author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1 class | downloads float64 1 1M ⌀ | gated bool 2 classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2 classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sled-umich | null | @misc{storks2021tiered,
title={Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding},
author={Shane Storks and Qiaozi Gao and Yichi Zhang and Joyce Chai},
year={2021},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021},
location={Punta Cana, Dominican Republic},
publisher={Association for Computational Linguistics},
} | We introduce Tiered Reasoning for Intuitive Physics (TRIP), a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process. | false | 283 | false | sled-umich/TRIP | 2022-10-14T19:17:29.000Z | null | false | e8034abd1a23f948dc6bc68e1bceaa47d7e966c2 | [] | [
"annotations_creators:expert-generated",
"language:en",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:natural-language-inference"
] | https://huggingface.co/datasets/sled-umich/TRIP/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
pretty_name: 'TRIP: Tiered Reasoning for Intuitive Physics'
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# [TRIP - Tiered Reasoning for Intuitive Physics](https://aclanthology.org/2021.findings-emnlp.422/)
Official dataset for [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://aclanthology.org/2021.findings-emnlp.422/). Shane Storks, Qiaozi Gao, Yichi Zhang, Joyce Chai. EMNLP Findings, 2021.
For our official model and experiment code, please check [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU).
## Overview
We introduce Tiered Reasoning for Intuitive Physics (TRIP), a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process.
It includes dense annotations for each story capturing multiple tiers of reasoning beyond the end task. From these annotations, we propose a tiered evaluation, where given a pair of highly similar stories (differing only by one sentence which makes one of the stories implausible), systems must jointly identify (1) the plausible story, (2) a pair of conflicting sentences in the implausible story, and (3) the underlying physical states in those sentences causing the conflict. The goal of TRIP is to enable a systematic evaluation of machine coherence toward the end task prediction of plausibility. In particular, we evaluate whether a high-level plausibility prediction can be verified based on lower-level understanding, for example, physical state changes that would support the prediction.
## Download
```python
from datasets import load_dataset
dataset = load_dataset("sled-umich/TRIP")
```
* [HuggingFace-Dataset](https://huggingface.co/datasets/sled-umich/TRIP)
* [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU)
## Cite
```bibtex
@misc{storks2021tiered,
title={Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding},
author={Shane Storks and Qiaozi Gao and Yichi Zhang and Joyce Chai},
year={2021},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021},
location={Punta Cana, Dominican Republic},
publisher={Association for Computational Linguistics},
}
```
|
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-b079e4-1737160612 | 2022-10-12T19:01:30.000Z | null | false | 6d3d5c6d6497f657f192f3c977b08d036ea51384 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:adversarial_qa"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-b079e4-1737160612/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: Adrian/distilbert-base-uncased-finetuned-squad-colab
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: Adrian/distilbert-base-uncased-finetuned-squad-colab
* 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 [@saad](https://huggingface.co/saad) for evaluating this model. |
ashraq | null | null | null | false | 3 | false | ashraq/financial-news | 2022-10-12T19:05:51.000Z | null | false | 7fedd76a179cb2b6e1230a0d095c5a290ed4c2f0 | [] | [] | https://huggingface.co/datasets/ashraq/financial-news/resolve/main/README.md | The data was obtained from [here](https://www.kaggle.com/datasets/miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests?select=raw_partner_headlines.csv). |
allenai | null | null | null | false | 1 | false | allenai/multinews_dense_max | 2022-11-11T01:29:44.000Z | multi-news | false | 907311f023524778117adba50143bbc6eab91d51 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/allenai/multinews_dense_max/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: multi-news
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __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==10`
Retrieval results on the `train` set:
Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8661 | 0.6867 | 0.2118 | 0.7966 |
Retrieval results on the `validation` set:
Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8626 | 0.6859 | 0.2083 | 0.7949 |
Retrieval results on the `test` set:
Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8625 | 0.6927 | 0.2096 | 0.7971 | |
allenai | null | null | null | false | 2 | false | allenai/multinews_dense_mean | 2022-11-11T01:32:35.000Z | multi-news | false | f3e05a3d4e6d0c9f71a35502c911f21c96754a57 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/allenai/multinews_dense_mean/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: multi-news
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __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==3`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8661 | 0.6867 | 0.5936 | 0.6917 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8626 | 0.6859 | 0.5874 | 0.6925 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8625 | 0.6927 | 0.5938 | 0.6993 | |
allenai | null | null | null | false | 1 | false | allenai/multinews_dense_oracle | 2022-11-12T04:10:53.000Z | multi-news | false | 0a28a9ad21550cfaadec888b0d826eff2c5bf028 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization",
"task_ids:news-articles-summarization"
] | https://huggingface.co/datasets/allenai/multinews_dense_oracle/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: multi-news
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of 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 |
| ----------- | ----------- | ----------- | ----------- |
| 0.8661 | 0.6867 | 0.6867 | 0.6867 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8626 | 0.6859 | 0.6859 | 0.6859 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8625 | 0.6927 | 0.6927 | 0.6927 | |
AshleyRoni | null | null | null | false | null | false | AshleyRoni/lizzabliss2001 | 2022-10-12T19:34:59.000Z | null | false | 1a858281821ccffa0cf9d900727a7a9c8cbe68c9 | [] | [
"license:openrail"
] | https://huggingface.co/datasets/AshleyRoni/lizzabliss2001/resolve/main/README.md | ---
license: openrail
---
|
debosneed | null | null | null | false | null | false | debosneed/manuscript-captions | 2022-10-12T19:36:55.000Z | null | false | 5eb85b0cbb0259b14d92c550e4af42ea8815e20c | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/debosneed/manuscript-captions/resolve/main/README.md | ---
license: afl-3.0
---
|
sled-umich | null | @inproceedings{gao-etal-2018-action,
title = "What Action Causes This? Towards Naive Physical Action-Effect Prediction",
author = "Gao, Qiaozi and
Yang, Shaohua and
Chai, Joyce and
Vanderwende, Lucy",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1086",
doi = "10.18653/v1/P18-1086",
pages = "934--945",
} | Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples. | false | 22 | false | sled-umich/Action-Effect | 2022-10-14T19:12:20.000Z | null | false | 5e34c0587551f404f5a77198d74e06e6859bd75b | [] | [
"annotations_creators:crowdsourced",
"language:eng",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:image-classification",
"task_categories:image-to-text"
] | https://huggingface.co/datasets/sled-umich/Action-Effect/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- eng
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
pretty_name: Action-Effect-Prediction
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- image-classification
- image-to-text
task_ids: []
---
# Physical-Action-Effect-Prediction
Official dataset for ["What Action Causes This? Towards Naive Physical Action-Effect Prediction"](https://aclanthology.org/P18-1086/), ACL 2018.
