random | 3598 | random split used in the COLING 2022 paper |
| test_coling2022 | 3399 | temporal split used in the COLING 2022 paper |
| train_coling2022 | 3598 | temporal split used in the COLING 2022 paper |
For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `train_coling2022` and `test_coling2022` for temporal-shift, and `train_coling2022_random` and `test_coling2022_random` fir random split (the coling2022 split does not have validation set).
### Models
| model | training data | F1 | F1 (macro) | Accuracy |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|---------:|-------------:|-----------:|
| [cardiffnlp/roberta-large-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-all) | all (2020 + 2021) | 0.896043 | 0.800061 | 0.896043 |
| [cardiffnlp/roberta-base-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-all) | all (2020 + 2021) | 0.887773 | 0.79793 | 0.887773 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all) | all (2020 + 2021) | 0.892499 | 0.774494 | 0.892499 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all) | all (2020 + 2021) | 0.890136 | 0.776025 | 0.890136 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all) | all (2020 + 2021) | 0.894861 | 0.800952 | 0.894861 |
| [cardiffnlp/roberta-large-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-2020) | 2020 only | 0.878913 | 0.70565 | 0.878913 |
| [cardiffnlp/roberta-base-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-2020) | 2020 only | 0.868281 | 0.729667 | 0.868281 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020) | 2020 only | 0.882457 | 0.740187 | 0.882457 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020) | 2020 only | 0.87596 | 0.746275 | 0.87596 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020) | 2020 only | 0.877732 | 0.746119 | 0.877732 |
Model fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py).
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```python
{
"text": "Game day for {{USERNAME}} U18\u2019s against {{USERNAME}} U18\u2019s. Even though it\u2019s a \u2018home\u2019 game for the people that have settled in Mid Wales it\u2019s still a 4 hour round trip for us up to Colwyn Bay. Still enjoy it though!",
"date": "2019-09-08",
"label": 4,
"id": "1170606779568463874",
"label_name": "sports_&_gaming"
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_single/raw/main/dataset/label.single.json).
```python
{
"arts_&_culture": 0,
"business_&_entrepreneurs": 1,
"pop_culture": 2,
"daily_life": 3,
"sports_&_gaming": 4,
"science_&_technology": 5
}
```
### Citation Information
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
``` |
xianbao | null | null | null | false | null | false | xianbao/test | 2022-09-02T00:50:30.000Z | null | false | fd58a44fc0160dea934912d28c113b39279b92af | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/xianbao/test/resolve/main/README.md | ---
license: apache-2.0
---
|
Chr0my | null | null | null | false | 30 | false | Chr0my/Epidemic_music | 2022-09-02T02:25:43.000Z | null | false | 3a80376302783b83edcba43d8ef53f49eadb0298 | [] | [
"license:mit"
] | https://huggingface.co/datasets/Chr0my/Epidemic_music/resolve/main/README.md | ---
license: mit
---
|
tobiaslee | null | null | null | false | 1 | false | tobiaslee/FiCLS | 2022-09-02T03:14:32.000Z | null | false | 69d51c85d30f6f0202c140ecdd40bd010027e59f | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/tobiaslee/FiCLS/resolve/main/README.md | ---
license: afl-3.0
---
|
nid989 | null | null | null | false | 2 | false | nid989/EssayFroum-Dataset | 2022-09-02T04:45:37.000Z | null | false | 73d805de8c0299677d1037085f4272949da330ef | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/nid989/EssayFroum-Dataset/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-xsum-default-21f5cd-15036097 | 2022-09-02T09:46:38.000Z | null | false | 8b820b74765bc3a114dd3d1cbb344ed857bef73b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-xsum-default-21f5cd-15036097/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-xsum-9-6
metrics: ['accuracy']
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: sshleifer/distilbart-xsum-9-6
* 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 [@Rohil](https://huggingface.co/Rohil) for evaluating this model. |
speech-seq2seq | null | @article{DBLP:journals/corr/abs-2106-06909,
author = {Guoguo Chen and
Shuzhou Chai and
Guanbo Wang and
Jiayu Du and
Wei{-}Qiang Zhang and
Chao Weng and
Dan Su and
Daniel Povey and
Jan Trmal and
Junbo Zhang and
Mingjie Jin and
Sanjeev Khudanpur and
Shinji Watanabe and
Shuaijiang Zhao and
Wei Zou and
Xiangang Li and
Xuchen Yao and
Yongqing Wang and
Yujun Wang and
Zhao You and
Zhiyong Yan},
title = {GigaSpeech: An Evolving, Multi-domain {ASR} Corpus with 10, 000 Hours
of Transcribed Audio},
journal = {CoRR},
volume = {abs/2106.06909},
year = {2021},
url = {https://arxiv.org/abs/2106.06909},
eprinttype = {arXiv},
eprint = {2106.06909},
timestamp = {Wed, 29 Dec 2021 14:29:26 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-06909.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts
and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science,
sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable
for speech recognition training, and to filter out segments with low-quality transcription. For system training,
GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h.
