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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
onon214/mongolian-ner-demo | onon214 | 2022-07-25T14:10:51Z | 13 | 0 | null | [
"region:us"
] | 2022-07-25T14:10:51Z | 2022-07-25T14:10:49.000Z | 2022-07-25T14:10:49 | Entry not found | [
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autoevaluate/autoeval-staging-eval-project-conll2003-2dc2f6d8-11805572 | autoevaluate | 2022-07-25T14:27:10Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-25T14:27:10Z | 2022-07-25T14:25:58.000Z | 2022-07-25T14:25:58 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: AJGP/bert-finetuned-ner
metrics: []
dataset_name: conll2003
dataset_config: conll2003
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: AJGP/bert-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@hrezaeim](https://huggingface.co/hrezaeim) for evaluating this model. | [
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hdamghanian/Stock-QA-fa | hdamghanian | 2022-07-25T15:16:43Z | 13 | 0 | null | [
"license:mit",
"region:us"
] | 2022-07-25T15:16:43Z | 2022-07-25T15:06:08.000Z | 2022-07-25T15:06:08 | ---
license: mit
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
This dataset is to be served as a reference for QA tasks.
### Languages
Persian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
All annotations are done according to the SQuAD2.0 data format.
### Source Data
#### Initial Data Collection and Normalization
All context and some of questions are retrieved from [Faradars Introductory Course to Stock Market](https://blog.faradars.org/%d8%a2%d9%85%d9%88%d8%b2%d8%b4-%d8%a8%d9%88%d8%b1%d8%b3-%d8%b1%d8%a7%db%8c%da%af%d8%a7%d9%86/).
#### Who are the source language producers?
Persian (farsi)
### Annotations
#### Annotation process
All annotations are done via Deepset Haystack annotation tool.
#### Who are the annotators?
Hesam Damghanian (this HF account)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
| [
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0.06832364946... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cakiki/ASE_runs | cakiki | 2022-07-25T18:11:20Z | 13 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-07-25T18:11:20Z | 2022-07-25T18:09:35.000Z | 2022-07-25T18:09:35 | ---
license: apache-2.0
---
| [
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anzorq/kbd_lat-835k_ru-3M | anzorq | 2022-07-25T23:26:41Z | 13 | 0 | null | [
"license:unknown",
"region:us"
] | 2022-07-25T23:26:41Z | 2022-07-25T18:37:51.000Z | 2022-07-25T18:37:51 | ---
license: unknown
---
Kbd latin script: 835k lines from a scraped pile
ru: 3M lines from Wiki (OPUS) | [
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emilylearning/cond_ft_subreddit_on_reddit__prcnt_100__test_run_False__bert-base-uncased | emilylearning | 2022-07-25T20:06:37Z | 13 | 0 | null | [
"region:us"
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autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825575 | autoevaluate | 2022-07-25T22:33:19Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-25T22:33:19Z | 2022-07-25T22:30:32.000Z | 2022-07-25T22:30:32 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: deepset/deberta-v3-large-squad2
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/deberta-v3-large-squad2
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825576 | autoevaluate | 2022-07-25T22:32:36Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-25T22:32:36Z | 2022-07-25T22:30:35.000Z | 2022-07-25T22:30:35 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: deepset/bert-large-uncased-whole-word-masking-squad2
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-large-uncased-whole-word-masking-squad2
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-adversarial_qa-58460439-11825574 | autoevaluate | 2022-07-25T22:39:49Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-25T22:39:49Z | 2022-07-25T22:32:16.000Z | 2022-07-25T22:32:16 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: mbartolo/electra-large-synqa
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mbartolo/electra-large-synqa
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo](https://huggingface.co/mbartolo) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835579 | autoevaluate | 2022-07-25T22:39:01Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-25T22:39:01Z | 2022-07-25T22:34:58.000Z | 2022-07-25T22:34:58 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: deepset/roberta-large-squad2
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/roberta-large-squad2
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo ](https://huggingface.co/mbartolo ) for evaluating this model. | [
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chintagunta85/ncbi_disease | chintagunta85 | 2022-07-28T14:15:57Z | 13 | 0 | null | [
"region:us"
] | 2022-07-28T14:15:57Z | 2022-07-27T13:21:59.000Z | 2022-07-27T13:21:59 | Entry not found | [
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voidful/DRCDC | voidful | 2022-07-28T08:54:19Z | 13 | 0 | null | [
"region:us"
] | 2022-07-28T08:54:19Z | 2022-07-28T08:53:19.000Z | 2022-07-28T08:53:19 | Entry not found | [
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autoevaluate/autoeval-staging-eval-project-xsum-20a28003-12045607 | autoevaluate | 2022-07-28T20:27:48Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-28T20:27:48Z | 2022-07-28T20:00:01.000Z | 2022-07-28T20:00:01 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: nbroad/longt5-base-global-mediasum
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: nbroad/longt5-base-global-mediasum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model. | [
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gorkaartola/SDG_queries | gorkaartola | 2023-04-13T12:49:05Z | 13 | 0 | null | [
"region:us"
] | 2023-04-13T12:49:05Z | 2022-07-28T20:16:18.000Z | 2022-07-28T20:16:18 | Entry not found | [
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ahadda5/cve150k | ahadda5 | 2022-08-29T09:16:51Z | 13 | 0 | null | [
"region:us"
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ErickP/glocon | ErickP | 2022-08-01T16:20:30Z | 13 | 0 | null | [
"region:us"
] | 2022-08-01T16:20:30Z | 2022-08-01T16:19:38.000Z | 2022-08-01T16:19:38 | Entry not found | [