## Overview
Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.
### Datasets
- This dataset contains action-effect information for 140 verb-noun pairs. It has two parts: effects described by natural language, and effects depicted in images.
- The language data contains verb-noun pairs and their effects described in natural language. For each verb-noun pair, its possible effects are described by 10 different annotators. The format for each line is `verb noun, effect_sentence, [effect_phrase_1, effect_phrase_2, effect_phrase_3, ...]`. Effect_phrases were automatically extracted from their corresponding effect_sentences.
- The image data contains images depicting action effects. For each verb-noun pair, an average of 15 positive images and 15 negative images were collected. Positive images are those deemed to capture the resulting world state of the action. And negative images are those deemed to capture some state of the related object (*i.e.*, the nouns in the verb-noun pairs), but are not the resulting state of the corresponding action.
### Download
```python
from datasets import load_dataset
dataset = load_dataset("sled-umich/Action-Effect")
```
* [HuggingFace](https://huggingface.co/datasets/sled-umich/Action-Effect)
* [Google Drive](https://drive.google.com/drive/folders/1P1_xWdCUoA9bHGlyfiimYAWy605tdXlN?usp=sharing)
* Dropbox:
* [Language Data](https://www.dropbox.com/s/pi1ckzjipbqxyrw/action_effect_sentence_phrase.txt?dl=0)
* [Image Data](https://www.dropbox.com/s/ilmfrqzqcbdf22k/action_effect_image_rs.tar.gz?dl=0)
### Cite
[What Action Causes This? Towards Naïve Physical Action-Effect Prediction](https://sled.eecs.umich.edu/publication/dblp-confacl-vanderwende-cyg-18/). *Qiaozi Gao, Shaohua Yang, Joyce Chai, Lucy Vanderwende*. ACL, 2018. [[Paper]](https://aclanthology.org/P18-1086/) [[Slides]](https://aclanthology.org/attachments/P18-1086.Presentation.pdf)
```tex
@inproceedings{gao-etal-2018-action,
title = "What Action Causes This? Towards Naive Physical Action-Effect Prediction",
author = "Gao, Qiaozi and
Yang, Shaohua and
Chai, Joyce and
Vanderwende, Lucy",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1086",
doi = "10.18653/v1/P18-1086",
pages = "934--945",
abstract = "Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.",
}
```
|
DavidBatista | null | null | null | false | null | false | DavidBatista/ImageFolder | 2022-10-12T21:34:08.000Z | null | false | 261c1d1340f1ae506e0bac1cccf79bddade05b57 | [] | [
"license:artistic-2.0"
] | https://huggingface.co/datasets/DavidBatista/ImageFolder/resolve/main/README.md | ---
license: artistic-2.0
---
|
zhengxuanzenwu | null | null | null | false | 58 | false | zhengxuanzenwu/wikitext-2-split-128 | 2022-10-13T00:11:29.000Z | null | false | 817f68fefc4d740360dded88d91f53089f21c10d | [] | [] | https://huggingface.co/datasets/zhengxuanzenwu/wikitext-2-split-128/resolve/main/README.md | This is a dataset created from the WikiText-2 dataset by splitting longer sequences into sequences with maximum of 128 tokens after using a wordpiece tokenizer. |
maxwellfoley | null | null | null | false | 102 | false | maxwellfoley/corporate-surrealist-training | 2022-11-15T05:09:29.000Z | null | false | a67f1cabf204e8784e28195ca3badfcff9e8c3ae | [] | [] | https://huggingface.co/datasets/maxwellfoley/corporate-surrealist-training/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 226883173.0
num_examples: 507
download_size: 221334520
dataset_size: 226883173.0
---
# Dataset Card for "corporate-surrealist-training"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Gh0st | null | null | null | false | null | false | Gh0st/jiutian | 2022-11-11T09:52:39.000Z | null | false | 15f2a9871817cf2b5037445da14f34c3dde160de | [] | [
"license:unknown"
] | https://huggingface.co/datasets/Gh0st/jiutian/resolve/main/README.md | ---
license: unknown
---
|
fcabanilla | null | null | null | false | 1 | false | fcabanilla/Tobby | 2022-10-13T06:27:40.000Z | null | false | 12cf3a624e13fdd16a70dfb7d493fd55e561d650 | [] | [
"license:mit"
] | https://huggingface.co/datasets/fcabanilla/Tobby/resolve/main/README.md | ---
license: mit
---
|
fcabanilla | null | null | null | false | null | false | fcabanilla/tobby2 | 2022-10-13T07:11:17.000Z | null | false | 243851be0bc1f8846618ad4fe8c6432347191460 | [] | [
"license:mit"
] | https://huggingface.co/datasets/fcabanilla/tobby2/resolve/main/README.md | ---
license: mit
---
|
nikitam | null | null | null | false | 11 | false | nikitam/ACES | 2022-10-28T07:53:15.000Z | null | false | 079178b9e95576a70b67d1ed917216be8467569e | [] | [
"arxiv:2210.15615",
"language:multilingual",
"license:cc-by-nc-sa-4.0",
"multilinguality:multilingual",
"source_datasets:FLORES-101, FLORES-200, PAWS-X, XNLI, XTREME, WinoMT, Wino-X, MuCOW, EuroParl ConDisco, ParcorFull",
"task_categories:translation"
] | https://huggingface.co/datasets/nikitam/ACES/resolve/main/README.md | ---
language:
- multilingual
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
source_datasets:
- FLORES-101, FLORES-200, PAWS-X, XNLI, XTREME, WinoMT, Wino-X, MuCOW, EuroParl ConDisco, ParcorFull
task_categories:
- translation
pretty_name: ACES
---
# Dataset Card for ACES
## 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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Usage](#usage)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact](#contact)
## Dataset Description
- **Repository:** [ACES dataset repository](https://github.com/EdinburghNLP/ACES)
- **Paper:** [arXiv](https://arxiv.org/abs/2210.15615)
### Dataset Summary
ACES consists of 36,476 examples covering 146 language pairs and representing challenges from 68 phenomena for evaluating machine translation metrics. We focus on translation accuracy errors and base the phenomena covered in our challenge set on the Multidimensional Quality Metrics (MQM) ontology. The phenomena range from simple perturbations at the word/character level to more complex errors based on discourse and real-world knowledge.