For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage,
and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand,
are re-processed by professional human transcribers to ensure high transcription quality. | false | 1 | false | speech-seq2seq/ami | 2022-09-06T23:03:11.000Z | null | false | 7779c1f5ce465390fae18cef176c52cd371e8618 | [] | [] | https://huggingface.co/datasets/speech-seq2seq/ami/resolve/main/README.md | # Unormalized AMI
```python
from datasets import load_dataset
ami = load_dataset("speech-seq2seq/ami", "ihm")
```
## TODO(PVP) - explain exactly what normalization was accepted what wasn't |
graphs-datasets | null | null | null | false | 1 | false | graphs-datasets/AIDS | 2022-09-02T10:53:25.000Z | null | false | a2c431ddff4668df09beed0bd5450c77a87b7c27 | [] | [
"arxiv:2007.08663",
"licence:unknown"
] | https://huggingface.co/datasets/graphs-datasets/AIDS/resolve/main/README.md | ---
licence: unknown
---
# Dataset Card for AIDS
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data)**
- **Paper:**: (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-aids)
### Dataset Summary
The `AIDS` dataset is a dataset containing compounds checked for evidence of anti-HIV activity..
### Supported Tasks and Leaderboards
`AIDS` should be used for molecular classification, a binary classification task. The score used is accuracy with cross validation.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | medium |
| #graphs | 1999 |
| average #nodes | 15.5875 |
| average #edges | 32.39 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@InProceedings{10.1007/978-3-540-89689-0_33,
author="Riesen, Kaspar
and Bunke, Horst",
editor="da Vitoria Lobo, Niels
and Kasparis, Takis
and Roli, Fabio
and Kwok, James T.
and Georgiopoulos, Michael
and Anagnostopoulos, Georgios C.
and Loog, Marco",
title="IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning",
booktitle="Structural, Syntactic, and Statistical Pattern Recognition",
year="2008",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="287--297",
abstract="In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest in graph based representation, a lack of standardized graph data sets for benchmarking can be observed. Common practice is that researchers use their own data sets, and this behavior cumbers the objective evaluation of the proposed methods. In order to make the different approaches in graph based machine learning better comparable, the present paper aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.",
isbn="978-3-540-89689-0"
}
``` |
graphs-datasets | null | null | null | false | 1 | false | graphs-datasets/MD17-aspirin | 2022-09-02T11:31:22.000Z | null | false | cd3e1fa2eda6616334e18e10fcbf0a93bd8ec174 | [] | [
"arxiv:2007.08663",
"licence:unknown"
] | https://huggingface.co/datasets/graphs-datasets/MD17-aspirin/resolve/main/README.md | ---
licence: unknown
---
# Dataset Card for aspirin
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**: (see citation)
### Dataset Summary
The `aspirin` dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.
