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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dyhsup | null | null | null | false | 1 | false | dyhsup/ChemProt_CPR | 2022-08-31T12:09:31.000Z | null | false | bd9f47f758affab100c81931d6afba84bab9ae06 | [] | [
"license:other"
] | https://huggingface.co/datasets/dyhsup/ChemProt_CPR/resolve/main/README.md | ---
license: other
---
Warning: We don't follow the standard of the hugging face, download and process files according to your own needs.
There only contain the intra-sentence relationship.Gold is the positive from the original corpus.Positive is all the relationship intra-sentence. |
BigBang | null | null | null | false | 1 | false | BigBang/rosetta_new | 2022-08-24T16:24:00.000Z | null | false | 6e9893e2a78b8fa852f3268583592f8c4e37362a | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/BigBang/rosetta_new/resolve/main/README.md | ---
license: cc-by-sa-4.0
---
|
jonathanli | null | @inproceedings{chalkidis-etal-2019-large,
title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
author = "Chalkidis, Ilias and Fergadiotis, Emmanouil and Malakasiotis, Prodromos and Androutsopoulos, Ion",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1636",
doi = "10.18653/v1/P19-1636",
pages = "6314--6322"
} | EURLEX57K contains 57k legislative documents in English from EUR-Lex portal, annotated with EUROVOC concepts. | false | 3 | false | jonathanli/eurlex | 2022-10-24T15:26:49.000Z | eurlex57k | false | 6f7dc71b8fd4e8aed7b04752b563c5edf84694c7 | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-label-classification",
"tags:legal-topic-classification"
] | https://huggingface.co/datasets/jonathanli/eurlex/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: eurlex57k
pretty_name: the EUR-Lex dataset
tags:
- legal-topic-classification
---
# Dataset Card for the EUR-Lex dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/
- **Repository:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/
- **Paper:** https://www.aclweb.org/anthology/P19-1636/
- **Leaderboard:** N/A
### Dataset Summary
EURLEX57K can be viewed as an improved version of the dataset released by Mencia and Furnkranzand (2007), which has been widely used in Large-scale Multi-label Text Classification (LMTC) research, but is less than half the size of EURLEX57K (19.6k documents, 4k EUROVOC labels) and more than ten years old.
EURLEX57K contains 57k legislative documents in English from EUR-Lex (https://eur-lex.europa.eu) with an average length of 727 words. Each document contains four major zones:
- the header, which includes the title and name of the legal body enforcing the legal act;
- the recitals, which are legal background references; and
- the main body, usually organized in articles.
**Labeling / Annotation**
All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/).
While EUROVOC includes approx. 7k concepts (labels), only 4,271 (59.31%) are present in EURLEX57K, from which only 2,049 (47.97%) have been assigned to more than 10 documents. The 4,271 labels are also divided into frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.
### Supported Tasks and Leaderboards
The dataset supports:
**Multi-label Text Classification:** Given the text of a document, a model predicts the relevant EUROVOC concepts.
**Few-shot and Zero-shot learning:** As already noted, the labels can be divided into three groups: frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.
### Languages
All documents are written in English.
## Dataset Structure
### Data Instances
```json
{
"celex_id": "31979D0509",
"title": "79/509/EEC: Council Decision of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain",
"text": "COUNCIL DECISION of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,",
"eurovoc_concepts": ["192", "2356", "2560", "862", "863"]
}
```
### Data Fields
The following data fields are provided for documents (`train`, `dev`, `test`):
`celex_id`: (**str**) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\
`title`: (**str**) The title of the document.\
`text`: (**str**) The full content of each document, which is represented by its `header`, `recitals` and `main_body`.\
`eurovoc_concepts`: (**List[str]**) The relevant EUROVOC concepts (labels).
If you want to use the descriptors of EUROVOC concepts, similar to Chalkidis et al. (2020), please load: https://archive.org/download/EURLEX57K/eurovoc_concepts.jsonl
```python
import json
with open('./eurovoc_concepts.jsonl') as jsonl_file:
eurovoc_concepts = {json.loads(concept) for concept in jsonl_file.readlines()}
```
### Data Splits
| Split | No of Documents | Avg. words | Avg. labels |
| ------------------- | ------------------------------------ | --- | --- |
| Train | 45,000 | 729 | 5 |
|Development | 6,000 | 714 | 5 |
|Test | 6,000 | 725 | 5 |
## Dataset Creation
### Curation Rationale
The dataset was curated by Chalkidis et al. (2019).\
The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed format.
The documents were downloaded from EUR-Lex portal in HTML format.
The relevant metadata and EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
* The original documents are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed HTML format. The HTML code was striped and the documents split into sections.
* The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).
#### Who are the annotators?
Publications Office of EU (https://publications.europa.eu/en)
### Personal and Sensitive Information
The dataset does not include personal or sensitive information.
## 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
Chalkidis et al. (2019)
### Licensing Information
© European Union, 1998-2021
The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
### Citation Information
*Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis and Ion Androutsopoulos.*
*Large-Scale Multi-Label Text Classification on EU Legislation.*
*Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy. 2019*
```
@inproceedings{chalkidis-etal-2019-large,
title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Androutsopoulos, Ion",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1636",
doi = "10.18653/v1/P19-1636",
pages = "6314--6322"
}
```
### Contributions
Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.
|
mbarnig | null | null | null | false | 1 | false | mbarnig/Tatoeba-en-lb | 2022-08-24T15:38:33.000Z | null | false | 2cf7ca5314557b4a3203da2f987ff122e87aebbb | [] | [
"license:cc-by-nc-sa-4.0"
] | https://huggingface.co/datasets/mbarnig/Tatoeba-en-lb/resolve/main/README.md | ---
license: cc-by-nc-sa-4.0
---
|
teven | null | null | null | false | 21 | false | teven/code_docstring_corpus | 2022-08-24T20:01:58.000Z | null | false | 816621ee6b2c082e5e1062a5bad126feb81b9449 | [] | [] | https://huggingface.co/datasets/teven/code_docstring_corpus/resolve/main/README.md | HF version of Edinburgh-NLP's [Code docstrings corpus](https://github.com/EdinburghNLP/code-docstring-corpus) |
teven | null | null | null | false | 21 | false | teven/code_contests | 2022-08-24T20:01:04.000Z | null | false | 1d750cb1af1c154e447d6baa330110933105a600 | [] | [] | https://huggingface.co/datasets/teven/code_contests/resolve/main/README.md | HF-datasets version of Deepmind's [code_contests](https://github.com/deepmind/code_contests) dataset, notably used for AlphaGo. 1 row per solution, no test data or incorrect solutions included (only name/source/description/solution/language/difficulty) |
gondolas | null | null | null | false | 1 | false | gondolas/test | 2022-08-24T18:00:02.000Z | null | false | eef7d6d11d0e1bfe8cfab8e3030cb1ad35b45b49 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/gondolas/test/resolve/main/README.md | ---
license: unknown
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-eval-project-squad-54745b0c-1311450106 | 2022-08-24T20:36:33.000Z | null | false | 059b500407cd10d3d0254d9c143d353f89ed7271 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-project-squad-54745b0c-1311450106/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: FardinSaboori/bert-finetuned-squad
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: FardinSaboori/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 [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-eval-project-squad-54745b0c-1311450107 | 2022-08-24T20:37:00.000Z | null | false | 00f6010354dc41b964436402e91548d954663e01 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-project-squad-54745b0c-1311450107/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: 21iridescent/distilbert-base-uncased-finetuned-squad
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: 21iridescent/distilbert-base-uncased-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 [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-eval-project-squad-54745b0c-1311450108 | 2022-08-24T20:37:49.000Z | null | false | d2e7a920820db43013d54b67ef1fc315cb5f55cb | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-project-squad-54745b0c-1311450108/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: Aiyshwariya/bert-finetuned-squad
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Aiyshwariya/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 [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
britneymuller | null | null | null | false | 1 | false | britneymuller/cnbc_newsfeed | 2022-08-24T23:04:39.000Z | null | false | df82aa55f008dabd0c2c2d4d58bf8ebb38ce1928 | [] | [
"license:other"
] | https://huggingface.co/datasets/britneymuller/cnbc_newsfeed/resolve/main/README.md | ---
license: other
---
|
ZhangYuanhan | null | null | null | false | 1 | false | ZhangYuanhan/OmniBenchmark | 2022-08-25T02:10:18.000Z | null | false | 4611cf4a48fe1e181ffc5e64a6b25c8a1a6b4c83 | [] | [
"license:cc-by-nc-nd-4.0"
] | https://huggingface.co/datasets/ZhangYuanhan/OmniBenchmark/resolve/main/README.md | ---
license: cc-by-nc-nd-4.0
---
|
sberbank-ai | null | null | null | false | 1 | false | sberbank-ai/Peter | 2022-10-25T11:09:06.000Z | null | false | f7396bc0d39f208076d0d8af13b4644dc3bdd7f8 | [] | [
"arxiv:2103.09354",
"language:ru",
"license:mit",
"source_datasets:original",
"task_categories:image-segmentation",
"task_categories:object-detection",
"tags:optical-character-recognition",
"tags:text-detection",
"tags:ocr"
] | https://huggingface.co/datasets/sberbank-ai/Peter/resolve/main/README.md | ---
language:
- ru
license:
- mit
source_datasets:
- original
task_categories:
- image-segmentation
- object-detection
task_ids: []
tags:
- optical-character-recognition
- text-detection
- ocr
---
# Digital Peter
The Peter dataset can be used for reading texts from the manuscripts written by Peter the Great. The dataset annotation contain end-to-end markup for training detection and OCR models, as well as an end-to-end model for reading text from pages.