### Supported Tasks and Leaderboards
-Machine translation evaluation of metrics
-Potentially useful for contrastive machine translation evaluation
### Languages
The dataset covers 146 language pairs as follows:
af-en, af-fa, ar-en, ar-fr, ar-hi, be-en, bg-en, bg-lt, ca-en, ca-es, cs-en, da-en, de-en, de-es, de-fr, de-ja, de-ko, de-ru, de-zh, el-en, en-af, en-ar, en-be, en-bg, en-ca, en-cs, en-da, en-de, en-el, en-es, en-et, en-fa, en-fi, en-fr, en-gl, en-he, en-hi, en-hr, en-hu, en-hy, en-id, en-it, en-ja, en-ko, en-lt, en-lv, en-mr, en-nl, en-no, en-pl, en-pt, en-ro, en-ru, en-sk, en-sl, en-sr, en-sv, en-ta, en-tr, en-uk, en-ur, en-vi, en-zh, es-ca, es-de, es-en, es-fr, es-ja, es-ko, es-zh, et-en, fa-af, fa-en, fi-en, fr-de, fr-en, fr-es, fr-ja, fr-ko, fr-mr, fr-ru, fr-zh, ga-en, gl-en, he-en, he-sv, hi-ar, hi-en, hr-en, hr-lv, hu-en, hy-en, hy-vi, id-en, it-en, ja-de, ja-en, ja-es, ja-fr, ja-ko, ja-zh, ko-de, ko-en, ko-es, ko-fr, ko-ja, ko-zh, lt-bg, lt-en, lv-en, lv-hr, mr-en, nl-en, no-en, pl-en, pl-mr, pl-sk, pt-en, pt-sr, ro-en, ru-de, ru-en, ru-es, ru-fr, sk-en, sk-pl, sl-en, sr-en, sr-pt, sv-en, sv-he, sw-en, ta-en, th-en, tr-en, uk-en, ur-en, vi-en, vi-hy, wo-en, zh-de, zh-en, zh-es, zh-fr, zh-ja, zh-ko
## Dataset Structure
### Data Instances
Each data instance contains the following features: _source_, _good-translation_, _incorrect-translation_, _reference_, _phenomena_, _langpair_
See the [ACES corpus viewer](https://huggingface.co/datasets/nikitam/ACES/viewer/nikitam--ACES/train) to explore more examples.
An example from the ACES challenge set looks like the following:
```
{'source': "Proper nutritional practices alone cannot generate elite performances, but they can significantly affect athletes' overall wellness.", 'good-translation': 'Las prácticas nutricionales adecuadas por sí solas no pueden generar rendimiento de élite, pero pueden afectar significativamente el bienestar general de los atletas.', 'incorrect-translation': 'Las prácticas nutricionales adecuadas por sí solas no pueden generar rendimiento de élite, pero pueden afectar significativamente el bienestar general de los jóvenes atletas.', 'reference': 'No es posible que las prácticas nutricionales adecuadas, por sí solas, generen un rendimiento de elite, pero puede influir en gran medida el bienestar general de los atletas .', 'phenomena': 'addition', 'langpair': 'en-es'}
```
### Data Fields
- 'source': a string containing the text that needs to be translated
- 'good-translation': possible translation of the source sentence
- 'incorrect-translation': translation of the source sentence that contains an error or phenomenon of interest
- 'reference': the gold standard translation
- 'phenomena': the type of error or phenomena being studied in the example
- 'langpair': the source language and the target language pair of the example
Note that the _good-translation_ may not be free of errors but it is a better translation than the _incorrect-translation_
### Data Splits
The ACES dataset has 1 split: _train_ which contains the challenge set. There are 36476 examples.
## Dataset Creation
### Curation Rationale
With the advent of neural networks and especially Transformer-based architectures, machine translation outputs have become more and more fluent. Fluency errors are also judged less severely than accuracy errors by human evaluators \citep{freitag-etal-2021-experts} which reflects the fact that accuracy errors can have dangerous consequences in certain contexts, for example in the medical and legal domains. For these reasons, we decided to build a challenge set focused on accuracy errors.
Another aspect we focus on is including a broad range of language pairs in ACES. Whenever possible we create examples for all language pairs covered in a source dataset when we use automatic approaches. For phenomena where we create examples manually, we also aim to cover at least two language pairs per phenomenon but are of course limited to the languages spoken by the authors.
We aim to offer a collection of challenge sets covering both easy and hard phenomena. While it may be of interest to the community to continuously test on harder examples to check where machine translation evaluation metrics still break, we believe that easy challenge sets are just as important to ensure that metrics do not suddenly become worse at identifying error types that were previously considered ``solved''. Therefore, we take a holistic view when creating ACES and do not filter out individual examples or exclude challenge sets based on baseline metric performance or other factors.
### Source Data
#### Initial Data Collection and Normalization
Please see Sections 4 and 5 of the paper.
#### Who are the source language producers?
The dataset contains sentences found in FLORES-101, FLORES-200, PAWS-X, XNLI, XTREME, WinoMT, Wino-X, MuCOW, EuroParl ConDisco, ParcorFull datasets. Please refer to the respective papers for further details.
### Personal and Sensitive Information
The external datasets may contain sensitive information. Refer to the respective datasets for further details.
## Considerations for Using the Data
### Usage
ACES has been primarily designed to evaluate machine translation metrics on the accuracy errors. We expect the metric to score _good-translation_ consistently higher than _incorrect-translation_. We report the performance of metric based on Kendall-tau like correlation. It measures the number of times a metric scores the good translation above the incorrect translation (concordant) and equal to or lower than the incorrect translation (discordant).
### Discussion of Biases
Some examples within the challenge set exhibit biases, however, this is necessary in order to expose the limitations of existing metrics.