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Anas2000/testo | Anas2000 | 2022-08-02T12:47:27Z | 13 | 0 | null | [
"region:us"
] | 2022-08-02T12:47:27Z | 2022-08-01T17:10:45.000Z | 2022-08-01T17:10:45 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ShreySavaliya/TextSummarisation | ShreySavaliya | 2022-08-17T06:03:10Z | 13 | 0 | null | [
"language:unk",
"autotrain",
"summarization",
"region:us"
] | 2022-08-17T06:03:10Z | 2022-08-02T06:27:58.000Z | 2022-08-02T06:27:58 | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- vishw2703/autotrain-data-unisumm_3
co2_eq_emissions:
emissions: 1368.894142563709
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1228646724
- CO2 Emissions (in grams): 1368.8941
## Validation Metrics
- Loss: 2.319
- Rouge1: 43.703
- Rouge2: 16.106
- RougeL: 23.715
- RougeLsum: 38.984
- Gen Len: 141.091
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/vishw2703/autotrain-unisumm_3-1228646724
``` | [
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autoevaluate/autoeval-staging-eval-project-ml6team__cnn_dailymail_nl-bfaf23ee-12505670 | autoevaluate | 2022-08-03T21:16:04Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-03T21:16:04Z | 2022-08-03T19:33:11.000Z | 2022-08-03T19:33:11 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ml6team/cnn_dailymail_nl
eval_info:
task: summarization
model: yhavinga/long-t5-tglobal-small-dutch-cnn
metrics: []
dataset_name: ml6team/cnn_dailymail_nl
dataset_config: default
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: yhavinga/long-t5-tglobal-small-dutch-cnn
* Dataset: ml6team/cnn_dailymail_nl
* 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 [@yhavinga](https://huggingface.co/yhavinga) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-ben-yu__ms2_combined-823f066f-12515671 | autoevaluate | 2022-08-04T20:56:42Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-04T20:56:42Z | 2022-08-04T03:44:17.000Z | 2022-08-04T03:44:17 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ben-yu/ms2_combined
eval_info:
task: summarization
model: Blaise-g/long_t5_global_large_pubmed_explanatory
metrics: []
dataset_name: ben-yu/ms2_combined
dataset_config: ben-yu--ms2_combined
dataset_split: train
col_mapping:
text: Abstract
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: Summarization
* Model: Blaise-g/long_t5_global_large_pubmed_explanatory
* Dataset: ben-yu/ms2_combined
* Config: ben-yu--ms2_combined
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ben-yu](https://huggingface.co/ben-yu) for evaluating this model. | [
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Epidot/fuetal2017_highlights_temporal_preprocessed_TwitchLeagueBert_oversampled | Epidot | 2022-08-08T12:02:14Z | 13 | 0 | null | [
"region:us"
] | 2022-08-08T12:02:14Z | 2022-08-08T08:40:24.000Z | 2022-08-08T08:40:24 | Entry not found | [
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DeveloperOats/Million_News_Headlines | DeveloperOats | 2022-08-08T14:56:01Z | 13 | 1 | null | [
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2022-08-08T14:56:01Z | 2022-08-08T09:24:34.000Z | 2022-08-08T09:24:34 | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: million news headline
size_categories:
- 1M<n<10M
source_datasets: []
tags: []
task_categories: []
task_ids: []
---
About Dataset
Context
This contains data of news headlines published over a period of nineteen years.
Sourced from the reputable Australian news source ABC (Australian Broadcasting Corporation)
Agency Site: (http://www.abc.net.au)
Content
Format: CSV ; Single File
publish_date: Date of publishing for the article in yyyyMMdd format
headline_text: Text of the headline in Ascii , English , lowercase
Start Date: 2003-02-19 ; End Date: 2021-12-31
Inspiration
I look at this news dataset as a summarised historical record of noteworthy events in the globe from early-2003 to end-2021 with a more granular focus on Australia.
This includes the entire corpus of articles published by the abcnews website in the given date range.
With a volume of two hundred articles per day and a good focus on international news, we can be fairly certain that every event of significance has been captured here.
Digging into the keywords, one can see all the important episodes shaping the last decade and how they evolved over time.
Ex: afghanistan war, financial crisis, multiple elections, ecological disasters, terrorism, famous people, criminal activity et cetera.
Similar Work
Similar news datasets exploring other attributes, countries and topics can be seen on my profile.
Most kernals can be reused with minimal changes across these news datasets.
Prepared by Rohit Kulkarni
Taken from https://www.kaggle.com/datasets/therohk/million-headlines | [
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0.40326380729... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jakartaresearch/cerpen-corpus | jakartaresearch | 2022-11-28T04:15:40Z | 13 | 1 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"cerpen",
"shor... | 2022-11-28T04:15:40Z | 2022-08-08T14:05:26.000Z | 2022-08-08T14:05:26 | ---
annotations_creators:
- no-annotation
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Small Indonesian Short Story Corpus
size_categories:
- n<1K
- 10K<n<100K
source_datasets:
- original
tags:
- cerpen
- short-story
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for Cerpen Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a small size for Indonesian short story gathered from the internet.
We keep the large size for internal research. if you are interested, please join to [our discord server](https://discord.gg/6v28dq8dRE)
### 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 [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. | [
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0.36449185013771057... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
asaxena1990/Dummy_dataset | asaxena1990 | 2022-09-05T01:29:27Z | 13 | 0 | null | [
"license:cc-by-sa-4.0",
"region:us"
] | 2022-09-05T01:29:27Z | 2022-08-08T19:23:48.000Z | 2022-08-08T19:23:48 | ---
license: cc-by-sa-4.0
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- expert-generated
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Massive E-commerce Dataset for Retail and Insurance domain.