Paper is available at http://arxiv.org/abs/2103.09354
## Description
Digital Peter is an educational task with a historical slant created on the basis of several AI technologies (Computer Vision, NLP, and knowledge graphs). The task was prepared jointly with the Saint Petersburg Institute of History (N.P.Lihachov mansion) of Russian Academy of Sciences, Federal Archival Agency of Russia and Russian State Archive of Ancient Acts.
A detailed description of the problem (with an immersion in the problem) can be found in [detailed_description_of_the_task_en.pdf](https://github.com/sberbank-ai/digital_peter_aij2020/blob/master/desc/detailed_description_of_the_task_en.pdf)
The dataset consists of 662 full page images and 9696 annotated text files. There are 265788 symbols and approximately 50998 words.
## Annotation format
The annotation is in COCO format. The `annotation.json` should have the following dictionaries:
- `annotation["categories"]` - a list of dicts with a categories info (categotiy names and indexes).
- `annotation["images"]` - a list of dictionaries with a description of images, each dictionary must contain fields:
- `file_name` - name of the image file.
- `id` for image id.
- `annotation["annotations"]` - a list of dictioraties with a murkup information. Each dictionary stores a description for one polygon from the dataset, and must contain the following fields:
- `image_id` - the index of the image on which the polygon is located.
- `category_id` - the polygon’s category index.
- ```attributes``` - dict with some additional annotatioin information. In the `translation` subdict you can find text translation for the line.
- `segmentation` - the coordinates of the polygon, a list of numbers - which are coordinate pairs x and y.
## Competition
We held a competition based on Digital Peter dataset.
Here is github [link](https://github.com/sberbank-ai/digital_peter_aij2020). Here is competition [page](https://ods.ai/tracks/aij2020) (need to register). |
gishnum | null | null | null | false | 7 | false | gishnum/worldpopulation_neo4j_graph_dump | 2022-08-25T11:22:14.000Z | null | false | e5af44c540cda2e9007ad35b7f8e994225da7786 | [] | [
"license:gpl"
] | https://huggingface.co/datasets/gishnum/worldpopulation_neo4j_graph_dump/resolve/main/README.md | ---
license: gpl
---
|
OxAISH-AL-LLM | null | """
_DESCRIPTION = | Jigsaw Toxic Comment Challenge dataset. This dataset was the basis of a Kaggle competition run by Jigsaw | false | 17 | false | OxAISH-AL-LLM/wiki_toxic | 2022-09-19T15:53:19.000Z | null | false | 872656a156f32e4058307e50e234a44a727a9503 | [] | [
"annotations_creators:crowdsourced",
"language:en",
"language_creators:found",
"license:cc0-1.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"tags:wikipedia",
"tags:toxicity",
"tags:toxic comments",
"task_categories:text-classification",
"task... | https://huggingface.co/datasets/OxAISH-AL-LLM/wiki_toxic/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Toxic Wikipedia Comments
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
tags:
- wikipedia
- toxicity
- toxic comments
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Wiki Toxic
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Wiki Toxic dataset is a modified, cleaned version of the dataset used in the [Kaggle Toxic Comment Classification challenge](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/overview) from 2017/18. The dataset contains comments collected from Wikipedia forums and classifies them into two categories, `toxic` and `non-toxic`.
The Kaggle dataset was cleaned using the included `clean.py` file.
### Supported Tasks and Leaderboards
- Text Classification: the dataset can be used for training a model to recognise toxicity in sentences and classify them accordingly.
### Languages
The sole language used in the dataset is English.
## Dataset Structure
### Data Instances
For each data point, there is an id, the comment_text itself, and a label (0 for non-toxic, 1 for toxic).
```
{'id': 'a123a58f610cffbc',
'comment_text': '"This article SUCKS. It may be poorly written, poorly formatted, or full of pointless crap that no one cares about, and probably all of the above. If it can be rewritten into something less horrible, please, for the love of God, do so, before the vacuum caused by its utter lack of quality drags the rest of Wikipedia down into a bottomless pit of mediocrity."',
'label': 1}
```
### Data Fields
- `id`: A unique identifier string for each comment
- `comment_text`: A string containing the text of the comment
- `label`: An integer, either 0 if the comment is non-toxic, or 1 if the comment is toxic
### Data Splits
The Wiki Toxic dataset has three splits: *train*, *validation*, and *test*. The statistics for each split are below:
| Dataset Split | Number of data points in split |
| ----------- | ----------- |
| Train | 127,656 |
| Validation | 31,915 |
| Test | 63,978 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
wushan | null | null | null | false | 1 | false | wushan/vehicle_qa | 2022-08-25T13:14:33.000Z | null | false | 41688aa331d9ff438cd9a940495de12d6dd0bc8e | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/wushan/vehicle_qa/resolve/main/README.md | ---
license: apache-2.0
---
|
jokerak | null | null | null | false | 1 | false | jokerak/camvid | 2022-08-25T13:34:19.000Z | null | false | 2dcf46e0fe13816745e79fab84347e5d71fe74cc | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/jokerak/camvid/resolve/main/README.md | ---
license: apache-2.0
---
|
kakaobrain | null | null | null | false | 51 | false | kakaobrain/coyo-700m | 2022-08-30T19:07:52.000Z | null | false | 54ee2d8c64d3d80a5e10ef6952a4466551834fc1 | [] | [
"arxiv:2102.05918",
"arxiv:2204.06125",
"arxiv:2010.11929",
"annotations_creators:no-annotation",
"language:en",
"language_creators:other",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"tags:image-text pairs",
"task_categories:tex... | https://huggingface.co/datasets/kakaobrain/coyo-700m/resolve/main/README.md |
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- other
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: COYO-700M
size_categories:
- 100M<n<1B
source_datasets:
- original
tags:
- image-text pairs
task_categories:
- text-to-image
- image-to-text
- zero-shot-classification
task_ids:
- image-captioning
---
# Dataset Card for COYO-700M
## 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:** [COYO homepage](https://kakaobrain.com/contents/?contentId=7eca73e3-3089-43cb-b701-332e8a1743fd)
- **Repository:** [COYO repository](https://github.com/kakaobrain/coyo-dataset)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [COYO email](coyo@kakaobrain.com)
### Dataset Summary
**COYO-700M** is a large-scale dataset that contains **747M image-text pairs** as well as many other **meta-attributes** to increase the usability to train various models. Our dataset follows a similar strategy to previous vision-and-language datasets, collecting many informative pairs of alt-text and its associated image in HTML documents. We expect COYO to be used to train popular large-scale foundation models
complementary to other similar datasets. For more details on the data acquisition process, please refer to the technical paper to be released later.
### Supported Tasks and Leaderboards
We empirically validated the quality of COYO dataset by re-implementing popular models such as [ALIGN](https://arxiv.org/abs/2102.05918), [unCLIP](https://arxiv.org/abs/2204.06125), and [ViT](https://arxiv.org/abs/2010.11929).
We trained these models on COYO-700M or its subsets from scratch, achieving competitive performance to the reported numbers or generated samples in the original papers.
Our pre-trained models and training codes will be released soon along with the technical paper.
### Languages
The texts in the COYO-700M dataset consist of English.
## Dataset Structure
### Data Instances
Each instance in COYO-700M represents single image-text pair information with meta-attributes:
```
{
'id': 841814333321,
'url': 'https://blog.dogsof.com/wp-content/uploads/2021/03/Image-from-iOS-5-e1614711641382.jpg',
'text': 'A Pomsky dog sitting and smiling in field of orange flowers',
'width': 1000,
'height': 988,
'image_phash': 'c9b6a7d8469c1959',
'text_length': 59,
'word_count': 11,
'num_tokens_bert': 13,
'num_tokens_gpt': 12,
'num_faces': 0,
'clip_similarity_vitb32': 0.4296875,
'clip_similarity_vitl14': 0.35205078125,
'nsfw_score_opennsfw2': 0.00031447410583496094,
'nsfw_score_gantman': 0.03298913687467575,
'watermark_score': 0.1014641746878624,
'aesthetic_score_laion_v2': 5.435476303100586
}
```
### Data Fields
| name | type | description |
|--------------------------|---------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| id | long | Unique 64-bit integer ID generated by [monotonically_increasing_id()](https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.monotonically_increasing_id.html) |
| url | string | The image URL extracted from the `src` attribute of the `<img>` tag |
| text | string | The text extracted from the `alt` attribute of the `<img>` tag |
| width | integer | The width of the image |
| height | integer | The height of the image |
| image_phash | string | The [perceptual hash(pHash)](http://www.phash.org/) of the image |
| text_length | integer | The length of the text |
| word_count | integer | The number of words separated by spaces. |
| num_tokens_bert | integer | The number of tokens using [BertTokenizer](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer) |
| num_tokens_gpt | integer | The number of tokens using [GPT2TokenizerFast](https://huggingface.co/docs/transformers/model_doc/gpt2#transformers.GPT2TokenizerFast) |
| num_faces | integer | The number of faces in the image detected by [SCRFD](https://insightface.ai/scrfd) |
| clip_similarity_vitb32 | float | The cosine similarity between text and image(ViT-B/32) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP) |
| clip_similarity_vitl14 | float | The cosine similarity between text and image(ViT-L/14) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP) |
| nsfw_score_opennsfw2 | float | The NSFW score of the image by [OpenNSFW2](https://github.com/bhky/opennsfw2) |
| nsfw_score_gantman | float | The NSFW score of the image by [GantMan/NSFW](https://github.com/GantMan/nsfw_model) |
| watermark_score | float | The watermark probability of the image by our internal model |
| aesthetic_score_laion_v2 | float | The aesthetic score of the image by [LAION-Aesthetics-Predictor-V2](https://github.com/christophschuhmann/improved-aesthetic-predictor) |
### Data Splits
Data was not split, since the evaluation was expected to be performed on more widely used downstream task(s).