### Other Known Limitations
The ACES challenge set exhibits a number of biases. Firstly, there is greater coverage in terms of phenomena and the number of examples for the en-de and en-fr language pairs. This is in part due to the manual effort required to construct examples for some phenomena, in particular, those belonging to the discourse-level and real-world knowledge categories. Further, our choice of language pairs is also limited to the ones available in XLM-R. Secondly, ACES contains more examples for those phenomena for which examples could be generated automatically, compared to those that required manual construction/filtering. Thirdly, some of the automatically generated examples require external libraries which are only available for a few languages (e.g. Multilingual Wordnet). Fourthly, the focus of the challenge set is on accuracy errors. We leave the development of challenge sets for fluency errors to future work.
As a result of using existing datasets as the basis for many of the examples, errors present in these datasets may be propagated through into ACES. Whilst we acknowledge that this is undesirable, in our methods for constructing the incorrect translation we aim to ensure that the quality of the incorrect translation is always worse than the corresponding good translation.
The results and analyses presented in the paper exclude those metrics submitted to the WMT 2022 metrics shared task that provides only system-level outputs. We focus on metrics that provide segment-level outputs as this enables us to provide a broad overview of metric performance on different phenomenon categories and to conduct fine-grained analyses of performance on individual phenomena. For some of the fine-grained analyses, we apply additional constraints based on the language pairs covered by the metrics, or whether the metrics take the source as input, to address specific questions of interest. As a result of applying some of these additional constraints, our investigations tend to focus more on high and medium-resource languages than on low-resource languages. We hope to address this shortcoming in future work.
## Additional Information
### Licensing Information
The ACES dataset is Creative Commons Attribution Non-Commercial Share Alike 4.0 (cc-by-nc-sa-4.0)
### Citation Information
@inproceedings{amrhein-aces-2022,
title = "{ACES}: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics",
author = {Amrhein, Chantal and
Moghe, Nikita and
Guillou, Liane},
booktitle = "Seventh Conference on Machine Translation (WMT22)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
eprint = {2210.15615}
}
### Contact
[Chantal Amrhein](mailto:amrhein@cl.uzh.ch) and [Nikita Moghe](mailto:nikita.moghe@ed.ac.uk) and [Liane Guillou](mailto:lguillou@ed.ac.uk)
Dataset card based on [Allociné](https://huggingface.co/datasets/allocine) |
bigscience | null | null | null | false | 6 | false | bigscience/massive-probing-results | 2022-10-13T10:09:39.000Z | null | false | 161737dcf3c793ba3ac8d2271af518bc0195e329 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/bigscience/massive-probing-results/resolve/main/README.md | ---
license: apache-2.0
---
|
jianghuzhenyu | null | null | null | false | 2 | false | jianghuzhenyu/Atari_floringogianu | 2022-10-23T04:12:58.000Z | null | false | 94e5f3febd81cc62e9625b3e87d19ebda274282e | [] | [
"license:unknown"
] | https://huggingface.co/datasets/jianghuzhenyu/Atari_floringogianu/resolve/main/README.md | ---
license: unknown
---
|
dpasch01 | null | null | null | false | 3 | false | dpasch01/leaflet_offers | 2022-10-14T11:55:55.000Z | null | false | ab7264f30a130ff95a993abbd608f1abcd3e1c56 | [] | [] | https://huggingface.co/datasets/dpasch01/leaflet_offers/resolve/main/README.md | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 5644570.0
num_examples: 4
download_size: 0
dataset_size: 5644570.0
---
# Dataset Card for "leaflet_offers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
krm | null | null | null | false | 1 | false | krm/for-ULPGL-Dissertation | 2022-10-16T07:53:00.000Z | null | false | 8ef4028d8faf9906c3efe6573cc99e3c474834d2 | [] | [
"annotations_creators:other",
"language:fr",
"language_creators:other",
"license:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|orange_sum",
"tags:krm",
"tags:ulpgl",
"tags:orange",
"task_categories:summarization",
"task_ids:news-articles-summari... | https://huggingface.co/datasets/krm/for-ULPGL-Dissertation/resolve/main/README.md | ---
annotations_creators:
- other
language:
- fr
language_creators:
- other
license:
- other
multilinguality:
- monolingual
pretty_name: for-ULPGL-Dissertation
size_categories:
- 10K<n<100K
source_datasets:
- extended|orange_sum
tags:
- krm
- ulpgl
- orange
task_categories:
- summarization
task_ids:
- news-articles-summarization
---
# Dataset Card for [for-ULPGL-Dissertation]
## 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:** krm/for-ULPGL-Dissertation
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Ce dataset est essentiellement basé sur le dataset *GEM/Orange_sum* dédié à la synthèse d'articles en français. Il est constitué des données abstract de ce dataset (Orange_sum) auxquelles a été ajouté un certain nombre de synthèses générées par le système **Mon Résumeur** de **David Krame**.
### Supported Tasks and Leaderboards
Synthèse automatique
### Languages
Français
## Dataset Structure
### Data Fields
*summary* et *text* sont les champs du dataset avec :