size_categories:
- n<1K
source_datasets:
- original
tags:
- chatbots
- e-commerce
- retail
- insurance
- consumer
- consumer goods
task_categories:
- question-answering
- text-retrieval
- text2text-generation
- other
- translation
- conversational
task_ids:
- extractive-qa
- closed-domain-qa
- utterance-retrieval
- document-retrieval
- closed-domain-qa
- open-book-qa
- closed-book-qa
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction | [
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0.29930639266967... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925730 | autoevaluate | 2022-08-11T14:02:39Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-11T14:02:39Z | 2022-08-11T13:21:28.000Z | 2022-08-11T13:21:28 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-cnn
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925731 | autoevaluate | 2022-08-11T14:00:18Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-11T14:00:18Z | 2022-08-11T13:28:39.000Z | 2022-08-11T13:28:39 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-xsum
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925732 | autoevaluate | 2022-08-11T14:19:44Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-11T14:19:44Z | 2022-08-11T13:46:05.000Z | 2022-08-11T13:46:05 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-cnn-12-6
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-cnn-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925733 | autoevaluate | 2022-08-11T14:11:20Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-11T14:11:20Z | 2022-08-11T13:47:19.000Z | 2022-08-11T13:47:19 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: sshleifer/distilbart-xsum-12-6
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: sshleifer/distilbart-xsum-12-6
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model. | [
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ChristophSchuhmann/improved_aesthetics_4.75plus | ChristophSchuhmann | 2022-08-13T18:16:44Z | 13 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-08-13T18:16:44Z | 2022-08-11T13:47:47.000Z | 2022-08-11T13:47:47 | ---
license: apache-2.0
---
| [
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jokerak/imagenet100 | jokerak | 2022-08-15T11:51:06Z | 13 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-08-15T11:51:06Z | 2022-08-15T08:44:42.000Z | 2022-08-15T08:44:42 | ---
license: apache-2.0
---
| [
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pauwdanny/indonesian_hoax_news_dataset | pauwdanny | 2022-08-16T05:23:14Z | 13 | 1 | null | [
"region:us"
] | 2022-08-16T05:23:14Z | 2022-08-16T05:18:42.000Z | 2022-08-16T05:18:42 | Entry not found | [
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sepidmnorozy/Indonesian_sentiment | sepidmnorozy | 2022-08-16T09:23:21Z | 13 | 1 | null | [
"region:us"
] | 2022-08-16T09:23:21Z | 2022-08-16T09:22:30.000Z | 2022-08-16T09:22:30 | Entry not found | [
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0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sepidmnorozy/Turkish_sentiment | sepidmnorozy | 2022-08-16T10:03:06Z | 13 | 0 | null | [
"region:us"
] | 2022-08-16T10:03:06Z | 2022-08-16T10:02:23.000Z | 2022-08-16T10:02:23 | Entry not found | [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cakiki/arxiv-taxonomy | cakiki | 2022-08-23T13:57:47Z | 13 | 1 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-08-23T13:57:47Z | 2022-08-18T12:19:51.000Z | 2022-08-18T12:19:51 | ---
license: cc-by-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
dataset_name | [
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allenai/multinews_sparse_max | allenai | 2022-11-24T21:34:53Z | 13 | 0 | multi-news | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
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"language:en",
"license:other",
"region:us"
] | 2022-11-24T21:34:53Z | 2022-08-26T21:41:47.000Z | 2022-08-26T21:41:47 | ---
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 `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8793 | 0.7460 | 0.2213 | 0.8264 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8748 | 0.7453 | 0.2173 | 0.8232 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8775 | 0.7480 | 0.2187 | 0.8250 | | [
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QuoQA-NLP/KoCC12M | QuoQA-NLP | 2022-08-28T06:44:47Z | 13 | 0 | null | [
"region:us"
] | 2022-08-28T06:44:47Z | 2022-08-28T06:30:31.000Z | 2022-08-28T06:30:31 | CC12M of flax-community/conceptual-captions-12 translated from English to Korean. | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
khalidalt/SANAD | khalidalt | 2022-09-03T19:36:00Z | 13 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-09-03T19:36:00Z | 2022-08-31T13:34:53.000Z | 2022-08-31T13:34:53 | ---
license: cc-by-4.0
---
# Dataset Card for SANAD
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:https://data.mendeley.com/datasets/57zpx667y9/2**
### Dataset Summary
SANAD Dataset is a large collection of Arabic news articles that can be used in different Arabic NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular news websites: AlKhaleej, AlArabiya and Akhbarona. All datasets have seven categories [Culture, Finance, Medical, Politics, Religion, Sports and Tech], except AlArabiya which doesn’t have [Religion]. SANAD contains a total number of 190k+ articles.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
license: cc-by-4.0
### Citation Information
```
@article{einea2019sanad,
title={Sanad: Single-label arabic news articles dataset for automatic text categorization},
author={Einea, Omar and Elnagar, Ashraf and Al Debsi, Ridhwan},
journal={Data in brief},
volume={25},
pages={104076},
year={2019},
publisher={Elsevier}
}
```
### Contributions
| [
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0.34482041001319885... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906066 | autoevaluate | 2022-08-31T21:52:06Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T21:52:06Z | 2022-08-31T21:49:15.000Z | 2022-08-31T21:49:15 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-base-squad2
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/roberta-base-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | [
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-0.0209747869521... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906067 | autoevaluate | 2022-08-31T21:53:49Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T21:53:49Z | 2022-08-31T21:49:20.000Z | 2022-08-31T21:49:20 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-large-squad2
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/roberta-large-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906068 | autoevaluate | 2022-08-31T21:55:28Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T21:55:28Z | 2022-08-31T21:52:17.000Z | 2022-08-31T21:52:17 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/xlm-roberta-base-squad2
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/xlm-roberta-base-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906073 | autoevaluate | 2022-08-31T22:02:14Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T22:02:14Z | 2022-08-31T21:59:21.000Z | 2022-08-31T21:59:21 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepakvk/roberta-base-squad2-finetuned-squad
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepakvk/roberta-base-squad2-finetuned-squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | [
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0.0135471494868... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916077 | autoevaluate | 2022-08-31T22:04:31Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T22:04:31Z | 2022-08-31T22:01:55.000Z | 2022-08-31T22:01:55 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-medium-squad2-distilled