### Supported Tasks and Leaderboards
`aspirin` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the full set
dataset_pg_list = [Data(graph) for graph in dataset_hf["full"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 111762 |
| average #nodes | 21.0 |
| average #edges | 303.0447106824262 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@article{Chmiela_2017,
doi = {10.1126/sciadv.1603015},
url = {https://doi.org/10.1126%2Fsciadv.1603015},
year = 2017,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {3},
number = {5},
author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
title = {Machine learning of accurate energy-conserving molecular force fields},
journal = {Science Advances}
}
```
|
graphs-datasets | null | null | null | false | null | false | graphs-datasets/MD17-benzene | 2022-09-02T11:32:01.000Z | null | false | ae5b03a688d934c0226638734ad36f9131f88dff | [] | [
"arxiv:2007.08663",
"licence:unknown"
] | https://huggingface.co/datasets/graphs-datasets/MD17-benzene/resolve/main/README.md | ---
licence: unknown
---
# Dataset Card for benzene
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**: (see citation)
### Dataset Summary
The `benzene` dataset is molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.
### Supported Tasks and Leaderboards
`benzene` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 527983 |
| average #nodes | 12.0 |
| average #edges | 129.8848866632322 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@article{Chmiela_2017,
doi = {10.1126/sciadv.1603015},
url = {https://doi.org/10.1126%2Fsciadv.1603015},
year = 2017,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {3},
number = {5},
author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
title = {Machine learning of accurate energy-conserving molecular force fields},
journal = {Science Advances}
}
``` |
graphs-datasets | null | null | null | false | 1 | false | graphs-datasets/MD17-ethanol | 2022-09-02T11:36:14.000Z | null | false | b7f4194f478273b5e05bae2883fe8009f6d53fa8 | [] | [
"arxiv:2007.08663",
"licence:unknown"
] | https://huggingface.co/datasets/graphs-datasets/MD17-ethanol/resolve/main/README.md | ---
licence: unknown
---
# Dataset Card for ethanol
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**: (see citation)
### Dataset Summary
The `ethanol` dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.
### Supported Tasks and Leaderboards
`ethanol` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 455092 |
| average #nodes | 9.0 |
| average #edges | 72.0 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@article{Chmiela_2017,
doi = {10.1126/sciadv.1603015},
url = {https://doi.org/10.1126%2Fsciadv.1603015},
year = 2017,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {3},
number = {5},
author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
title = {Machine learning of accurate energy-conserving molecular force fields},
journal = {Science Advances}
}
``` |
graphs-datasets | null | null | null | false | 1 | false | graphs-datasets/MD17-malonaldehyde | 2022-09-02T12:14:41.000Z | null | false | 6424deaa2dca5feabeac96c36b043429d4252312 | [] | [
"arxiv:2007.08663",
"licence:unknown"
] | https://huggingface.co/datasets/graphs-datasets/MD17-malonaldehyde/resolve/main/README.md | ---
licence: unknown
---
# Dataset Card for malonaldehyde
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**: (see citation)
### Dataset Summary
The `malonaldehyde` dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.
### Supported Tasks and Leaderboards
`malonaldehyde` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 893237 |
| average #nodes | 9.0 |
| average #edges | 71.99990148202383 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@article{Chmiela_2017,
doi = {10.1126/sciadv.1603015},
url = {https://doi.org/10.1126%2Fsciadv.1603015},
year = 2017,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {3},
number = {5},
author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
title = {Machine learning of accurate energy-conserving molecular force fields},
journal = {Science Advances}
}
``` |
clips | null | null | null | false | 8 | false | clips/VaccinChatNL | 2022-09-06T13:42:34.000Z | null | false | 607e26b9ad9e36a1b1239aee8cc56b39210a6d27 | [] | [
"annotations_creators:expert-generated",
"language:nl",
"language_creators:other",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"tags:covid-19",
"tags:FAQ",
"tags:question-answer pairs",
"task_categories:text-classification",
"task... | https://huggingface.co/datasets/clips/VaccinChatNL/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- nl
language_creators:
- other
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: VaccinChatNL
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- covid-19
- FAQ
- question-answer pairs
task_categories:
- text-classification
task_ids:
- intent-classification
---
# Dataset Card for VaccinChatNL
## 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:** [To be added]
- **Leaderboard:** -->
- **Point of Contact:** [Jeska Buhmann](mailto:jeska.buhmann@uantwerpen.be)
### Dataset Summary
VaccinChatNL is a Flemish Dutch FAQ dataset on the topic of COVID-19 vaccinations in Flanders. It consists of 12,833 user questions divided over 181 answer labels, thus providing large groups of semantically equivalent paraphrases (a many-to-one mapping of user questions to answer labels). VaccinChatNL is the first Dutch many-to-one FAQ dataset of this size.