**text** contient les textes et
**summary** les synthèses correspondantes.
### Data Splits
Pour le moment (le 16 Octobre 2022), le dataset est constitué de :
> **21721** données d'entraînement (split dénommé **train**)
> **1545** données de validation (split dénommé **validation**)
> **1581** données de test (split dénommé **test**)
## 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
|
mboth | null | null | null | false | 1 | false | mboth/klassifizierung_sichern | 2022-10-13T11:35:15.000Z | null | false | 7de339d57179849ed97848e379ceb01e2300d321 | [] | [] | https://huggingface.co/datasets/mboth/klassifizierung_sichern/resolve/main/README.md | ---
dataset_info:
features:
- name: text
dtype: string
- name: Beschreibung
dtype: string
- name: Name
dtype: string
- name: label
dtype:
class_label:
names:
0: Brandmeldeanlage
1: Brandschutzklappe
2: Rauchmeldeanlage
3: SichernAllgemein
splits:
- name: test
num_bytes: 27534.405120481926
num_examples: 133
- name: train
num_bytes: 219861.18975903615
num_examples: 1062
- name: valid
num_bytes: 27534.405120481926
num_examples: 133
download_size: 84648
dataset_size: 274930.0
---
# Dataset Card for "klassifizierung_sichern"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
viriato999 | null | null | null | false | null | false | viriato999/myselfinput | 2022-10-13T19:56:09.000Z | null | false | de815a397f35794aa63035d882f002b57c258a09 | [] | [
"doi:10.57967/hf/0041"
] | https://huggingface.co/datasets/viriato999/myselfinput/resolve/main/README.md | hello |
Thamognya | null | null | null | false | 1 | false | Thamognya/ALotNLI | 2022-10-13T12:58:20.000Z | null | false | 47719131c9b874ae69837038b360209a9ee48aa5 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:agpl-3.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:snli",
"source_datasets:multi_nli",
"source_datasets:anli",
"task_categories:text-classification",
"task_ids:natural-la... | https://huggingface.co/datasets/Thamognya/ALotNLI/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- agpl-3.0
multilinguality:
- monolingual
pretty_name: A Lot of NLI
size_categories:
- 100K<n<1M
source_datasets:
- snli
- multi_nli
- anli
task_categories:
- text-classification
task_ids:
- natural-language-inference
viewer: true
---
# Repo
Github Repo: [thamognya/TBertNLI](https://github.com/thamognya/TBertNLI) specifically in the [src/data directory](https://github.com/thamognya/TBertNLI/tree/master/src/data).
# Sample
``` premise hypothesis label
0 this church choir sings to the masses as they ... the church is filled with song 0
1 this church choir sings to the masses as they ... a choir singing at a baseball game 2
2 a woman with a green headscarf blue shirt and ... the woman is young 1
3 a woman with a green headscarf blue shirt and ... the woman is very happy 0
4 a woman with a green headscarf blue shirt and ... the woman has been shot 2
```
# Datsets Origin
As of now the marked datasets have been used to make this dataset and the other ones are todo
- [x] SNLI
- [x] MultiNLI
- SuperGLUE
- FEVER
- WIKI-FACTCHECK
- [x] ANLI
- more from huggingface
# Reasons
Just for finetuning of NLI models and purely made for NLI (not zero shot classification)
|
Gazoche | null | null | null | false | 23 | false | Gazoche/gundam-captioned | 2022-10-15T01:44:59.000Z | null | false | 5a2de83a1ba84820500e321ed830053d200b5ad1 | [] | [
"license:cc-by-nc-sa-4.0",
"annotations_creators:machine-generated",
"language:en",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<2K",
"task_categories:text-to-image"
] | https://huggingface.co/datasets/Gazoche/gundam-captioned/resolve/main/README.md | ---
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: 'Gundam captioned'
size_categories:
- n<2K
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for captioned Gundam
Scraped from mahq.net (https://www.mahq.net/mecha/gundam/index.htm) and manually cleaned to only keep drawings and "Mobile Suits" (i.e, humanoid-looking machines).
The captions were automatically generated from a generic hardcoded description + the dominant colors as described by [BLIP](https://github.com/salesforce/BLIP). |
Pavankalyan | null | null | null | false | null | false | Pavankalyan/SN_v1 | 2022-10-13T13:30:26.000Z | null | false | 290838850336d4f82991245a2eb6958ea1659d59 | [] | [] | https://huggingface.co/datasets/Pavankalyan/SN_v1/resolve/main/README.md | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: story
sequence: string
- name: title
dtype: string
- name: link
dtype: string
splits:
- name: train
num_bytes: 16879783141.0
num_examples: 575
download_size: 16107391373
dataset_size: 16879783141.0
---
# Dataset Card for "SN_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
projecte-aina | null | The Racó Forums Corpus is a 19,205,185-million-sentence corpus of Catalan user-generated text built from the forums of Racó Català.
Since the existing available corpora in Catalan lacked conversational data, we searched for a major source of such data for Catalan, and we found Racó Català, a popular multitopic online forum. We obtained a database dump and we transformed all the threads so that we obtained documents that traversed all the existing paths from the root (initial comment) to the leaves (last comment with no reply). In other words, if T is a tree such that T = {A,B,C,D} and the first comment is A that is replied by B and C independently, and, then, C is replied by D, we obtain two different documents A,B and A,C,D in the fairseq language modeling format. | false | null | false | projecte-aina/raco_forums | 2022-11-10T12:25:34.000Z | null | false | c8a3f55dbcc0196a37882d68f2aedf67bf5bbde1 | [] | [
"annotations_creators:no-annotation",
"language_creators:found",
"language:ca",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"task_categories:fill-mask"
] | https://huggingface.co/datasets/projecte-aina/raco_forums/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ca
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: Racó Forums
size_categories:
- ?
task_categories:
- fill-mask
task_ids: []
---
# Dataset Card for Racó Forums 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)
- [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
- **Point of Contact:** [blanca.calvo@bsc.es](blanca.calvo@bsc.es)
### Dataset Summary
The Racó Forums Corpus is a 19-million-sentence corpus of Catalan user-generated text built from the forums of [Racó Català](https://www.racocatala.cat/forums).
Since the existing available corpora in Catalan lacked conversational data, we searched for a major source of such data for Catalan, and we found Racó Català, a popular multitopic online forum. We obtained a database dump and we transformed all the threads so that we obtained documents that traversed all the existing paths from the root (initial comment) to the leaves (last comment with no reply). In other words, if T is a tree such that T = {A,B,C,D} and the first comment is A that is replied by B and C independently, and, then, C is replied by D, we obtain two different documents A,B and A,C,D in the fairseq language modeling format.
### Supported Tasks and Leaderboards
This corpus is mainly intended to pretrain language models and word representations.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
The sentences are ordered to preserve the forum structure of comments and answers. T is a tree such that T = {A,B,C,D} and the first comment is A that is replied by B and C independently, and, then, C is replied by D, we obtain two different documents A,B and A,C,D in the fairseq language modeling format.
### Data Instances
```
Ni la Paloma, ni la Razz, ni Bikini, ni res: la cafeteria Slàvia, a Les borges Blanques. Quin concertàs el d'ahir de Pomada!!! Fuà!!! va ser tan tan tan tan tan tan tan bo!!! Flipant!!! Irrepetible!!
És cert, l'Slàvia mola màxim.
```
### Data Splits
The dataset contains two splits: `train` and `valid`.
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. The data was structured to preserve the dialogue structure of forums.