## Dataset Creation
### Curation Rationale
Similar to most vision-and-language datasets, our primary goal in the data creation process is to collect many pairs of alt-text and image sources in HTML documents crawled from the web. Therefore, We attempted to eliminate uninformative images or texts with minimal cost and improve our dataset's usability by adding various meta-attributes. Users can use these meta-attributes to sample a subset from COYO-700M and use it to train the desired model. For instance, the *num_faces* attribute could be used to make a subset like *COYO-Faces* and develop a privacy-preserving generative model.
### Source Data
#### Initial Data Collection and Normalization
We collected about 10 billion pairs of alt-text and image sources in HTML documents in [CommonCrawl](https://commoncrawl.org/) from Oct. 2020 to Aug. 2021. and eliminated uninformative pairs through the image and/or text level filtering process with minimal cost.
**Image Level**
* Included all image formats that [Pillow library](https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html) can decode. (JPEG, WEBP, PNG, BMP, ...)
* Removed images less than 5KB image size.
* Removed images with an aspect ratio greater than 3.0.
* Removed images with min(width, height) < 200.
* Removed images with a score of [OpenNSFW2](https://github.com/bhky/opennsfw2) or [GantMan/NSFW](https://github.com/GantMan/nsfw_model) higher than 0.5.
* Removed all duplicate images based on the image [pHash](http://www.phash.org/) value from external public datasets.
* ImageNet-1K/21K, Flickr-30K, MS-COCO, CC-3M, CC-12M
**Text Level**
* Collected only English text using [cld3](https://github.com/google/cld3).
* Replaced consecutive whitespace characters with a single whitespace and removed the whitespace before and after the sentence.
(e.g. `"\n \n Load image into Gallery viewer, valentine&#39;s day roses\n \n" → "Load image into Gallery viewer, valentine&#39;s day roses"`)
* Removed texts with a length of 5 or less.
* Removed texts that do not have a noun form.
* Removed texts with less than 3 words or more than 256 words and texts over 1000 in length.
* Removed texts appearing more than 10 times.
(e.g. `“thumbnail for”, “image for”, “picture of”`)
* Removed texts containing NSFW words collected from [profanity_filter](https://github.com/rominf/profanity-filter/blob/master/profanity_filter/data/en_profane_words.txt), [better_profanity](https://github.com/snguyenthanh/better_profanity/blob/master/better_profanity/profanity_wordlist.txt), and [google_twunter_lol](https://gist.github.com/ryanlewis/a37739d710ccdb4b406d).
**Image-Text Level**
* Removed duplicated samples based on (image_phash, text).
(Different text may exist for the same image URL.)
#### Who are the source language producers?
[Common Crawl](https://commoncrawl.org/) is the data source for COYO-700M.
### Annotations
#### Annotation process
The dataset was built in a fully automated process that did not require human annotation.
#### Who are the annotators?
No human annotation
### Personal and Sensitive Information
#### Disclaimer & Content Warning
The COYO dataset is recommended to be used for research purposes.
Kakao Brain tried to construct a "Safe" dataset when building the COYO dataset. (See [Data Filtering](#source-data) Section) Kakao Brain is constantly making efforts to create more "Safe" datasets.
However, despite these efforts, this large-scale dataset was not hand-picked by humans to avoid the risk due to its very large size (over 700M).
Keep in mind that the unscreened nature of the dataset means that the collected images can lead to strongly discomforting and disturbing content for humans.
The COYO dataset may contain some inappropriate data, and any problems resulting from such data are the full responsibility of the user who used it.
Therefore, it is strongly recommended that this dataset be used only for research, keeping this in mind when using the dataset, and Kakao Brain does not recommend using this dataset as it is without special processing to clear inappropriate data to create commercial products.
## Considerations for Using the Data
### Social Impact of Dataset
It will be described in a paper to be released soon.
### Discussion of Biases
It will be described in a paper to be released soon.
### Other Known Limitations
It will be described in a paper to be released soon.
## Additional Information
### Dataset Curators
COYO dataset was released as an open source in the hope that it will be helpful to many research institutes and startups for research purposes. We look forward to contacting us from various places who wish to cooperate with us.
[coyo@kakaobrain.com](mailto:coyo@kakaobrain.com)
### Licensing Information
#### License
The COYO dataset of Kakao Brain is licensed under [CC-BY-4.0 License](https://creativecommons.org/licenses/by/4.0/).
The full license can be found in the [LICENSE.cc-by-4.0 file](./coyo-700m/blob/main/LICENSE.cc-by-4.0).
The dataset includes “Image URL” and “Text” collected from various sites by analyzing Common Crawl data, an open data web crawling project.
The collected data (images and text) is subject to the license to which each content belongs.
#### Obligation to use
While Open Source may be free to use, that does not mean it is free of obligation.
To determine whether your intended use of the COYO dataset is suitable for the CC-BY-4.0 license, please consider the license guide.
If you violate the license, you may be subject to legal action such as the prohibition of use or claim for damages depending on the use.
### Citation Information
If you apply this dataset to any project and research, please cite our code:
```
@misc{kakaobrain2022coyo-700m,
title = {COYO-700M: Image-Text Pair Dataset},
author = {Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, Saehoon Kim},
year = {2022},
howpublished = {\url{https://github.com/kakaobrain/coyo-dataset}},
}
```
### Contributions
- Minwoo Byeon ([@mwbyeon](https://github.com/mwbyeon))
- Beomhee Park ([@beomheepark](https://github.com/beomheepark))
- Haecheon Kim ([@HaecheonKim](https://github.com/HaecheonKim))
- Sungjun Lee ([@justhungryman](https://github.com/justHungryMan))
- Woonhyuk Baek ([@wbaek](https://github.com/wbaek))
- Saehoon Kim ([@saehoonkim](https://github.com/saehoonkim))
- and Kakao Brain Large-Scale AI Studio
|
teticio | null | null | null | false | 77 | false | teticio/audio-diffusion-256 | 2022-11-09T10:49:48.000Z | null | false | 60eceef746f537c1efe46ffd2d5485d631a9c9d8 | [] | [
"size_categories:10K<n<100K",
"tags:audio",
"tags:spectrograms",
"task_categories:image-to-image"
] | https://huggingface.co/datasets/teticio/audio-diffusion-256/resolve/main/README.md | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: Mel spectrograms of music
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- audio
- spectrograms
task_categories:
- image-to-image
task_ids: []
---
Over 20,000 256x256 mel spectrograms of 5 second samples of music from my Spotify liked playlist. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models.
```
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
``` |
allenai | null | null | null | false | 2 | false | allenai/multixscience_sparse_mean | 2022-11-03T21:37:02.000Z | multi-xscience | false | c7f32a0dee3d5baaeb76b4ea9a665294e0b097eb | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:summarization-other-paper-abstract-generation"
] | https://huggingface.co/datasets/allenai/multixscience_sparse_mean/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- summarization-other-paper-abstract-generation
paperswithcode_id: multi-xscience
pretty_name: Multi-XScience
---
This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `related_work` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==4`
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.548 | 0.2272 | 0.1611 | 0.2704 | |
allenai | null | null | null | false | 2 | false | allenai/multixscience_sparse_max | 2022-11-03T21:36:09.000Z | multi-xscience | false | 7f3fadb0ae53ea8691def662411b4c453dc7172e | [] | [
"annotations_creators:found",
"language_creators:found",
"language:en",
"license:unknown",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:summarization-other-paper-abstract-generation"
] | https://huggingface.co/datasets/allenai/multixscience_sparse_max/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- summarization-other-paper-abstract-generation
paperswithcode_id: multi-xscience
pretty_name: Multi-XScience
---
This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `related_work` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==20`
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.548 | 0.2272 | 0.055 | 0.4039 | |
angelolab | null | @InProceedings{huggingface:dataset,
title = {Ark Analysis Example Dataset},
author={Angelo Lab},
year={2022}
} | This dataset contains 11 Field of Views (FOVs), each with 22 channels. | false | 4,375 | false | angelolab/ark_example | 2022-11-11T02:52:32.000Z | null | false | 9b38f7ef9596b183cffa9ddcea70136668c3a459 | [] | [
"annotations_creators:no-annotation",
"license:apache-2.0",
"size_categories:n<1K",
"source_datasets:original",
"tags:MIBI",
"tags:Multiplexed-Imaging",
"task_categories:image-segmentation",
"task_ids:instance-segmentation"
] | https://huggingface.co/datasets/angelolab/ark_example/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language: []
language_creators: []
license:
- apache-2.0
multilinguality: []
pretty_name: An example dataset for analyzing multiplexed imaging data.