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-medium-squad2-distilled
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | [
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-0.4735691249370575,
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-0.059831097... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916078 | autoevaluate | 2022-08-31T22:10:07Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T22:10:07Z | 2022-08-31T22:05:09.000Z | 2022-08-31T22:05:09 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-large-uncased-whole-word-masking-squad2
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-large-uncased-whole-word-masking-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | [
-0.476833701133728,
-0.47604498267173767,
0.3154894709587097,
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-0.083964988589... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-38b250-14916080 | autoevaluate | 2022-08-31T22:13:11Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-08-31T22:13:11Z | 2022-08-31T22:06:43.000Z | 2022-08-31T22:06:43 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/deberta-v3-large-squad2
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/deberta-v3-large-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | [
-0.4581720530986786,
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Leli1024/Race-processed | Leli1024 | 2022-09-07T12:04:09Z | 13 | 0 | null | [
"region:us"
] | 2022-09-07T12:04:09Z | 2022-09-07T08:00:19.000Z | 2022-09-07T08:00:19 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nielsr/coco-panoptic-categories | nielsr | 2022-09-16T09:53:49Z | 13 | 0 | null | [
"region:us"
] | 2022-09-16T09:53:49Z | 2022-09-16T09:53:40.000Z | 2022-09-16T09:53:40 | Entry not found | [
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OxAISH-AL-LLM/pubmed_20k_rct | OxAISH-AL-LLM | 2022-09-21T19:40:11Z | 13 | 1 | null | [
"region:us"
] | 2022-09-21T19:40:11Z | 2022-09-16T15:23:52.000Z | 2022-09-16T15:23:52 | Entry not found | [
-0.32276472449302673,
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
zyznull/dureader-retrieval-corpus | zyznull | 2023-01-03T08:05:06Z | 13 | 3 | null | [
"license:apache-2.0",
"region:us"
] | 2023-01-03T08:05:06Z | 2022-09-28T08:03:03.000Z | 2022-09-28T08:03:03 | ---
license: apache-2.0
---
# dureader
数据来自DuReader-Retreval数据集，这里是[原始地址](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval)。
> 本数据集只用作学术研究使用。如果本仓库涉及侵权行为，会立即删除。
| [
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alexboresoff/trainv2 | alexboresoff | 2022-09-29T15:55:00Z | 13 | 0 | null | [
"region:us"
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morinfla/prova | morinfla | 2022-10-02T15:24:48Z | 13 | 0 | null | [
"region:us"
] | 2022-10-02T15:24:48Z | 2022-10-01T14:45:28.000Z | 2022-10-01T14:45:28 | Entry not found | [
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Mainred/model | Mainred | 2022-10-01T19:46:07Z | 13 | 0 | null | [
"license:unknown",
"region:us"
] | 2022-10-01T19:46:07Z | 2022-10-01T16:56:43.000Z | 2022-10-01T16:56:43 | ---
license: unknown
---
| [
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ricewind/logo-union | ricewind | 2022-10-02T11:13:10Z | 13 | 0 | null | [
"region:us"
] | 2022-10-02T11:13:10Z | 2022-10-02T10:59:07.000Z | 2022-10-02T10:59:07 | imagenes logo del real union tenerife
license: other
---
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Sanatbek/uzbek-kazakh-parallel-corpora | Sanatbek | 2023-08-02T22:27:43Z | 13 | 3 | null | [
"region:us"
] | 2023-08-02T22:27:43Z | 2022-10-02T18:43:18.000Z | 2022-10-02T18:43:18 | # To download:
- from datasets import load_dataset
- uz_dev = load_dataset("Sanatbek/uzbek-kazakh-parallel-corpora", split="train[:13373]") (*10%*)
- uz_test = load_dataset("Sanatbek/uzbek-kazakh-parallel-corpora", split="train[13374:40120]") (*20%*)
- uz_train = load_dataset("Sanatbek/uzbek-kazakh-parallel-corpora", split="train[40121:]") (*70%*) | [
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TCL98/images-rocio | TCL98 | 2022-10-02T23:03:48Z | 13 | 0 | null | [
"region:us"
] | 2022-10-02T23:03:48Z | 2022-10-02T22:49:04.000Z | 2022-10-02T22:49:04 | Entry not found | [
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Boryak/Images | Boryak | 2022-10-04T18:01:04Z | 13 | 0 | null | [
"license:openrail",
"region:us"
] | 2022-10-04T18:01:04Z | 2022-10-04T18:00:20.000Z | 2022-10-04T18:00:20 | ---
license: openrail
---
| [
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Stable12/karkilu | Stable12 | 2022-10-04T21:40:22Z | 13 | 0 | null | [
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] | 2022-10-04T21:40:22Z | 2022-10-04T21:34:54.000Z | 2022-10-04T21:34:54 | Entry not found | [
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arbml/RES | arbml | 2022-11-03T13:43:51Z | 13 | 0 | null | [
"region:us"
] | 2022-11-03T13:43:51Z | 2022-10-05T13:13:51.000Z | 2022-10-05T13:13:51 | Entry not found | [
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arbml/names_transliteration | arbml | 2022-11-03T14:13:07Z | 13 | 0 | null | [
"region:us"
] | 2022-11-03T14:13:07Z | 2022-10-05T22:10:59.000Z | 2022-10-05T22:10:59 | Entry not found | [
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arbml/AMCD | arbml | 2022-11-03T14:43:07Z | 13 | 0 | null | [
"region:us"
] | 2022-11-03T14:43:07Z | 2022-10-05T22:41:52.000Z | 2022-10-05T22:41:52 | Entry not found | [
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tomekkorbak/detoxify-pile-chunk3-3350000-3400000 | tomekkorbak | 2022-10-06T02:59:47Z | 13 | 0 | null | [
"region:us"
] | 2022-10-06T02:59:47Z | 2022-10-06T02:59:40.000Z | 2022-10-06T02:59:40 | Entry not found | [
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scikit-learn/Fish | scikit-learn | 2022-10-06T19:02:45Z | 13 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-10-06T19:02:45Z | 2022-10-06T18:52:45.000Z | 2022-10-06T18:52:45 | ---
license: cc-by-4.0
---
# Dataset Summary
Dataset recording various measurements of 7 different species of fish at a fish market. Predictive models can be used to predict weight, species, etc.
## Feature Descriptions
- Species - Species name of fish
- Weight - Weight of fish in grams
- Length1 - Vertical length in cm
- Length2 - Diagonal length in cm
- Length3 - Cross length in cm
- Height - Height in cm
- Width - Width in cm
## Acknowledgments
Dataset created by Aung Pyae, and found on [Kaggle](https://www.kaggle.com/datasets/aungpyaeap/fish-market) | [
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tomekkorbak/detoxify-pile-chunk3-5050000-5100000 | tomekkorbak | 2022-10-06T19:58:38Z | 13 | 0 | null | [
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] | 2022-10-06T19:58:38Z | 2022-10-06T19:58:27.000Z | 2022-10-06T19:58:27 | Entry not found | [
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frankier/multiscale_rotten_tomatoes_critic_reviews | frankier | 2022-11-04T12:09:34Z | 13 | 0 | null | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-scoring",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc0-1.0",
"reviews",
"ratings",
"ordinal",
"text",
"region:us"
] | 2022-11-04T12:09:34Z | 2022-10-07T12:54:12.000Z | 2022-10-07T12:54:12 | ---
language:
- en
language_creators:
- found
license: cc0-1.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
tags:
- reviews
- ratings
- ordinal
- text
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
---
Cleaned up version of the rotten tomatoes critic reviews dataset. The original
is obtained from Kaggle:
https://www.kaggle.com/datasets/stefanoleone992/rotten-tomatoes-movies-and-critic-reviews-dataset
Data has been scraped from the publicly available website
https://www.rottentomatoes.com as of 2020-10-31.