### Source Data
#### Initial Data Collection and Normalization
The data was structured and anonymized by the BSC.
#### Who are the source language producers?
The data was provided by Racó Català.
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The data was annonymised to remove user names and emails, which were changed to random Catalan names. The mentions to the chat itself have also been changed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that, since the data comes from user-generated forums, this will contain biases, hate speech and toxic content. We have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution Non-commercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
```
### Contributions
Thanks to Racó Català for sharing their data.
| |
siberspace | null | null | null | false | null | false | siberspace/pascalsibertin | 2022-10-13T15:43:16.000Z | null | false | 1e30cec2fdcbf6faaa3084f16daa7883189cbbc3 | [] | [] | https://huggingface.co/datasets/siberspace/pascalsibertin/resolve/main/README.md | |
danf0 | null | null | null | false | null | false | danf0/snli_shortcut_grammar | 2022-10-13T14:44:55.000Z | null | false | c434323f1afa94715848c1823c35fbf2338632f9 | [] | [] | https://huggingface.co/datasets/danf0/snli_shortcut_grammar/resolve/main/README.md | ---
dataset_info:
features:
- name: uid
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: string
- name: tree
dtype: string
splits:
- name: train
num_bytes: 5724044
num_examples: 16380
download_size: 0
dataset_size: 5724044
---
# Dataset Card for "snli_shortcut_grammar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
danf0 | null | null | null | false | null | false | danf0/subj_shortcut_grammar | 2022-10-13T14:54:05.000Z | null | false | f1243ea9fec9059f5b69c8f9bac9c79edc1ee22e | [] | [] | https://huggingface.co/datasets/danf0/subj_shortcut_grammar/resolve/main/README.md | ---
dataset_info:
features:
- name: uid
dtype: string
- name: sentence
dtype: string
- name: label
dtype: string
- name: tree
dtype: string
splits:
- name: train
num_bytes: 1077802
num_examples: 2000
download_size: 522313
dataset_size: 1077802
---
# Dataset Card for "subj_shortcut_grammar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dennlinger | null | null | null | false | 1 | false | dennlinger/wiki-paragraphs | 2022-10-13T22:12:37.000Z | null | false | bc7ad2163db81844b31026d76cc244b816d8e96c | [] | [
"arxiv:2012.03619",
"annotations_creators:machine-generated",
"language:en",
"language_creators:crowdsourced",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"tags:wikipedia",
"tags:self-similarity",
"task_categories:text-classifi... | https://huggingface.co/datasets/dennlinger/wiki-paragraphs/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- crowdsourced
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: wiki-paragraphs
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- wikipedia
- self-similarity
task_categories:
- text-classification
- sentence-similarity
task_ids:
- semantic-similarity-scoring
---
# Dataset Card for `wiki-paragraphs`
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/dennlinger/TopicalChange
- **Paper:** https://arxiv.org/abs/2012.03619
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Dennis Aumiller](aumiller@informatik.uni-heidelberg.de)
### Dataset Summary
The wiki-paragraphs dataset is constructed by automatically sampling two paragraphs from a Wikipedia article. If they are from the same section, they will be considered a "semantic match", otherwise as "dissimilar". Dissimilar paragraphs can in theory also be sampled from other documents, but have not shown any improvement in the particular evaluation of the linked work.
The alignment is in no way meant as an accurate depiction of similarity, but allows to quickly mine large amounts of samples.
### Supported Tasks and Leaderboards
The dataset can be used for "same-section classification", which is a binary classification task (either two sentences/paragraphs belong to the same section or not).
This can be combined with document-level coherency measures, where we can check how many misclassifications appear within a single document.
Please refer to [our paper](https://arxiv.org/abs/2012.03619) for more details.
### Languages
The data was extracted from English Wikipedia, therefore predominantly in English.
## Dataset Structure
### Data Instances
A single instance contains three attributes:
```
{
"sentence1": "<Sentence from the first paragraph>",
"sentence2": "<Sentence from the second paragraph>",
"label": 0/1 # 1 indicates two belong to the same section
}
```
### Data Fields
- sentence1: String containing the first paragraph
- sentence2: String containing the second paragraph
- label: Integer, either 0 or 1. Indicates whether two paragraphs belong to the same section (1) or come from different sections (0)
### Data Splits
We provide train, validation and test splits, which were split as 80/10/10 from a randomly shuffled original data source.
In total, we provide 25375583 training pairs, as well as 3163685 validation and test instances, respectively.
## Dataset Creation
### Curation Rationale
The original idea was applied to self-segmentation of Terms of Service documents. Given that these are of domain-specific nature, we wanted to provide a more generally applicable model trained on Wikipedia data.
It is meant as a cheap-to-acquire pre-training strategy for large-scale experimentation with semantic similarity for long texts (paragraph-level).
Based on our experiments, it is not necessarily sufficient by itself to replace traditional hand-labeled semantic similarity datasets.
### Source Data
#### Initial Data Collection and Normalization
The data was collected based on the articles considered in the Wiki-727k dataset by Koshorek et al. The dump of their dataset can be found through the [respective Github repository](https://github.com/koomri/text-segmentation). Note that we did *not* use the pre-processed data, but rather only information on the considered articles, which were re-acquired from Wikipedia at a more recent state.
This is due to the fact that paragraph information was not retained by the original Wiki-727k authors.
We did not verify the particular focus of considered pages.
#### Who are the source language producers?
We do not have any further information on the contributors; these are volunteers contributing to en.wikipedia.org.
### Annotations
#### Annotation process
No manual annotation was added to the dataset.
We automatically sampled two sections from within the same article; if these belong to the same section, they were assigned a label indicating the "similarity" (1), otherwise the label indicates that they are not belonging to the same section (0).
We sample three positive and three negative samples per section, per article.
#### Who are the annotators?
No annotators were involved in the process.
### Personal and Sensitive Information
We did not modify the original Wikipedia text in any way. Given that personal information, such as dates of birth (e.g., for a person of interest) may be on Wikipedia, this information is also considered in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of the dataset is to serve as a *pre-training addition* for semantic similarity learning.
Systems building on this dataset should consider additional, manually annotated data, before using a system in production.
### Discussion of Biases
To our knowledge, there are some works indicating that male people have a several times larger chance of having a Wikipedia page created (especially in historical contexts). Therefore, a slight bias towards over-representation might be left in this dataset.