size_categories:
- n<1K
source_datasets:
- original
tags:
- MIBI
- Multiplexed-Imaging
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@angelolab](https://github.com/angelolab) for adding this dataset. |
iejMac | null | null | null | false | 5 | false | iejMac/CLIP-WebVid | 2022-10-04T09:10:24.000Z | null | false | 11ef172f3c13e60eaf30fcf319e3919c760785fb | [] | [] | https://huggingface.co/datasets/iejMac/CLIP-WebVid/resolve/main/README.md | ---
license: mit
---
|
merkalo-ziri | null | null | null | false | 1 | false | merkalo-ziri/qa_shreded | 2022-08-26T01:27:18.000Z | null | false | f9ad319b1eb78b0af0b1c8f5dc951c3092d6ee9c | [] | [
"annotations_creators:found",
"language:rus",
"language_creators:found",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:question-answering"
] | https://huggingface.co/datasets/merkalo-ziri/qa_shreded/resolve/main/README.md | ---
annotations_creators:
- found
language:
- rus
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: qa_main
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids: []
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
justahandsomeboy | null | null | null | false | 1 | false | justahandsomeboy/recipedia_1 | 2022-08-26T04:22:13.000Z | null | false | 1ac837cf3234412532906d405756f6918233ca1e | [] | [
"license:mit"
] | https://huggingface.co/datasets/justahandsomeboy/recipedia_1/resolve/main/README.md | ---
license: mit
---
|
Zaid | null | @inproceedings{tiedemann-2020-tatoeba,
title = "The {T}atoeba {T}ranslation {C}hallenge {--} {R}ealistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
} | The Tatoeba Translation Challenge is a multilingual data set of
machine translation benchmarks derived from user-contributed
translations collected by [Tatoeba.org](https://tatoeba.org/) and
provided as parallel corpus from [OPUS](https://opus.nlpl.eu/). This
dataset includes test and development data sorted by language pair. It
includes test sets for hundreds of language pairs and is continuously
updated. Please, check the version number tag to refer to the release
that your are using. | false | 11 | false | Zaid/tatoeba_mt | 2022-08-26T04:55:12.000Z | null | false | 488d2a94c56bd52eb4f69cecdd868204886e418e | [] | [
"license:other"
] | https://huggingface.co/datasets/Zaid/tatoeba_mt/resolve/main/README.md | ---
license: other
---
|
Bingsu | null | null | null | false | 8 | false | Bingsu/Gameplay_Images | 2022-08-26T05:31:58.000Z | null | false | 227e4266899d746172ebd46f90e26af2d370f799 | [] | [
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"task_categories:image-classification"
] | https://huggingface.co/datasets/Bingsu/Gameplay_Images/resolve/main/README.md | ---
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Gameplay Images
size_categories:
- 1K<n<10K
task_categories:
- image-classification
---
# Gameplay Images
## Dataset Description
- **Homepage:** [kaggle](https://www.kaggle.com/datasets/aditmagotra/gameplay-images)
- **Download Size** 2.50 GiB
- **Generated Size** 1.68 GiB
- **Total Size** 4.19 GiB
A dataset from [kaggle](https://www.kaggle.com/datasets/aditmagotra/gameplay-images).
This is a dataset of 10 very famous video games in the world.
These include
- Among Us
- Apex Legends
- Fortnite
- Forza Horizon
- Free Fire
- Genshin Impact
- God of War
- Minecraft
- Roblox
- Terraria
There are 1000 images per class and all are sized `640 x 360`. They are in the `.png` format.
This Dataset was made by saving frames every few seconds from famous gameplay videos on Youtube.
※ This dataset was uploaded in January 2022. Game content updated after that will not be included.
### License
CC-BY-4.0
## Dataset Structure
### Data Instance
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/Gameplay_Images")
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 10000
})
})
```
```python
>>> dataset["train"].features
{'image': Image(decode=True, id=None),
'label': ClassLabel(num_classes=10, names=['Among Us', 'Apex Legends', 'Fortnite', 'Forza Horizon', 'Free Fire', 'Genshin Impact', 'God of War', 'Minecraft', 'Roblox', 'Terraria'], id=None)}
```
### Data Size
download: 2.50 GiB<br>
generated: 1.68 GiB<br>
total: 4.19 GiB
### Data Fields
- image: `Image`
- A `PIL.Image.Image object` containing the image. size=640x360
- Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`.
- label: an int classification label.
Class Label Mappings:
```json
{
"Among Us": 0,
"Apex Legends": 1,
"Fortnite": 2,
"Forza Horizon": 3,
"Free Fire": 4,
"Genshin Impact": 5,
"God of War": 6,
"Minecraft": 7,
"Roblox": 8,
"Terraria": 9
}
```
```python
>>> dataset["train"][0]
{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=640x360>,
'label': 0}
```
### Data Splits
| | train |
| ---------- | -------- |
| # of data | 10000 |
### Note
#### train_test_split
```python
>>> ds_new = dataset["train"].train_test_split(0.2, seed=42, stratify_by_column="label")
>>> ds_new
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 8000
})
test: Dataset({
features: ['image', 'label'],
num_rows: 2000
})
})
```
|
autoevaluate | null | null | null | false | 5 | false | autoevaluate/autoeval-eval-project-samsum-61336320-1319050351 | 2022-08-26T07:18:03.000Z | null | false | 863991fde636390a0678f092906ca0bbabdd8566 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-project-samsum-61336320-1319050351/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: facebook/bart-large-xsum
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: facebook/bart-large-xsum
* 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 [@hgoyal194](https://huggingface.co/hgoyal194) for evaluating this model. |
Lo | null | null | null | false | 1 | false | Lo/clip-bert-data | 2022-08-29T07:51:51.000Z | null | false | 1aa5ac59eca5b4a5922cd999d83188ee40237277 | [] | [
"arxiv:2109.11321",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/Lo/clip-bert-data/resolve/main/README.md | ---
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
---
# CLIP-BERT training data
This data was used to train the CLIP-BERT model first described in [this paper](https://arxiv.org/abs/2109.11321).
The dataset is based on text and images from MS COCO, SBU Captions, Visual Genome QA and Conceptual Captions.
The image features have been extracted using the CLIP model [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) available on Huggingface. |
Lo | null | null | null | false | 1 | false | Lo/adapt-pre-trained-VL-models-to-text-data-Wikipedia | 2022-08-29T08:26:22.000Z | null | false | bc8abd0b59c26ab913464fb535e080c27dce15ff | [] | [
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/Lo/adapt-pre-trained-VL-models-to-text-data-Wikipedia/resolve/main/README.md | ---
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
---
The Wikipedia train data used to train BERT-base baselines and adapt vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?".