### Supported Tasks and Leaderboards
- 'text-classification': the dataset can be used to train a classification model for Dutch frequently asked questions on the topic of COVID-19 vaccination in Flanders.
### Languages
Dutch (Flemish): the BCP-47 code for Dutch as generally spoken in Flanders (Belgium) is nl-BE.
## Dataset Structure
### Data Instances
For each instance, there is a string for the user question and a string for the label of the annotated answer. See the [CLiPS / VaccinChatNL dataset viewer](https://huggingface.co/datasets/clips/VaccinChatNL/viewer/clips--VaccinChatNL/train).
```
{"sentence1": "Waar kan ik de bijsluiters van de vaccins vinden?", "label": "faq_ask_bijsluiter"}
```
### Data Fields
- `sentence1`: a string containing the user question
- `label`: a string containing the name of the intent (the answer class)
### Data Splits
The VaccinChatNL dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics for the dataset.
| Dataset Split | Number of Labeled User Questions in Split |
| ------------- | ------------------------------------------ |
| Train | 10,542 |
| Validation | 1,171 |
| Test | 1,170 |
## Dataset Creation
<!-- ### Curation Rationale
[More Information Needed] -->
<!-- ### Source Data
[Perhaps a link to vaccinchat.be and some of the website that were used for information] -->
<!-- #### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed] -->
### Annotations
#### Annotation process
Annotation was an iterative semi-automatic process. Starting from a very limited dataset with approximately 50 question-answer pairs (_sentence1-label_ pairs) a text classification model was trained and implemented in a publicly available chatbot. When the chatbot was used, the predicted labels for the new questions were checked and corrected if necessary. In addition, new answers were added to the dataset. After each round of corrections, the model was retrained on the updated dataset. This iterative approach led to the final dataset containing 12,883 user questions divided over 181 answer labels.
#### Who are the annotators?
The VaccinChatNL data were annotated by members and students of [CLiPS](https://www.uantwerpen.be/en/research-groups/clips/). All annotators have a background in Computational Linguistics.
### Personal and Sensitive Information
The data are anonymized in the sense that a user question can never be traced back to a specific individual.
## Considerations for Using the Data
<!-- ### Social Impact of Dataset
[More Information Needed] -->
### Discussion of Biases
This dataset contains real user questions, including a rather large section (7%) of out-of-domain questions or remarks (_label: nlu_fallback_). This class of user questions consists of ununderstandable questions, but also jokes and insulting remarks.
<!-- ### Other Known Limitations
[Perhaps some information of % of exact overlap between train and test set] -->
## Additional Information
<!-- ### Dataset Curators
[More Information Needed] -->
<!-- ### Licensing Information
[More Information Needed] -->
### Citation Information
Will be added asap.
<!-- ### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. -->
|
graphs-datasets | null | null | null | false | 1 | false | graphs-datasets/MD17-naphthalene | 2022-09-02T11:54:50.000Z | null | false | c9d4ba90a4cf784feff32721f5c9072d49fed2a9 | [] | [
"arxiv:2007.08663",
"licence:unknown"
] | https://huggingface.co/datasets/graphs-datasets/MD17-naphthalene/resolve/main/README.md | ---
licence: unknown
---
# Dataset Card for naphthalene
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**: (see citation)
### Dataset Summary
The `naphthalene` dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.
### Supported Tasks and Leaderboards
`naphthalene` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 226255 |
| average #nodes | 18.0 |
| average #edges | 254.73246234354005 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@article{Chmiela_2017,
doi = {10.1126/sciadv.1603015},
url = {https://doi.org/10.1126%2Fsciadv.1603015},
year = 2017,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {3},
number = {5},
author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
title = {Machine learning of accurate energy-conserving molecular force fields},
journal = {Science Advances}
}
``` |
graphs-datasets | null | null | null | false | null | false | graphs-datasets/MD17-salicylic_acid | 2022-09-02T12:14:25.000Z | null | false | 6800b85764a9982635ccc319c99edb38266b23c6 | [] | [
"arxiv:2007.08663",
"licence:unknown"
] | https://huggingface.co/datasets/graphs-datasets/MD17-salicylic_acid/resolve/main/README.md | ---
licence: unknown
---
# Dataset Card for salicylic_acid
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**: (see citation)
### Dataset Summary
The `salicylic_acid` dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.