The clean up process drops anything without both a review and a rating, as well
as standardising the ratings onto several integer, ordinal scales.
Requires the `kaggle` library to be installed, and kaggle API keys passed
through environment variables or in ~/.kaggle/kaggle.json. See [the Kaggle
docs](https://www.kaggle.com/docs/api#authentication).
A processed version is available at
https://huggingface.co/datasets/frankier/processed_multiscale_rt_critics
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ywchoi/pmc_1_cleaned | ywchoi | 2022-10-07T17:19:45Z | 13 | 0 | null | [
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ywchoi/pmc_6_cleaned | ywchoi | 2022-10-07T19:40:09Z | 13 | 0 | null | [
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] | 2022-10-07T19:40:09Z | 2022-10-07T18:41:19.000Z | 2022-10-07T18:41:19 | Entry not found | [
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ywchoi/pmc_2_cleaned | ywchoi | 2022-10-07T20:40:08Z | 13 | 0 | null | [
"region:us"
] | 2022-10-07T20:40:08Z | 2022-10-07T20:27:10.000Z | 2022-10-07T20:27:10 | Entry not found | [
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ywchoi/pmc_3_cleaned | ywchoi | 2022-10-07T21:47:30Z | 13 | 0 | null | [
"region:us"
] | 2022-10-07T21:47:30Z | 2022-10-07T21:08:42.000Z | 2022-10-07T21:08:42 | Entry not found | [
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autoevaluate/autoeval-eval-inverse-scaling__quote-repetition-inverse-scaling__quot-3aff83-1695059590 | autoevaluate | 2022-10-08T12:54:39Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-08T12:54:39Z | 2022-10-08T12:53:45.000Z | 2022-10-08T12:53:45 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- inverse-scaling/quote-repetition
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-125m_eval
metrics: []
dataset_name: inverse-scaling/quote-repetition
dataset_config: inverse-scaling--quote-repetition
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# 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: inverse-scaling/opt-125m_eval
* Dataset: inverse-scaling/quote-repetition
* Config: inverse-scaling--quote-repetition
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model. | [
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autoevaluate/autoeval-eval-inverse-scaling__redefine-math-inverse-scaling__redefin-f7efd9-1695359602 | autoevaluate | 2022-10-08T13:27:39Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-08T13:27:39Z | 2022-10-08T13:02:42.000Z | 2022-10-08T13:02:42 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- inverse-scaling/redefine-math
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-6.7b_eval
metrics: []
dataset_name: inverse-scaling/redefine-math
dataset_config: inverse-scaling--redefine-math
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# 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: inverse-scaling/opt-6.7b_eval
* Dataset: inverse-scaling/redefine-math
* Config: inverse-scaling--redefine-math
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model. | [
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ywchoi/pmc_4_cleaned | ywchoi | 2022-10-08T20:00:50Z | 13 | 0 | null | [
"region:us"
] | 2022-10-08T20:00:50Z | 2022-10-08T19:11:48.000Z | 2022-10-08T19:11:48 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ywchoi/pmc_7_cleaned | ywchoi | 2022-10-09T02:09:17Z | 13 | 0 | null | [
"region:us"
] | 2022-10-09T02:09:17Z | 2022-10-09T00:58:52.000Z | 2022-10-09T00:58:52 | Entry not found | [
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0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mdalvi/stt_demo | mdalvi | 2022-10-10T17:29:41Z | 13 | 0 | null | [
"region:us"
] | 2022-10-10T17:29:41Z | 2022-10-10T17:29:37.000Z | 2022-10-10T17:29:37 | Entry not found | [
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-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kiddelpool/HarryPotter | kiddelpool | 2022-10-11T08:34:21Z | 13 | 0 | null | [
"license:openrail",
"region:us"
] | 2022-10-11T08:34:21Z | 2022-10-11T08:10:33.000Z | 2022-10-11T08:10:33 | ---
license: openrail
---
| [
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alkzar90/rock-glacier-dataset | alkzar90 | 2022-12-19T02:36:59Z | 13 | 2 | null | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:human-curator",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | 2022-12-19T02:36:59Z | 2022-10-11T17:23:58.000Z | 2022-10-11T17:23:58 | ---
annotations_creators:
- human-curator
language:
- en
license:
- mit
pretty_name: RockGlacier
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for Rock Glacier Detection
## 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:** [RockGlacier Homepage](https://github.com/alcazar90/rock-glacier-detection)
- **Repository:** [alcazar90/rock-glacier-detection](https://github.com/alcazar90/rock-glacier-detection)
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### Dataset Summary
Rock Glacier Detection dataset with satelital images of rock glaciers in the Chilean Andes.
### Supported Tasks and Leaderboards
- `image-classification`: Based on a satelitel images (from sentinel2), the goal of this task is to predict a rock glacier in the geographic area, if there any.
- `image-segmentation`: ...
### Languages
Spanish
## Dataset Structure
### Data Instances
A sample from the image-classification training set is provided below:
```
df = load_dataset("alkzar90/rock-glacier-dataset", name="image-classification")
df["train"][666]
> {'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=128x128 at 0x7FB2EC58C6D0>,
'labels': 0,
'path': 'train/cordillera/1512.png'
}
```
A sample from the image-segmentation training set is provided below:
```
df = load_dataset("alkzar90/rock-glacier-dataset", name="image-segmentation")
df["train"][666]
> {'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=128x128 at 0x7FB2EB7C1160>,
'masks': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=128x128 at 0x7FB2EC5A08E0>,
'path': 'train/cordillera/1512.png'}
```
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. 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]`.
- `labels`: an `int` classification label.