The data has been created from the "20200501.en" revision of the [wikipedia dataset](https://huggingface.co/datasets/wikipedia) on Huggingface. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-emotion-2d469b4f-13675887 | 2022-08-26T09:18:42.000Z | null | false | 9006ce5811a9c44f8435dd489af9d18205f98a1d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-2d469b4f-13675887/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: autoevaluate/multi-class-classification
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: 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-project-emotion-ed9fef1a-13685888 | 2022-08-26T09:38:16.000Z | null | false | 161773d6bbc56e44575c2c3fe2eb367531843818 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-ed9fef1a-13685888/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: autoevaluate/multi-class-classification
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: 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 | 2 | false | autoevaluate/autoeval-staging-eval-project-emotion-a7ced70d-13715889 | 2022-08-26T09:52:29.000Z | null | false | 9d1adbcfd839d250e57ba00f5626c2a9bc2ba7b6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-a7ced70d-13715889/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: autoevaluate/multi-class-classification
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: 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. |
NareshIT | null | null | null | false | 1 | false | NareshIT/javatraining | 2022-08-26T09:56:08.000Z | null | false | a5841b873d4be24808b58c1273fde15f374aed41 | [] | [
"license:other"
] | https://huggingface.co/datasets/NareshIT/javatraining/resolve/main/README.md | ---
license: other
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-emotion-1d3a2bc7-13735890 | 2022-08-26T10:08:48.000Z | null | false | 41b13853d318d8f2aac4db268055ab7c99d27d9f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-1d3a2bc7-13735890/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: autoevaluate/multi-class-classification
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: 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-eval-project-LeoCordoba__CC-NEWS-ES-titles-0e1ed2c7-1320150403 | 2022-08-26T11:42:03.000Z | null | false | 6ab186192e317f65fb9f28127827c3b6a5001f30 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:LeoCordoba/CC-NEWS-ES-titles"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-project-LeoCordoba__CC-NEWS-ES-titles-0e1ed2c7-1320150403/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- LeoCordoba/CC-NEWS-ES-titles
eval_info:
task: summarization
model: josmunpen/mt5-small-spanish-summarization
metrics: []
dataset_name: LeoCordoba/CC-NEWS-ES-titles
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: output_text
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: josmunpen/mt5-small-spanish-summarization
* Dataset: LeoCordoba/CC-NEWS-ES-titles
* 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 [@LeoCordoba](https://huggingface.co/LeoCordoba) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-eval-project-LeoCordoba__CC-NEWS-ES-titles-0e1ed2c7-1320150404 | 2022-08-26T11:42:07.000Z | null | false | f8135894035cb2881d24390353fbf528fe3dc906 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:LeoCordoba/CC-NEWS-ES-titles"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-project-LeoCordoba__CC-NEWS-ES-titles-0e1ed2c7-1320150404/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- LeoCordoba/CC-NEWS-ES-titles
eval_info:
task: summarization
model: LeoCordoba/mt5-small-cc-news-es-titles
metrics: []
dataset_name: LeoCordoba/CC-NEWS-ES-titles
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: output_text
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: LeoCordoba/mt5-small-cc-news-es-titles
* Dataset: LeoCordoba/CC-NEWS-ES-titles
* 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 [@LeoCordoba](https://huggingface.co/LeoCordoba) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-b541c518-13705892 | 2022-08-26T13:03:38.000Z | null | false | 6d228ace568d2c1de21d663452f1c25938774286 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-b541c518-13705892/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-30a8951e-13725893 | 2022-08-26T13:03:44.000Z | null | false | 5261fdbd27f9caf2abd70fdb48963c829ef7c00e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-30a8951e-13725893/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-08ca88d1-13695891 | 2022-08-26T13:04:02.000Z | null | false | 9cc1c7b8d9200c633fb1fdb3870ee18a43bcbc26 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-08ca88d1-13695891/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-conll2003-90a08c43-13745894 | 2022-08-26T13:03:03.000Z | null | false | 300aa70d0b8680b78f26487f34738c3ad25d20de | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:conll2003"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-conll2003-90a08c43-13745894/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: autoevaluate/entity-extraction
metrics: []
dataset_name: conll2003
dataset_config: conll2003
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: autoevaluate/entity-extraction
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-884b60f3-13755895 | 2022-08-26T13:15:55.000Z | null | false | e897197576f659a384e06cdf1586482fa76efc87 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-884b60f3-13755895/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
Shagun5 | null | null | null | false | 1 | false | Shagun5/sandhi | 2022-08-26T14:13:49.000Z | null | false | 36a121215a184bceb6e183ddeb169beef7e8eab3 | [] | [
"license:cc-by-nc-sa-4.0"
] | https://huggingface.co/datasets/Shagun5/sandhi/resolve/main/README.md | ---
license: cc-by-nc-sa-4.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-sasha__dog-food-8a6c4abe-13775897 | 2022-08-26T14:55:53.000Z | null | false | 2e4b287dda99722789449ed901e31a6b153d7739 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:sasha/dog-food"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-sasha__dog-food-8a6c4abe-13775897/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- sasha/dog-food
eval_info:
task: image_binary_classification
model: abhishek/autotrain-dog-vs-food
metrics: ['matthews_correlation']
dataset_name: sasha/dog-food
dataset_config: sasha--dog-food
dataset_split: train
col_mapping:
image: image
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: Binary Image Classification
* Model: abhishek/autotrain-dog-vs-food
* Dataset: sasha/dog-food
* Config: sasha--dog-food
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-sasha__dog-food-8a6c4abe-13775898 | 2022-08-26T14:55:52.000Z | null | false | 5cdc512c0c73bde43a077497e24fc006f149b377 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:sasha/dog-food"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-sasha__dog-food-8a6c4abe-13775898/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- sasha/dog-food
eval_info:
task: image_binary_classification
model: sasha/dog-food-swin-tiny-patch4-window7-224
metrics: ['matthews_correlation']
dataset_name: sasha/dog-food
dataset_config: sasha--dog-food
dataset_split: train
col_mapping:
image: image
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: Binary Image Classification
* Model: sasha/dog-food-swin-tiny-patch4-window7-224
* Dataset: sasha/dog-food
* Config: sasha--dog-food
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-sasha__dog-food-8a6c4abe-13775899 | 2022-08-26T14:55:56.000Z | null | false | f3ce6b224624d2dbb8fc7ba79ddddc4eb102c89e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:sasha/dog-food"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-sasha__dog-food-8a6c4abe-13775899/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- sasha/dog-food
eval_info:
task: image_binary_classification
model: sasha/dog-food-convnext-tiny-224
metrics: ['matthews_correlation']
dataset_name: sasha/dog-food
dataset_config: sasha--dog-food
dataset_split: train
col_mapping:
image: image
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: Binary Image Classification
* Model: sasha/dog-food-convnext-tiny-224
* Dataset: sasha/dog-food
* Config: sasha--dog-food
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-sasha__dog-food-8a6c4abe-13775900 | 2022-08-26T14:56:07.000Z | null | false | 5348159e41b3268f6acbd0fb8f548e2fcaa81dca | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:sasha/dog-food"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-sasha__dog-food-8a6c4abe-13775900/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- sasha/dog-food
eval_info:
task: image_binary_classification
model: sasha/dog-food-vit-base-patch16-224-in21k
metrics: ['matthews_correlation']
dataset_name: sasha/dog-food
dataset_config: sasha--dog-food
dataset_split: train
col_mapping:
image: image
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: Binary Image Classification
* Model: sasha/dog-food-vit-base-patch16-224-in21k
* Dataset: sasha/dog-food
* Config: sasha--dog-food
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-emotion-8f618256-13785901 | 2022-08-26T14:55:39.000Z | null | false | 113d1a02c1000ed7d2fc83ea05b793aedf45ed04 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-8f618256-13785901/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: Abdelrahman-Rezk/distilbert-base-uncased-finetuned-emotion
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: Abdelrahman-Rezk/distilbert-base-uncased-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 [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-emotion-8f618256-13785902 | 2022-08-26T14:55:44.000Z | null | false | 7b656d3d66a90c5f20d5c39934ffdc4a7fca1b66 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-8f618256-13785902/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: Ahmed007/distilbert-base-uncased-finetuned-emotion
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: Ahmed007/distilbert-base-uncased-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 [@ahmetgunduz](https://huggingface.co/ahmetgunduz) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-emotion-04ae905d-13795904 | 2022-08-26T15:05:37.000Z | null | false | f806a9562420f08f3ac7be388014a057449722f5 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-04ae905d-13795904/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: tbasic5/distilbert-base-uncased-finetuned-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: tbasic5/distilbert-base-uncased-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
asaxena1990 | null | null | null | false | 1 | false | asaxena1990/dummyset2 | 2022-08-26T15:12:01.000Z | null | false | d9e7c98518e605a1caf45c3391939d2416aa0616 | [] | [
"license:cc-by-nc-sa-4.0"
] | https://huggingface.co/datasets/asaxena1990/dummyset2/resolve/main/README.md | ---
license: cc-by-nc-sa-4.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-bddd30a5-13805905 | 2022-08-26T15:27:25.000Z | null | false | aacf079fc5d248f979e4a1c7dedf1fcdc07a2b69 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-bddd30a5-13805905/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: 123tarunanand/roberta-base-finetuned
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: 123tarunanand/roberta-base-finetuned
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-glue-fa8727be-13825907 | 2022-08-26T16:43:30.000Z | null | false | 86cb54e837d8bd67b8432be7b4a7a4e73f64535f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-fa8727be-13825907/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: autoevaluate/glue-mrpc
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: test
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: autoevaluate/glue-mrpc
* Dataset: glue
* Config: mrpc
* 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-project-autoevaluate__zero-shot-classification-sample-c8bb9099-11 | 2022-08-26T19:54:42.000Z | null | false | 5eb65ec3e766cf83f00e4bd20d7f214dfee652da | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:autoevaluate/zero-shot-classification-sample"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-autoevaluate__zero-shot-classification-sample-c8bb9099-11/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- autoevaluate/zero-shot-classification-sample
eval_info:
task: zero_shot_classification
model: autoevaluate/zero-shot-classification
metrics: []
dataset_name: autoevaluate/zero-shot-classification-sample
dataset_config: autoevaluate--zero-shot-classification-sample
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: autoevaluate/zero-shot-classification
* Dataset: autoevaluate/zero-shot-classification-sample
* Config: autoevaluate--zero-shot-classification-sample
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
BDas | null | ----ArabicNLPDataset---- | The dataset, prepared in Arabic, includes 10.000 tests, 10.000 validations and 80000 train data.