### Supported Tasks and Leaderboards
`salicylic_acid` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 220231 |
| average #nodes | 16.0 |
| average #edges | 208.2681717461586 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@article{Chmiela_2017,
doi = {10.1126/sciadv.1603015},
url = {https://doi.org/10.1126%2Fsciadv.1603015},
year = 2017,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {3},
number = {5},
author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
title = {Machine learning of accurate energy-conserving molecular force fields},
journal = {Science Advances}
}
``` |
graphs-datasets | null | null | null | false | 1 | false | graphs-datasets/MD17-toluene | 2022-09-02T12:13:51.000Z | null | false | 23193a565cb880fae47912ee75fe1e73a2886308 | [] | [
"arxiv:2007.08663",
"licence:unknown"
] | https://huggingface.co/datasets/graphs-datasets/MD17-toluene/resolve/main/README.md | ---
licence: unknown
---
# Dataset Card for toluene
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**: (see citation)
### Dataset Summary
The `toluene` dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.
### Supported Tasks and Leaderboards
`toluene` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 342790 |
| average #nodes | 15.0 |
| average #edges | 192.30698588936116 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@article{Chmiela_2017,
doi = {10.1126/sciadv.1603015},
url = {https://doi.org/10.1126%2Fsciadv.1603015},
year = 2017,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {3},
number = {5},
author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
title = {Machine learning of accurate energy-conserving molecular force fields},
journal = {Science Advances}
}
``` |
openclimatefix | null | null | null | false | 1 | false | openclimatefix/era5-reanalysis | 2022-11-16T20:32:37.000Z | null | false | fe3d5cddf36a843472e077e83ff6b474ad028559 | [] | [
"license:mit"
] | https://huggingface.co/datasets/openclimatefix/era5-reanalysis/resolve/main/README.md | ---
license: mit
---
This repo contains converted ECMWF ERA5 reanalysis files for both hourly atmospheric and land variables from Jan 2014 to October 2022. The data has been converted from the downloaded NetCDF files into Zarr using Xarray. Each file is 1 day of reanalysis, and so has 24 timesteps at a 0.25 degree grid resolution. All variables in the reanalysis are included here. |
patrickfrank1 | null | null | null | false | 1 | false | patrickfrank1/chess-pgn-games | 2022-09-02T14:07:22.000Z | null | false | 51204a59442e2b988dd4939ec1c89056f8c949b4 | [] | [
"license:cc0-1.0"
] | https://huggingface.co/datasets/patrickfrank1/chess-pgn-games/resolve/main/README.md | ---
license: cc0-1.0
---
|
open-source-metrics | null | null | null | false | 1 | false | open-source-metrics/transformers-dependents | 2022-11-09T17:21:28.000Z | null | false | f30a065014b9d5b82d13e7691efee55a56864b0f | [] | [
"license:apache-2.0",
"tags:github-stars"
] | https://huggingface.co/datasets/open-source-metrics/transformers-dependents/resolve/main/README.md | ---
license: apache-2.0
pretty_name: transformers metrics
tags:
- github-stars
---
# transformers metrics
This dataset contains metrics about the huggingface/transformers package.
Number of repositories in the dataset: 27067
Number of packages in the dataset: 823
## Package dependents
This contains the data available in the [used-by](https://github.com/huggingface/transformers/network/dependents)
tab on GitHub.
### Package & Repository star count
This section shows the package and repository star count, individually.
Package | Repository
:-------------------------:|:-------------------------:
 | 
There are 65 packages that have more than 1000 stars.
There are 140 repositories that have more than 1000 stars.