Class Label Mappings:
```json
{
"cordillera": 0
"glaciar": 1,
}
```
### Data Splits
| |train|validation| test|
|-------------|----:|---------:|-----:|
|# of examples|7875 |1125 |2700 |
## 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
```
@ONLINE {rock-glacier-dataset,
author="CMM - Glaciares (UChile)",
title="Rock Glacier Dataset",
month="October",
year="2022",
url="https://github.com/alcazar90/rock-glacier-detection"
}
```
### Contributions
Thanks to...
| [
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-0.0795155912637710... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
allenai/ms2_dense_mean | allenai | 2022-11-18T19:40:11Z | 13 | 0 | multi-document-summarization | [
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"lang... | 2022-11-18T19:40:11Z | 2022-10-12T14:06:02.000Z | 2022-10-12T14:06:02 | ---
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
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 `train`, `validation` and `test` splits have been replaced by a __dense__ 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__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) 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==17`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4764 | 0.2395 | 0.2271 | 0.2418 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4364 | 0.2125 | 0.2131 | 0.2074 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4481 | 0.2224 | 0.2254 | 0.2100 | | [
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-0.32725125551223755,
0.20025597512722015,
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-0.5212194919586182,
-0.8144605755805969,
0.089262671768... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
allenai/wcep_dense_oracle | allenai | 2022-11-06T21:49:24Z | 13 | 0 | wcep | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2022-11-06T21:49:24Z | 2022-10-12T14:09:02.000Z | 2022-10-12T14:09:02 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: WCEP-10
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: wcep
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 [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of the `train`, `validation`, and `test` splits have been replaced by a __dense__ 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__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) 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 `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8590 | 0.6490 | 0.6490 | 0.6490 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8578 | 0.6326 | 0.6326 | 0.6326 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8678 | 0.6631 | 0.6631 | 0.6631 | | [
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-0.6818073391914368,
-0.021683445200... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
allenai/wcep_dense_mean | allenai | 2022-11-18T20:00:21Z | 13 | 0 | wcep | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2022-11-18T20:00:21Z | 2022-10-12T14:33:21.000Z | 2022-10-12T14:33:21 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: WCEP-10
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: wcep
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 [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `train`, `validation, and `test` splits have been have been replaced by a __dense__ 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__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) 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==9`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8590 | 0.6490 | 0.6239 | 0.6271 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8578 | 0.6326 | 0.6301 | 0.6031 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8678 | 0.6631 | 0.6564 | 0.6338 | | [
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-0.7885121703147888,
0.04247928783297539... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ashraq/financial-news | ashraq | 2022-10-12T19:05:51Z | 13 | 5 | null | [
"region:us"
] | 2022-10-12T19:05:51Z | 2022-10-12T19:01:10.000Z | 2022-10-12T19:01:10 | The data was obtained from [here](https://www.kaggle.com/datasets/miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests?select=raw_partner_headlines.csv). | [
-0.10895396023988724,
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-0.062816858291625... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961034 | autoevaluate | 2022-10-13T15:56:46Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-13T15:56:46Z | 2022-10-13T15:48:43.000Z | 2022-10-13T15:48:43 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-3b
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: mismatch
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: bigscience/bloom-3b
* Dataset: phpthinh/examplei
* Config: mismatch
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model. | [
-0.2222314327955246,
-0.34619835019111633,
0.5210999846458435,
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Mrdanizm/Mestablediffusion | Mrdanizm | 2022-10-17T14:02:07Z | 13 | 0 | null | [
"license:other",
"region:us"
] | 2022-10-17T14:02:07Z | 2022-10-17T13:52:11.000Z | 2022-10-17T13:52:11 | ---
license: other
---
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noahgift/social-power-nba | noahgift | 2022-10-17T16:07:45Z | 13 | 0 | null | [
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2022-10-17T16:07:45Z | 2022-10-17T16:03:02.000Z | 2022-10-17T16:03:02 | ---
license: cc-by-nc-nd-4.0
---
A dataset that has NBA data as well as social media data including twitter and wikipedia
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autoevaluate/autoeval-eval-conceptual_captions-unlabeled-ccbde0-1800162251 | autoevaluate | 2022-10-18T23:14:21Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-18T23:14:21Z | 2022-10-18T09:04:43.000Z | 2022-10-18T09:04:43 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conceptual_captions
eval_info:
task: summarization
model: 0ys/mt5-small-finetuned-amazon-en-es
metrics: ['accuracy']
dataset_name: conceptual_captions
dataset_config: unlabeled
dataset_split: train
col_mapping:
text: image_url
target: caption
---
# 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: 0ys/mt5-small-finetuned-amazon-en-es
* Dataset: conceptual_captions
* Config: unlabeled
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@DonaldDaz](https://huggingface.co/DonaldDaz) for evaluating this model. | [
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jeffdshen/redefine_math0_8shot | jeffdshen | 2022-10-23T20:17:15Z | 13 | 0 | null | [
"license:cc-by-2.0",
"region:us"
] | 2022-10-23T20:17:15Z | 2022-10-23T20:16:12.000Z | 2022-10-23T20:16:12 | ---
license: cc-by-2.0
---
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rufimelo/PortugueseLegalSentences-v0 | rufimelo | 2022-10-24T00:55:55Z | 13 | 0 | null | [
"annotations_creators:no-annotation",
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"multilinguality:monolingual",
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"license:apache-2.0",
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] | 2022-10-24T00:55:55Z | 2022-10-23T21:27:33.000Z | 2022-10-23T21:27:33 | ---
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
- monolingual
source_datasets:
- original
---
# Portuguese Legal Sentences
Collection of Legal Sentences from the Portuguese Supreme Court of Justice
The goal of this dataset was to be used for MLM and TSDAE
### Contributions
[@rufimelo99](https://github.com/rufimelo99)
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"region:us"
] | 2022-10-25T19:44:10Z | 2022-10-25T19:35:59.000Z | 2022-10-25T19:35:59 | ---
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---
# Dataset Card for "code_search_data-pep8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot-mathema-acb860-1886064280 | autoevaluate | 2022-10-26T04:17:02Z | 13 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-26T04:17:02Z | 2022-10-26T04:12:25.000Z | 2022-10-26T04:12:25 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_cot
eval_info:
task: text_zero_shot_classification
model: facebook/opt-2.7b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_cot
dataset_config: mathemakitten--winobias_antistereotype_test_cot
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: facebook/opt-2.7b
* Dataset: mathemakitten/winobias_antistereotype_test_cot
* Config: mathemakitten--winobias_antistereotype_test_cot
* 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. | [
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adamlouly/enron_spam_data | adamlouly | 2022-10-27T23:11:14Z | 13 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-10-27T23:11:14Z | 2022-10-27T21:54:56.000Z | 2022-10-27T21:54:56 | ---
license: apache-2.0
---
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bonadossou/afrolm_active_learning_dataset | bonadossou | 2023-03-29T18:10:21Z | 13 | 2 | null | [
"task_categories:fill-mask",
"task_ids:masked-language-modeling",
"annotations_creators:crowdsourced",
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"language:amh",
"language:orm",
"language:lin",
"language:hau",
"langu... | 2023-03-29T18:10:21Z | 2022-10-28T11:07:51.000Z | 2022-10-28T11:07:51 | ---
annotations_creators:
- crowdsourced
language:
- amh
- orm
- lin
- hau
- ibo
- kin
- lug
- luo
- pcm
- swa
- wol
- yor
- bam
- bbj
- ewe
- fon
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- sna
- tsn
- twi
- xho
- zul
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: afrolm-dataset
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- afrolm
- active learning
- language modeling
- research papers
- natural language processing
- self-active learning
task_categories:
- fill-mask
task_ids:
- masked-language-modeling
---
# AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
- [GitHub Repository of the Paper](https://github.com/bonaventuredossou/MLM_AL)
This repository contains the dataset for our paper [`AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages`](https://arxiv.org/pdf/2211.03263.pdf) which will appear at the third Simple and Efficient Natural Language Processing, at EMNLP 2022.