The data is composed of customer comments and created from e-commerce sites. | false | 61 | false | BDas/ArabicNLPDataset | 2022-09-26T18:52:01.000Z | null | false | 322604b436887a56f8cbcdd4ed3ecf2e60a2a488 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:ar",
"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label... | https://huggingface.co/datasets/BDas/ArabicNLPDataset/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ar
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
pretty_name: 'ArabicNLPDataset'
---
# Dataset Card for "ArabicNLPDataset"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/BihterDass/ArabicTextClassificationDataset]
- **Repository:** [https://github.com/BihterDass/ArabicTextClassificationDataset]
- **Size of downloaded dataset files:** 23.5 MB
- **Size of the generated dataset:** 23.5 MB
### Dataset Summary
The dataset was compiled from user comments from e-commerce sites. It consists of 10,000 validations, 10,000 tests and 80000 train data. Data were classified into 3 classes (positive(pos), negative(neg) and natural(nor). The data is available to you on github.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
#### arabic-dataset-v1
- **Size of downloaded dataset files:** 23.5 MB
- **Size of the generated dataset:** 23.5 MB
### Data Fields
The data fields are the same among all splits.
#### arabic-dataset-v-v1
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `positive` (2), `natural` (1), `negative` (0).
### Data Splits
| |train |validation|test |
|----|--------:|---------:|---------:|
|Data| 80000 | 10000 | 10000 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@PnrSvc](https://github.com/PnrSvc) for adding this dataset. |
allenai | null | null | null | false | 1 | false | allenai/ms2_sparse_max | 2022-11-04T00:48:15.000Z | multi-document-summarization | false | 8077caffc0d89430c15479f250bdb7774e3bac7a | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"task_categories:summarization",
"task_... | https://huggingface.co/datasets/allenai/ms2_sparse_max/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
task_ids:
- summarization-other-query-based-summarization
- summarization-other-query-based-multi-document-summarization
- summarization-other-scientific-documents-summarization
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==25`
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.378 | 0.1827 | 0.1559 | 0.2188 | |
allenai | null | null | null | false | 5 | false | allenai/multinews_sparse_max | 2022-11-12T00:15:32.000Z | multi-news | false | 3c14c1694fea0f8466712a252a62f4caaf9e061d | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/allenai/multinews_sparse_max/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: multi-news
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==10`
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8775 | 0.7480 | 0.2187 | 0.8250 | |
allenai | null | null | null | false | 1 | false | allenai/ms2_sparse_mean | 2022-11-04T00:27:35.000Z | multi-document-summarization | false | 42620d2817bbfdda6b54c02e91e06280aed1736e | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"task_categories:summarization",
"task_... | https://huggingface.co/datasets/allenai/ms2_sparse_mean/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
task_ids:
- summarization-other-query-based-summarization
- summarization-other-query-based-multi-document-summarization
- summarization-other-scientific-documents-summarization
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==17`
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.3780 | 0.1827 | 0.1815 | 0.1792 | |
allenai | null | null | null | false | 2 | false | allenai/ms2_sparse_oracle | 2022-11-04T00:47:22.000Z | multi-document-summarization | false | 4d49eb87fcfcb496794b1c23c05252a744335654 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"task_categories:summarization",
"task_... | https://huggingface.co/datasets/allenai/ms2_sparse_oracle/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
task_ids:
- summarization-other-query-based-summarization
- summarization-other-query-based-multi-document-summarization
- summarization-other-scientific-documents-summarization
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.3780 | 0.1827 | 0.1827 | 0.1827 | |
allenai | null | null | null | false | 3 | false | allenai/multinews_sparse_mean | 2022-11-12T00:15:19.000Z | multi-news | false | 62858daa311434d8f3531bd4e587ba9f86a9bfba | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/allenai/multinews_sparse_mean/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: multi-news
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==3`
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8775 | 0.7480 | 0.6370 | 0.7443 | |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-autoevaluate__zero-shot-classification-sample-18ef74e8-21 | 2022-08-27T00:14:03.000Z | null | false | 21dbd148b6f8581ce774fbe1a84d225aa0dd5a06 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:autoevaluate/zero-shot-classification-sample"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-autoevaluate__zero-shot-classification-sample-18ef74e8-21/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- autoevaluate/zero-shot-classification-sample
eval_info:
task: text_zero_shot_classification
model: autoevaluate/zero-shot-classification
metrics: []
dataset_name: autoevaluate/zero-shot-classification-sample
dataset_config: autoevaluate--zero-shot-classification-sample
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: autoevaluate/zero-shot-classification
* Dataset: autoevaluate/zero-shot-classification-sample
* Config: autoevaluate--zero-shot-classification-sample
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
BDas | null | ----EnglishNLPDataset---- | The dataset, prepared in English, includes 10.000 tests, 10.000 validations and 80000 train data.
The data is composed of customer comments and created from e-commerce sites. | false | 6 | false | BDas/EnglishNLPDataset | 2022-08-27T11:13:01.000Z | null | false | a3692ff6d4f7958e6eea80025ac7ae9f4472cfe0 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label... | https://huggingface.co/datasets/BDas/EnglishNLPDataset/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
pretty_name: 'EnglishNLPDataset'
---
# Dataset Card for "EnglishNLPDataset"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/BihterDass/EnglishTextClassificationDataset]
- **Repository:** [https://github.com/BihterDass/EnglishTextClassificationDataset]
- **Size of downloaded dataset files:** 8.71 MB
- **Size of the generated dataset:** 8.71 MB
### Dataset Summary
The dataset was compiled from user comments from e-commerce sites. It consists of 10,000 validations, 10,000 tests and 80000 train data. Data were classified into 3 classes (positive(pos), negative(neg) and natural(nor). The data is available to you on github.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
#### english-dataset-v1
- **Size of downloaded dataset files:** 8.71 MB
- **Size of the generated dataset:** 8.71 MB
### Data Fields
The data fields are the same among all splits.
#### english-dataset-v-v1
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `positive` (2), `natural` (1), `negative` (0).
### Data Splits
| |train |validation|test |
|----|--------:|---------:|---------:|
|Data| 80000 | 10000 | 10000 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@PnrSvc](https://github.com/PnrSvc) for adding this dataset. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-d38f255e-13865909 | 2022-08-27T13:15:49.000Z | null | false | 2a76ba3097a5386ab779d20e6a9f86c14de143e0 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-d38f255e-13865909/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: deepset/xlm-roberta-base-squad2
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/xlm-roberta-base-squad2
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sakamoto](https://huggingface.co/sakamoto) for evaluating this model. |
sazibc | null | null | null | false | 1 | false | sazibc/flowers | 2022-08-27T20:19:25.000Z | null | false | cee0bbe45cd41cfbf181459fa786cedc4f075542 | [] | [
"license:mit"
] | https://huggingface.co/datasets/sazibc/flowers/resolve/main/README.md | ---
license: mit
---
|
priyank-m | null | null | null | false | 103 | false | priyank-m/SROIE_2019_text_recognition | 2022-08-27T21:38:24.000Z | null | false | 04f6537e418eeb88863d617eb27817cc496522d7 | [] | [
"language:en",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"tags:text-recognition",
"tags:recognition",
"task_categories:image-to-text",
"task_ids:image-captioning"
] | https://huggingface.co/datasets/priyank-m/SROIE_2019_text_recognition/resolve/main/README.md | ---
annotations_creators: []
language:
- en
language_creators: []
license: []
multilinguality:
- monolingual
pretty_name: SROIE_2019_text_recognition
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- text-recognition
- recognition
task_categories:
- image-to-text
task_ids:
- image-captioning
---
This dataset we prepared using the Scanned receipts OCR and information extraction(SROIE) dataset.
The SROIE dataset contains 973 scanned receipts in English language.
Cropping the bounding boxes from each of the receipts to generate this text-recognition dataset resulted in 33626 images for train set and 18704 images for the test set.
The text annotations for all the images inside a split are stored in a metadata.jsonl file.
usage:
from dataset import load_dataset
data = load_dataset("priyank-m/SROIE_2019_text_recognition")
source of raw SROIE dataset:
https://www.kaggle.com/datasets/urbikn/sroie-datasetv2 |
jamescalam | null | @InProceedings{huggingface:dataset,
title = {Unsplash Lite Dataset 1.2.0 Photos},
author={Unsplash},
year={2022}
} | This is a dataset that streams photos data from the Unsplash 25K servers. | false | 13 | false | jamescalam/unsplash-25k-photos | 2022-09-13T13:02:46.000Z | null | false | ae9e759dd31d60479354cc06e4f4291c0c27bbca | [] | [
"annotations_creators:found",
"language:en",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"tags:images",
"tags:unsplash",
"tags:photos",
"task_categories:image-to-image",
"task_categories:image-classification",
"task_categories:image-to-text",
"task_c... | https://huggingface.co/datasets/jamescalam/unsplash-25k-photos/resolve/main/README.md | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: Unsplash Lite 25K Photos
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- images
- unsplash
- photos
task_categories:
- image-to-image
- image-classification
- image-to-text
- text-to-image
- zero-shot-image-classification
task_ids: []
---
# Unsplash Lite Dataset Photos
This dataset is linked to the Unsplash Lite dataset containing data on 25K images from Unsplash. The dataset here only includes data from a single file `photos.tsv000`. The dataset builder script streams this data directly from the Unsplash 25K dataset source.