The top 10 in each category are the following:
*Package*
[hankcs/HanLP](https://github.com/hankcs/HanLP): 26958
[fastai/fastai](https://github.com/fastai/fastai): 22774
[slundberg/shap](https://github.com/slundberg/shap): 17482
[fastai/fastbook](https://github.com/fastai/fastbook): 16052
[jina-ai/jina](https://github.com/jina-ai/jina): 16052
[huggingface/datasets](https://github.com/huggingface/datasets): 14101
[microsoft/recommenders](https://github.com/microsoft/recommenders): 14017
[borisdayma/dalle-mini](https://github.com/borisdayma/dalle-mini): 12872
[flairNLP/flair](https://github.com/flairNLP/flair): 12033
[allenai/allennlp](https://github.com/allenai/allennlp): 11198
*Repository*
[huggingface/transformers](https://github.com/huggingface/transformers): 70487
[hankcs/HanLP](https://github.com/hankcs/HanLP): 26959
[ageron/handson-ml2](https://github.com/ageron/handson-ml2): 22886
[ray-project/ray](https://github.com/ray-project/ray): 22047
[jina-ai/jina](https://github.com/jina-ai/jina): 16052
[RasaHQ/rasa](https://github.com/RasaHQ/rasa): 14844
[microsoft/recommenders](https://github.com/microsoft/recommenders): 14017
[deeplearning4j/deeplearning4j](https://github.com/deeplearning4j/deeplearning4j): 12617
[flairNLP/flair](https://github.com/flairNLP/flair): 12034
[allenai/allennlp](https://github.com/allenai/allennlp): 11198
### Package & Repository fork count
This section shows the package and repository fork count, individually.
Package | Repository
:-------------------------:|:-------------------------:
 | 
There are 55 packages that have more than 200 forks.
There are 128 repositories that have more than 200 forks.
The top 10 in each category are the following:
*Package*
[hankcs/HanLP](https://github.com/hankcs/HanLP): 7388
[fastai/fastai](https://github.com/fastai/fastai): 7297
[fastai/fastbook](https://github.com/fastai/fastbook): 6033
[slundberg/shap](https://github.com/slundberg/shap): 2646
[microsoft/recommenders](https://github.com/microsoft/recommenders): 2473
[allenai/allennlp](https://github.com/allenai/allennlp): 2218
[jina-ai/clip-as-service](https://github.com/jina-ai/clip-as-service): 1972
[jina-ai/jina](https://github.com/jina-ai/jina): 1967
[flairNLP/flair](https://github.com/flairNLP/flair): 1934
[huggingface/datasets](https://github.com/huggingface/datasets): 1841
*Repository*
[huggingface/transformers](https://github.com/huggingface/transformers): 16159
[ageron/handson-ml2](https://github.com/ageron/handson-ml2): 11053
[hankcs/HanLP](https://github.com/hankcs/HanLP): 7389
[aws/amazon-sagemaker-examples](https://github.com/aws/amazon-sagemaker-examples): 5493
[deeplearning4j/deeplearning4j](https://github.com/deeplearning4j/deeplearning4j): 4933
[RasaHQ/rasa](https://github.com/RasaHQ/rasa): 4106
[ray-project/ray](https://github.com/ray-project/ray): 3876
[apache/beam](https://github.com/apache/beam): 3648
[plotly/dash-sample-apps](https://github.com/plotly/dash-sample-apps): 2795
[microsoft/recommenders](https://github.com/microsoft/recommenders): 2473
|
kordless | null | null | null | false | null | false | kordless/steve | 2022-09-02T13:24:32.000Z | null | false | 501043cd0842281776bceb72effc91b75bca500c | [] | [] | https://huggingface.co/datasets/kordless/steve/resolve/main/README.md | |
lewtun | null | \ | false | 6 | false | lewtun/music_classification | 2022-09-02T17:08:02.000Z | null | false | 30ec6a996b5554d1f4294ca4c6b2879926981728 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/lewtun/music_classification/resolve/main/README.md | ---
license: unknown
---
| |
Hemaxi | null | null | null | false | 1 | false | Hemaxi/3DU-Vec | 2022-09-19T15:29:26.000Z | null | false | 081009c3673cab07e4417b6442934d15d1004aa8 | [] | [
"license:gpl-3.0"
] | https://huggingface.co/datasets/Hemaxi/3DU-Vec/resolve/main/README.md | ---
license: gpl-3.0
---
This repository contains the dataset used in our [work](https://github.com/HemaxiN/3D_U-Vec). |
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