## Our self-active learning framework
## Languages Covered
AfroLM has been pretrained from scratch on 23 African Languages: Amharic, Afan Oromo, Bambara, Ghomalá, Éwé, Fon, Hausa, Ìgbò, Kinyarwanda, Lingala, Luganda, Luo, Mooré, Chewa, Naija, Shona, Swahili, Setswana, Twi, Wolof, Xhosa, Yorùbá, and Zulu.
## Evaluation Results
AfroLM was evaluated on MasakhaNER1.0 (10 African Languages) and MasakhaNER2.0 (21 African Languages) datasets; on text classification and sentiment analysis. AfroLM outperformed AfriBERTa, mBERT, and XLMR-base, and was very competitive with AfroXLMR. AfroLM is also very data efficient because it was pretrained on a dataset 14x+ smaller than its competitors' datasets. Below the average F1-score performances of various models, across various datasets. Please consult our paper for more language-level performance.
Model | MasakhaNER | MasakhaNER2.0* | Text Classification (Yoruba/Hausa) | Sentiment Analysis (YOSM) | OOD Sentiment Analysis (Twitter -> YOSM) |
|:---: |:---: |:---: | :---: |:---: | :---: |
`AfroLM-Large` | **80.13** | **83.26** | **82.90/91.00** | **85.40** | **68.70** |
`AfriBERTa` | 79.10 | 81.31 | 83.22/90.86 | 82.70 | 65.90 |
`mBERT` | 71.55 | 80.68 | --- | --- | --- |
`XLMR-base` | 79.16 | 83.09 | --- | --- | --- |
`AfroXLMR-base` | `81.90` | `84.55` | --- | --- | --- |
- (*) The evaluation was made on the 11 additional languages of the dataset.
- Bold numbers represent the performance of the model with the **smallest pretrained data**.
## Pretrained Models and Dataset
**Models:**: [AfroLM-Large](https://huggingface.co/bonadossou/afrolm_active_learning) and **Dataset**: [AfroLM Dataset](https://huggingface.co/datasets/bonadossou/afrolm_active_learning_dataset)
## HuggingFace usage of AfroLM-large
```python
from transformers import XLMRobertaModel, XLMRobertaTokenizer
model = XLMRobertaModel.from_pretrained("bonadossou/afrolm_active_learning")
tokenizer = XLMRobertaTokenizer.from_pretrained("bonadossou/afrolm_active_learning")
tokenizer.model_max_length = 256
```
`Autotokenizer` class does not successfully load our tokenizer. So we recommend using directly the `XLMRobertaTokenizer` class. Depending on your task, you will load the according mode of the model. Read the [XLMRoberta Documentation](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)
## Reproducing our result: Training and Evaluation
- To train the network, run `python active_learning.py`. You can also wrap it around a `bash` script.
- For the evaluation:
- NER Classification: `bash ner_experiments.sh`
- Text Classification & Sentiment Analysis: `bash text_classification_all.sh`
## Citation
``@inproceedings{dossou-etal-2022-afrolm,
title = "{A}fro{LM}: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 {A}frican Languages",
author = "Dossou, Bonaventure F. P. and
Tonja, Atnafu Lambebo and
Yousuf, Oreen and
Osei, Salomey and
Oppong, Abigail and
Shode, Iyanuoluwa and
Awoyomi, Oluwabusayo Olufunke and
Emezue, Chris",
booktitle = "Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sustainlp-1.11",
pages = "52--64",}``
## Reach out
Do you have a question? Please create an issue and we will reach out as soon as possible | [
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rufimelo/PortugueseLegalSentences-v3 | rufimelo | 2022-11-01T13:15:47Z | 13 | 3 | null | [
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annotations_creators:
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license:
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multilinguality:
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source_datasets:
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---
# Portuguese Legal Sentences
Collection of Legal Sentences from the Portuguese Supreme Court of Justice
The goal of this dataset was to be used for MLM and TSDAE
Extended version of rufimelo/PortugueseLegalSentences-v1
400000/50000/50000
### Contributions
[@rufimelo99](https://github.com/rufimelo99)
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ghomasHudson/muld_OpenSubtitles | ghomasHudson | 2022-11-02T11:56:13Z | 13 | 0 | null | [
"region:us"
] | 2022-11-02T11:56:13Z | 2022-11-02T11:55:18.000Z | 2022-11-02T11:55:18 | ---
dataset_info:
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download_size: 967763941
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---
# Dataset Card for "muld_OpenSubtitles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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lmqg/qag_tweetqa | lmqg | 2022-12-02T19:16:46Z | 13 | 0 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
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"license:cc-by-sa-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2022-12-02T19:16:46Z | 2022-11-11T11:11:25.000Z | 2022-11-11T11:11:25 | ---
license: cc-by-sa-4.0
pretty_name: TweetQA for question generation
language: en
multilinguality: monolingual
size_categories: 1k<n<10K
source_datasets: tweet_qa
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qag_tweetqa"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the [tweet_qa](https://huggingface.co/datasets/tweet_qa). The test set of the original data is not publicly released, so we randomly sampled test questions from the training set.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
English (en)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"paragraph": "I would hope that Phylicia Rashad would apologize now that @missjillscott has! You cannot discount 30 victims who come with similar stories.— JDWhitner (@JDWhitner) July 7, 2015",
"questions": [ "what should phylicia rashad do now?", "how many victims have come forward?" ],
"answers": [ "apologize", "30" ],
"questions_answers": "Q: what should phylicia rashad do now?, A: apologize Q: how many victims have come forward?, A: 30"
}
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|4536 | 583| 583|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
``` | [
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0.1592778265... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vegeta/tokenedlegal | vegeta | 2022-11-12T23:42:28Z | 13 | 0 | null | [
"region:us"
] | 2022-11-12T23:42:28Z | 2022-11-12T22:58:08.000Z | 2022-11-12T22:58:08 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 29279261498
num_examples: 218374246
- name: validation
num_bytes: 3195898734
num_examples: 23880923
download_size: 8182611602
dataset_size: 32475160232
---
# Dataset Card for "tokenedlegal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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-0.6784900426864624,
-0.388333350419... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/bio_sim_verb | bigbio | 2022-12-22T15:43:25Z | 13 | 1 | null | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | 2022-12-22T15:43:25Z | 2022-11-13T22:06:20.000Z | 2022-11-13T22:06:20 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: Bio-SimVerb
homepage: https://github.com/cambridgeltl/bio-simverb
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- SEMANTIC_SIMILARITY
---
# Dataset Card for Bio-SimVerb
## Dataset Description
- **Homepage:** https://github.com/cambridgeltl/bio-simverb
- **Pubmed:** True
- **Public:** True
- **Tasks:** STS
This repository contains the evaluation datasets for the paper Bio-SimVerb and Bio-SimLex: Wide-coverage Evaluation Sets of Word Similarity in Biomedicine by Billy Chiu, Sampo Pyysalo and Anna Korhonen.