For full details, please see the [Unsplash Dataset GitHub repo](https://github.com/unsplash/datasets), or read the preview (copied from the repo) below.
---
# The Unsplash Dataset
The Unsplash Dataset is made up of over 250,000+ contributing global photographers and data sourced from hundreds of millions of searches across a nearly unlimited number of uses and contexts. Due to the breadth of intent and semantics contained within the Unsplash dataset, it enables new opportunities for research and learning.
The Unsplash Dataset is offered in two datasets:
- the Lite dataset: available for commercial and noncommercial usage, containing 25k nature-themed Unsplash photos, 25k keywords, and 1M searches
- the Full dataset: available for noncommercial usage, containing 3M+ high-quality Unsplash photos, 5M keywords, and over 250M searches
As the Unsplash library continues to grow, we’ll release updates to the dataset with new fields and new images, with each subsequent release being [semantically versioned](https://semver.org/).
We welcome any feedback regarding the content of the datasets or their format. With your input, we hope to close the gap between the data we provide and the data that you would like to leverage. You can [open an issue](https://github.com/unsplash/datasets/issues/new/choose) to report a problem or to let us know what you would like to see in the next release of the datasets.
For more on the Unsplash Dataset, see [our announcement](https://unsplash.com/blog/the-unsplash-dataset/) and [site](https://unsplash.com/data).
## Download
### Lite Dataset
The Lite dataset contains all of the same fields as the Full dataset, but is limited to ~25,000 photos. It can be used for both commercial and non-commercial usage, provided you abide by [the terms](https://github.com/unsplash/datasets/blob/master/TERMS.md).
[⬇️ Download the Lite dataset](https://unsplash.com/data/lite/latest) [~650MB compressed, ~1.4GB raw]
### Full Dataset
The Full dataset is available for non-commercial usage and all uses must abide by [the terms](https://github.com/unsplash/datasets/blob/master/TERMS.md). To access, please go to [unsplash.com/data](https://unsplash.com/data) and request access. The dataset weighs ~20 GB compressed (~43GB raw)).
## Documentation
See the [documentation for a complete list of tables and fields](https://github.com/unsplash/datasets/blob/master/DOCS.md).
## Usage
You can follow these examples to load the dataset in these common formats:
- [Load the dataset in a PostgreSQL database](https://github.com/unsplash/datasets/tree/master/how-to/psql)
- [Load the dataset in a Python environment](https://github.com/unsplash/datasets/tree/master/how-to/python)
- [Submit an example doc](https://github.com/unsplash/datasets/blob/master/how-to/README.md#submit-an-example)
## Share your work
We're making this data open and available with the hopes of enabling researchers and developers to discover interesting and useful connections in the data.
We'd love to see what you create, whether that's a research paper, a machine learning model, a blog post, or just an interesting discovery in the data. Send us an email at [data@unsplash.com](mailto:data@unsplash.com).
If you're using the dataset in a research paper, you can attribute the dataset as `Unsplash Lite Dataset 1.2.0` or `Unsplash Full Dataset 1.2.0` and link to the permalink [`unsplash.com/data`](https://unsplash.com/data).
----
The Unsplash Dataset is made available for research purposes. [It cannot be used to redistribute the images contained within](https://github.com/unsplash/datasets/blob/master/TERMS.md). To use the Unsplash library in a product, see [the Unsplash API](https://unsplash.com/developers).
 |
teticio | null | null | null | false | 17 | false | teticio/audio-diffusion-breaks-256 | 2022-11-09T10:50:38.000Z | null | false | 82e568dfe8ee3e016c18290dbbbddd010479eb87 | [] | [
"size_categories:10K<n<100K",
"tags:audio",
"tags:spectrograms",
"task_categories:image-to-image"
] | https://huggingface.co/datasets/teticio/audio-diffusion-breaks-256/resolve/main/README.md | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: Mel spectrograms of sampled music
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- audio
- spectrograms
task_categories:
- image-to-image
task_ids: []
---
30,000 256x256 mel spectrograms of 5 second samples that have been used in music, sourced from [WhoSampled](https://whosampled.com) and [YouTube](https://youtube.com). The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models.
```
x_res = 256
y_res = 256
sample_rate = 22050
n_fft = 2048
hop_length = 512
``` |
anonymousdeepcc | null | null | null | false | 1 | false | anonymousdeepcc/DeepCC | 2022-09-05T01:15:43.000Z | null | false | 214dbf214e508c61aeaf431ef753e60ab5e263aa | [] | [] | https://huggingface.co/datasets/anonymousdeepcc/DeepCC/resolve/main/README.md | In this repository, we have all the datasets and source code used to develop DeepCC. Below, we describe each files contained in the repository:
1) It contains the raw dataset used for DeepCC, Py150, name as DeepCCDatasetPy150.zip
2) It contains the extracted dataset from the Py150 dataset inside processed_dataset.zip
3) It contains the dataset extracting code inside processed_dataset.zip
4) It contains the source code to build the model pipeline, train the model, and evaluate the model inside the DeepCC.ipynb. |
QuoQA-NLP | null | null | null | false | 1 | false | QuoQA-NLP/KoCC12M | 2022-08-28T06:44:47.000Z | null | false | 5cadc7b30860162ea82aa2729102c02485d152b3 | [] | [] | https://huggingface.co/datasets/QuoQA-NLP/KoCC12M/resolve/main/README.md | CC12M of flax-community/conceptual-captions-12 translated from English to Korean. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-c78baf7d-13885910 | 2022-08-28T10:52:35.000Z | null | false | 71d5c298b9dc85f34b468eb393301fa436405bbb | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-c78baf7d-13885910/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: nlpconnect/deberta-v3-xsmall-squad2
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: nlpconnect/deberta-v3-xsmall-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 [@ankur310974](https://huggingface.co/ankur310974) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-squad-4690f1f9-13895911 | 2022-08-28T10:52:24.000Z | null | false | 7ad42c0cbd4e102579d6323231e05a87c739318b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-4690f1f9-13895911/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: nlpconnect/deberta-v3-xsmall-squad2
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: nlpconnect/deberta-v3-xsmall-squad2
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ankur310794](https://huggingface.co/ankur310794) for evaluating this model. |
kokhayas | null | null | null | false | 1 | false | kokhayas/english-debate-motions-utds | 2022-08-30T03:18:43.000Z | null | false | 2afbf37414683a8ad881fe0dc8913b1f246b9aa7 | [] | [] | https://huggingface.co/datasets/kokhayas/english-debate-motions-utds/resolve/main/README.md | English Debate Motions gathered by University of Tokyo Debate Society
@misc{english-debate-motions-utds,
title={english-debate-motions-utds},
author={members of the University of Tokyo Debate Society},
year={2022},
}
|
unpredictable | null | null | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 1 | false | unpredictable/unpredictable_full | 2022-08-28T18:42:31.000Z | null | false | 72aa912bbf09c96c6cf38bb76bec24e8d8a82367 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:te... | https://huggingface.co/datasets/unpredictable/unpredictable_full/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-full
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-full" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/AnonCodeShare/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/unpredictable/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/unpredictable/unpredictable_support-google-com)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Licensing Information
Apache 2.0 |
unpredictable | null | null | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 1 | false | unpredictable/unpredictable_5k | 2022-08-28T18:13:41.000Z | null | false | ec38db9a85ca5dca7ef9211bbb73cc27e1a47208 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:te... | https://huggingface.co/datasets/unpredictable/unpredictable_5k/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-5k
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-5k" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/AnonCodeShare/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/unpredictable/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/unpredictable/unpredictable_support-google-com)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Licensing Information
Apache 2.0
|
unpredictable | null | null | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 1 | false | unpredictable/unpredictable_support-google-com | 2022-08-28T18:25:26.000Z | null | false | 76db35834d995d0bd5d14d1352277461fe3f225f | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:te... | https://huggingface.co/datasets/unpredictable/unpredictable_support-google-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-support-google-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-support-google-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/AnonCodeShare/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/unpredictable/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/unpredictable/unpredictable_support-google-com)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Licensing Information
Apache 2.0 |
unpredictable | null | null | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 1 | false | unpredictable/unpredictable_unique | 2022-08-28T18:26:18.000Z | null | false | 7b0b1a6c2c61cc1f9304725ceb54c826be65816f | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:te... | https://huggingface.co/datasets/unpredictable/unpredictable_unique/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-unique
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-unique" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/AnonCodeShare/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/unpredictable/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/unpredictable/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/unpredictable/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/unpredictable/unpredictable_support-google-com)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Licensing Information
Apache 2.0 |
williamlee | null | null | null | false | 1 | false | williamlee/test2 | 2022-08-29T02:00:50.000Z | null | false | 103c2fe8cb50ef4f095da366e90254008bae0bb8 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/williamlee/test2/resolve/main/README.md | ---
license: apache-2.0
---
|
Lo | null | null | null | false | 1 | false | Lo/adapt-pre-trained-VL-models-to-text-data-Wikipedia-finetune | 2022-08-29T08:27:33.000Z | null | false | 5f17b065b8739c725a84d3a6965ed7f040cdae04 | [] | [
"language:en",
"license:cc-by-sa-3.0",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/Lo/adapt-pre-trained-VL-models-to-text-data-Wikipedia-finetune/resolve/main/README.md | ---
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
---
The Wikipedia finetune data used to train visual features for the adaption of vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?".