### Other Known Limitations
As previously stated, the automatically extracted semantic similarity is not perfect; it should be treated as such.
## Additional Information
### Dataset Curators
The dataset was originally developed as a practical project by Lucienne-Sophie Marm� under the supervision of Dennis Aumiller.
Contributions to the original sampling strategy were made by Satya Almasian and Michael Gertz
### Licensing Information
Wikipedia data is available under the CC-BY-SA 3.0 license.
### Citation Information
```
@inproceedings{DBLP:conf/icail/AumillerAL021,
author = {Dennis Aumiller and
Satya Almasian and
Sebastian Lackner and
Michael Gertz},
editor = {Juliano Maranh{\~{a}}o and
Adam Zachary Wyner},
title = {Structural text segmentation of legal documents},
booktitle = {{ICAIL} '21: Eighteenth International Conference for Artificial Intelligence
and Law, S{\~{a}}o Paulo Brazil, June 21 - 25, 2021},
pages = {2--11},
publisher = {{ACM}},
year = {2021},
url = {https://doi.org/10.1145/3462757.3466085},
doi = {10.1145/3462757.3466085}
}
``` |
Narsil | null | null | null | false | null | false | Narsil/test | 2022-10-13T16:28:14.000Z | null | false | a35ff716f3044a2fba62bc09106c612147766b25 | [] | [
"benchmark:ttt",
"task:xxx",
"type:prediction"
] | https://huggingface.co/datasets/Narsil/test/resolve/main/README.md | ---
benchmark: ttt
task: xxx
type: prediction
---
# Batch job
model_id: {model_id}
dataset_name: {job.dataset_name}
dataset_config: {job.dataset_config}
dataset_split: {job.dataset_split}
dataset_column: {job.dataset_column} |
ellabettison | null | null | null | false | 2 | false | ellabettison/processed_finbert_dataset_concat_small | 2022-10-13T18:46:46.000Z | null | false | 56de5f75013a141db1e447ac2d99dbcfadda4893 | [] | [] | https://huggingface.co/datasets/ellabettison/processed_finbert_dataset_concat_small/resolve/main/README.md | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 19170800.0
num_examples: 5323
download_size: 4979632
dataset_size: 19170800.0
---
# Dataset Card for "processed_finbert_dataset_concat_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861028 | 2022-10-13T16:36:34.000Z | null | false | d32ebd8c3c0866b82ddf50a414b0e87cc047202a | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861028/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-560m
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: all
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-560m
* Dataset: phpthinh/examplei
* Config: all
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761023 | 2022-10-13T16:33:50.000Z | null | false | 42737255477a4ba10197c9f2cedb10951b459626 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761023/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-560m
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: match
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-560m
* Dataset: phpthinh/examplei
* Config: match
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761027 | 2022-10-13T19:15:51.000Z | null | false | 2d08cabd04857edf128ef4e8686c8306e3827912 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761027/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-7b1
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: match
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-7b1
* Dataset: phpthinh/examplei
* Config: match
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961033 | 2022-10-13T15:52:18.000Z | null | false | 86d4d3c4c650524d4e6061df4c2c1654ca749d67 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961033/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-560m
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-560m
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761025 | 2022-10-13T17:00:52.000Z | null | false | 9e622d9ca71fd45291935717af7ae5ac8965cd8c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761025/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b7
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: match
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-1b7
* Dataset: phpthinh/examplei
* Config: match
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761024 | 2022-10-13T16:39:35.000Z | null | false | 63a8b8a186bced02a57b89b2ba42cc898efb0dd8 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761024/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b1
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: match
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-1b1
* Dataset: phpthinh/examplei
* Config: match
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761026 | 2022-10-13T17:13:43.000Z | null | false | 6657d73dbd64c8fae8f1b322a2125f83ee77d23d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-match-bd10ea-1748761026/resolve/main/README.md | ---
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: match
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: match
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861032 | 2022-10-13T19:34:07.000Z | null | false | 96e7c17d355c9a960767c4ceb7428ee215a736fe | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861032/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-7b1
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: all
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-7b1
* Dataset: phpthinh/examplei
* Config: all
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861029 | 2022-10-13T17:20:41.000Z | null | false | dcc7e5080c28aef00de4d89ee1e812c3e4408433 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861029/resolve/main/README.md | ---
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: all
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: all
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861031 | 2022-10-13T17:05:40.000Z | null | false | ae03eb0b6145b1615869e9dfd305c69e29cefeb0 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861031/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b7
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: all
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-1b7
* Dataset: phpthinh/examplei
* Config: all
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861030 | 2022-10-13T16:41:32.000Z | null | false | 098d1710ff5b7142a54bdadc8df19931280105cb | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861030/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b1
metrics: ['f1']
dataset_name: phpthinh/examplei
dataset_config: all
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-1b1
* Dataset: phpthinh/examplei
* Config: all
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961035 | 2022-10-13T15:53:05.000Z | null | false | c7ec9581b7eb4a040dd84a1888cfe42a0a963b3c | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961035/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b1
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-1b1
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961034 | 2022-10-13T15:56:46.000Z | null | false | 250efc78a2ab118fa00ba5871511caad7cf77b77 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961034/resolve/main/README.md | ---
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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961036 | 2022-10-13T15:55:31.000Z | null | false | 794749a5402864cad20f58d2cd06b034137b5c70 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961036/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b7
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-1b7
* 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. |
autoevaluate | null | null | null | false | null | false | autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961037 | 2022-10-13T16:08:31.000Z | null | false | ee0fefac8bae648f9a85e33f52fc39fd2fd2ddce | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:phpthinh/examplei"
] | https://huggingface.co/datasets/autoevaluate/autoeval-eval-phpthinh__examplei-mismatch-1389aa-1748961037/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplei
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-7b1
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-7b1
* 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. |
csebuetnlp | null | to be added | We present a high quality bangla paraphrase dataset containing about 466k paraphrase pairs. The paraphrases ensures high quality by being semantically coherent and syntactically diverse. | false | 14 | false | csebuetnlp/BanglaParaphrase | 2022-11-14T15:39:43.000Z | null | false | 55a7cf0a0b66ce56ba9c35e5a56bf52c88adfd30 | [] | [
"arxiv:2210.05109",
"annotations_creators:found",
"language_creators:found",
"language:bn",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:100k<n<1M",
"source_datasets:original",
"task_categories:text2text-generation",
"tags:conditional-text-generation",
"tags:paraphr... | https://huggingface.co/datasets/csebuetnlp/BanglaParaphrase/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- bn
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100k<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: BanglaParaphrase
tags:
- conditional-text-generation
- paraphrase-generation
---
# Dataset Card for "BanglaParaphrase"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/csebuetnlp/banglaparaphrase](https://github.com/csebuetnlp/banglaparaphrase)
- **Paper:** [BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset](https://arxiv.org/abs/2210.05109)
- **Point of Contact:** [Najrin Sultana](mailto:nazrinshukti@gmail.com)
### Dataset Summary
We present BanglaParaphrase, a high quality synthetic Bangla paraphrase dataset containing about 466k paraphrase pairs.