## Citation Information
```
@article{article,
title = {
Bio-SimVerb and Bio-SimLex: Wide-coverage evaluation sets of word
similarity in biomedicine
},
author = {Chiu, Billy and Pyysalo, Sampo and Vulić, Ivan and Korhonen, Anna},
year = 2018,
month = {02},
journal = {BMC Bioinformatics},
volume = 19,
pages = {},
doi = {10.1186/s12859-018-2039-z}
}
```
| [
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0.4109465181827... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/medal | bigbio | 2022-12-22T15:45:07Z | 13 | 0 | null | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | 2022-12-22T15:45:07Z | 2022-11-13T22:09:21.000Z | 2022-11-13T22:09:21 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: NLM_LICENSE
pretty_name: MeDAL
homepage: https://github.com/BruceWen120/medal
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for MeDAL
## Dataset Description
- **Homepage:** https://github.com/BruceWen120/medal
- **Pubmed:** True
- **Public:** True
- **Tasks:** NED
The Repository for Medical Dataset for Abbreviation Disambiguation for Natural Language Understanding (MeDAL) is
a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding
pre-training in the medical domain.
## Citation Information
```
@inproceedings{,
title = {MeDAL\: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining},
author = {Wen, Zhi and Lu, Xing Han and Reddy, Siva},
booktitle = {Proceedings of the 3rd Clinical Natural Language Processing Workshop},
month = {Nov},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/2020.clinicalnlp-1.15},
pages = {130--135},
}
```
| [
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0.550040602684... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/msh_wsd | bigbio | 2022-12-22T15:45:41Z | 13 | 1 | null | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | 2022-12-22T15:45:41Z | 2022-11-13T22:10:11.000Z | 2022-11-13T22:10:11 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: UMLS_LICENSE
pretty_name: MSH WSD
homepage: https://lhncbc.nlm.nih.gov/ii/areas/WSD/collaboration.html
bigbio_pubmed: True
bigbio_public: False
bigbio_tasks:
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for MSH WSD
## Dataset Description
- **Homepage:** https://lhncbc.nlm.nih.gov/ii/areas/WSD/collaboration.html
- **Pubmed:** True
- **Public:** False
- **Tasks:** NED
Evaluation of Word Sense Disambiguation methods (WSD) in the biomedical domain is difficult because the available
resources are either too small or too focused on specific types of entities (e.g. diseases or genes). We have
developed a method that can be used to automatically develop a WSD test collection using the Unified Medical Language
System (UMLS) Metathesaurus and the manual MeSH indexing of MEDLINE. The resulting dataset is called MSH WSD and
consists of 106 ambiguous abbreviations, 88 ambiguous terms and 9 which are a combination of both, for a total of 203
ambiguous words. Each instance containing the ambiguous word was assigned a CUI from the 2009AB version of the UMLS.
For each ambiguous term/abbreviation, the data set contains a maximum of 100 instances per sense obtained from
MEDLINE; totaling 37,888 ambiguity cases in 37,090 MEDLINE citations.
## Citation Information
```
@article{jimeno2011exploiting,
title={Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation},
author={Jimeno-Yepes, Antonio J and McInnes, Bridget T and Aronson, Alan R},
journal={BMC bioinformatics},
volume={12},
number={1},
pages={1--14},
year={2011},
publisher={BioMed Central}
}
```
| [
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0.5594680905342102,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stjiris/portuguese-legal-sentences-v0 | stjiris | 2023-01-08T14:23:33Z | 13 | 5 | null | [
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:pt",
"license:apache-2.0",
"region:us"
] | 2023-01-08T14:23:33Z | 2022-11-14T21:28:26.000Z | 2022-11-14T21:28:26 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- pt
license:
- apache-2.0
multilinguality:
- monolingual
source_datasets:
- original
---
Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/).
Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/)
# Portuguese Legal Sentences
Collection of Legal Sentences from the Portuguese Supreme Court of Justice
The goal of this dataset was to be used for MLM and TSDAE
### Contributions
[@rufimelo99](https://github.com/rufimelo99)
If you use this work, please cite:
```bibtex
@inproceedings{MeloSemantic,
author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o},
title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a},
}
```
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0.515185534954071,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
israfelsr/mm_tiny_imagenet | israfelsr | 2022-12-16T11:19:54Z | 13 | 1 | null | [
"region:us"
] | 2022-12-16T11:19:54Z | 2022-11-17T12:44:50.000Z | 2022-11-17T12:44:50 | ---
dataset_info:
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dtype: image
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download_size: 149059401
dataset_size: 199983661.0
---
# Dataset Card for "mm_tiny_imagenet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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