The data has been created from the "20200501.en" revision of the [wikipedia dataset](https://huggingface.co/datasets/wikipedia) on Huggingface.
|
Lo | null | null | null | false | 1 | false | Lo/adapt-pre-trained-VL-models-to-text-data-LXMERT | 2022-08-29T08:30:05.000Z | null | false | d6fe56688ae0435f11bcc1860fe7de01e0d3ffe4 | [] | [
"language:en",
"license:mit",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/Lo/adapt-pre-trained-VL-models-to-text-data-LXMERT/resolve/main/README.md | ---
language:
- en
license:
- mit
multilinguality:
- monolingual
---
The LXMERT text train data used to train BERT-base baselines and adapt vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?".
The data has been created from the data made available by the [LXMERT repo](https://github.com/airsplay/lxmert).
|
Lo | null | null | null | false | 1 | false | Lo/adapt-pre-trained-VL-models-to-text-data-LXMERT-finetune | 2022-08-29T08:31:45.000Z | null | false | ea1623c9c1f7b042aff76cbcf1ca5c0a3ef8e114 | [] | [
"language:en",
"license:mit",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/Lo/adapt-pre-trained-VL-models-to-text-data-LXMERT-finetune/resolve/main/README.md | ---
language:
- en
license:
- mit
multilinguality:
- monolingual
---
The LXMERT text finetune data used to train visual features for the adaption of vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?".
The data has been created from the data made available by the [LXMERT repo](https://github.com/airsplay/lxmert).
|
ashwinperti | null | null | null | false | 1 | false | ashwinperti/yelpnew | 2022-08-29T08:38:42.000Z | null | false | 3d2ddf11220d67832edb32043e9abdbfb8d035af | [] | [
"license:eupl-1.1"
] | https://huggingface.co/datasets/ashwinperti/yelpnew/resolve/main/README.md | ---
license: eupl-1.1
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-glue-f7900ebf-13965913 | 2022-08-29T09:37:29.000Z | null | false | 683b752aaead07750f544d18639ee871f912a697 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-f7900ebf-13965913/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: binary_classification
model: autoevaluate/binary-classification
metrics: []
dataset_name: glue
dataset_config: sst2
dataset_split: validation
col_mapping:
text: 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: Binary Text Classification
* Model: autoevaluate/binary-classification
* Dataset: glue
* Config: sst2
* 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-glue-e9a4b61a-13985914 | 2022-08-29T10:05:51.000Z | null | false | 513ed4cfbc29df4be9c167bef472b3a4aeae7dca | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-e9a4b61a-13985914/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: autoevaluate/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: autoevaluate/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 | 1 | false | autoevaluate/autoeval-staging-eval-project-glue-4805e982-13995915 | 2022-08-29T10:07:21.000Z | null | false | 6b3840bc7bb94a480e42c79200caf31a3b598fd1 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-4805e982-13995915/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: autoevaluate/glue-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: autoevaluate/glue-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 | 1 | false | autoevaluate/autoeval-staging-eval-project-autoevaluate__squad-sample-11b52eb1-14005916 | 2022-08-29T10:25:07.000Z | null | false | 9cee6f8497cb95ce974e7e7e511c347c5a572d8f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:autoevaluate/squad-sample"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-autoevaluate__squad-sample-11b52eb1-14005916/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- autoevaluate/squad-sample
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
dataset_name: autoevaluate/squad-sample
dataset_config: autoevaluate--squad-sample
dataset_split: test
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: autoevaluate/extractive-question-answering
* Dataset: autoevaluate/squad-sample
* Config: autoevaluate--squad-sample
* 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-project-glue-f16e6c43-14015917 | 2022-08-29T12:07:18.000Z | null | false | ec3c96f7624cc7b419297c51779b9800826a818c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-f16e6c43-14015917/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: mrm8488/deberta-v3-small-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: mrm8488/deberta-v3-small-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-project-emotion-af6a16fe-14025918 | 2022-08-29T12:07:19.000Z | null | false | 64dea239da2de88405fb3120dc26f511eaff7891 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-af6a16fe-14025918/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: anindabitm/sagemaker-distilbert-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: anindabitm/sagemaker-distilbert-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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-samsum-afdf25d0-14035919 | 2022-08-29T12:27:41.000Z | null | false | 3160df47c1c1eef5087fa86fb551b61adfe2f552 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-samsum-afdf25d0-14035919/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: ARTeLab/it5-summarization-fanpage
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: ARTeLab/it5-summarization-fanpage
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-samsum-afdf25d0-14035921 | 2022-08-29T12:29:49.000Z | null | false | a4895d7e5d6f96414fce19ef999a68f0adc509e9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-samsum-afdf25d0-14035921/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch
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: Ameer05/bart-large-cnn-samsum-rescom-finetuned-resume-summarizer-10-epoch
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-squad_v2-82949658-14045922 | 2022-08-29T12:29:21.000Z | null | false | 60630ce757b999088709d5d6816592c9b7fdbd89 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-82949658-14045922/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Adrian/distilbert-base-uncased-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Adrian/distilbert-base-uncased-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-squad_v2-82949658-14045923 | 2022-08-29T12:30:22.000Z | null | false | f94df08f28998f2e61b9017f89692664e0530679 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-82949658-14045923/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Aiyshwariya/bert-finetuned-squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Aiyshwariya/bert-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-wmt16-a5e2262a-14055924 | 2022-08-29T12:28:47.000Z | null | false | c82c3e92c8ce1011435ff34246d830634d4f3ab3 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:wmt16"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-wmt16-a5e2262a-14055924/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- wmt16
eval_info:
task: translation
model: Lvxue/finetuned-mt5-small-10epoch
metrics: []
dataset_name: wmt16
dataset_config: de-en
dataset_split: test
col_mapping:
source: translation.en
target: translation.de
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: Lvxue/finetuned-mt5-small-10epoch
* Dataset: wmt16
* Config: de-en
* 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-project-glue-c88eb4d4-14065928 | 2022-08-29T12:27:58.000Z | null | false | d04e8305a7b1fe40ced830c06b1b435aa0252f6a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-c88eb4d4-14065928/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: binary_classification
model: mrm8488/deberta-v3-small-finetuned-cola
metrics: []
dataset_name: glue
dataset_config: cola
dataset_split: validation
col_mapping:
text: 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: Binary Text Classification
* Model: mrm8488/deberta-v3-small-finetuned-cola
* Dataset: glue
* Config: cola
* 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-project-glue-ca80bfc9-14105932 | 2022-08-29T12:31:56.000Z | null | false | a45bcb2ef853109b882d5f6c7cb99c3bd54bb223 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-ca80bfc9-14105932/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: mrm8488/deberta-v3-large-finetuned-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: mrm8488/deberta-v3-large-finetuned-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-project-glue-91d4fe29-14115933 | 2022-08-29T12:28:56.000Z | null | false | 81d0f6caa3ab9c6300a0bab43cfb0fdc10d53b05 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-91d4fe29-14115933/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: mrm8488/deberta-v3-small-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: mrm8488/deberta-v3-small-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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-staging-eval-project-glue-f56b6c46-14085930 | 2022-08-29T12:28:55.000Z | null | false | ca26aa07d44b0cf23ae600e6fcf1690a0c2992c5 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-f56b6c46-14085930/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: train
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: train
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-project-glue-f1585abe-14095931 | 2022-08-29T12:31:25.000Z | null | false | 3034f92e343d8e9629ba792ece2bfbfb067a5181 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-f1585abe-14095931/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: JeremiahZ/roberta-base-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: JeremiahZ/roberta-base-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-project-glue-f6cacc01-14075929 | 2022-08-29T12:28:52.000Z | null | false | a4c35f2ecd42cb2bfca9ea1cda04793fae25b6b9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-f6cacc01-14075929/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: binary_classification
model: mrm8488/deberta-v3-small-finetuned-sst2
metrics: []
dataset_name: glue
dataset_config: sst2
dataset_split: validation
col_mapping:
text: 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: Binary Text Classification
* Model: mrm8488/deberta-v3-small-finetuned-sst2
* Dataset: glue
* Config: sst2
* 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-project-glue-67467c9c-14145936 | 2022-08-29T12:29:38.000Z | null | false | 2536141082d13670fa08230b1c7f2cd4c8ad43f1 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-67467c9c-14145936/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: Alireza1044/mobilebert_rte
metrics: []
dataset_name: glue
dataset_config: rte
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: Alireza1044/mobilebert_rte
* Dataset: glue
* Config: rte
* 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-project-glue-67467c9c-14145935 | 2022-08-29T12:30:00.000Z | null | false | b090c700a076dcf043522e5ddce467f6add05a67 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:glue"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-glue-67467c9c-14145935/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: JeremiahZ/roberta-base-rte
metrics: []
dataset_name: glue
dataset_config: rte
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: JeremiahZ/roberta-base-rte
* Dataset: glue
* Config: rte
* 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. |
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