The paraphrases ensures high quality by being semantically coherent and syntactically diverse.
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Languages
- `bengali`
## Loading the dataset
```python
from datasets import load_dataset
from datasets import load_dataset
ds = load_dataset("csebuetnlp/BanglaParaphrase")
```
## Dataset Structure
### Data Instances
One example from the `train` part of the dataset is given below in JSON format.
```
{
"source": "বেশিরভাগ সময় প্রকৃতির দয়ার ওপরেই বেঁচে থাকতেন উপজাতিরা।",
"target": "বেশিরভাগ সময়ই উপজাতিরা প্রকৃতির দয়ার উপর নির্ভরশীল ছিল।"
}
```
### Data Fields
- 'source': A string representing the source sentence.
- 'target': A string representing the target sentence.
### Data Splits
Dataset with train-dev-test example counts are given below:
Language | ISO 639-1 Code | Train | Validation | Test |
-------------- | ---------------- | ------- | ----- | ------ |
Bengali | bn | 419, 967 | 233, 31 | 233, 32 |
## Dataset Creation
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Source Data
[Roar Bangla](https://roar.media/bangla)
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
### Annotations
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Annotation process
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Who are the annotators?
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Discussion of Biases
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Other Known Limitations
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
## Additional Information
### Dataset Curators
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
```
@article{akil2022banglaparaphrase,
title={BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset},
author={Akil, Ajwad and Sultana, Najrin and Bhattacharjee, Abhik and Shahriyar, Rifat},
journal={arXiv preprint arXiv:2210.05109},
year={2022}
}
```
### Contributions
|
Wauplin | null | null | null | false | null | false | Wauplin/tmp-test | 2022-10-13T16:50:24.000Z | null | false | 16236541e739287e60ddc54a2b8dccd08c8815c5 | [] | [] | https://huggingface.co/datasets/Wauplin/tmp-test/resolve/main/README.md | ---
benchmark: ttt
task: xxx
type: prediction
--
# Batch job
model_id: {model_id}
dataset_name: {job.dataset_name}
dataset_config: {job.dataset_config}
dataset_split: {job.dataset_split}
dataset_column: {job.dataset_column} |
williambr | null | null | null | false | null | false | williambr/snowmed_signsymptom | 2022-10-13T17:34:49.000Z | null | false | 816f7881391c6ee586eb9fbdb784619871fc04e2 | [] | [
"license:mit"
] | https://huggingface.co/datasets/williambr/snowmed_signsymptom/resolve/main/README.md | ---
license: mit
---
|
Whispering-GPT | null | null | null | false | 25 | false | Whispering-GPT/whisper-transcripts-the-verge | 2022-10-23T10:54:59.000Z | null | false | fd35c6358fd302556f3c8d52acdd19ed8e61381e | [] | [] | https://huggingface.co/datasets/Whispering-GPT/whisper-transcripts-the-verge/resolve/main/README.md | annotations_creators:
- machine-generated
language:
- en
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
paperswithcode_id: wikitext-2
pretty_name: Whisper-Transcripts
size_categories:
- 1M<n<10M
source_datasets:
- original
tags: []
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling |
ChiangLz | null | null | null | false | 52 | false | ChiangLz/zapotecojuchitan | 2022-10-23T18:48:42.000Z | null | false | 5a28efd1123b3a08a64878f48dd171a8a859389d | [] | [
"license:cc-by-nc-nd-4.0"
] | https://huggingface.co/datasets/ChiangLz/zapotecojuchitan/resolve/main/README.md | ---
license: cc-by-nc-nd-4.0
---
|
DimDymov | null | null | null | false | null | false | DimDymov/Vilmarina | 2022-10-13T19:12:38.000Z | null | false | ddb7af253443c37bc559afd65936fe21a5177d15 | [] | [
"license:cc-by-nd-4.0"
] | https://huggingface.co/datasets/DimDymov/Vilmarina/resolve/main/README.md | ---
license: cc-by-nd-4.0
---
|
dpasch01 | null | null | null | false | 3 | false | dpasch01/sidewalk-imagery | 2022-10-13T19:12:05.000Z | null | false | f41838f3135528d90d7727487737421a01b7866d | [] | [] | https://huggingface.co/datasets/dpasch01/sidewalk-imagery/resolve/main/README.md | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 3202716.0
num_examples: 10
download_size: 3192547
dataset_size: 3202716.0
---
# Dataset Card for "sidewalk-imagery"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kavindu99 | null | null | null | false | null | false | Kavindu99/celeb-identities | 2022-10-13T20:27:44.000Z | null | false | 9a8e1119eccce3f5559d8d26538230d3a4f90f3f | [] | [] | https://huggingface.co/datasets/Kavindu99/celeb-identities/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: Emilia_Clarke
1: Henry_Cavil
2: Jason_Mamoa
3: Sadie_Sink
4: Sangakkara
5: Zendaya
splits:
- name: train
num_bytes: 160371.0
num_examples: 18
download_size: 160832
dataset_size: 160371.0
---
# Dataset Card for "celeb-identities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jamescalam | null | null | null | false | 34 | false | jamescalam/youtube-transcriptions | 2022-10-22T01:20:07.000Z | null | false | 174b3afde4a8dec38e49d843fc9fc0857c4a8bd9 | [] | [
"annotations_creators:no-annotation",
"language:en",
"language_creators:found",
"license:afl-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"tags:youtube",
"tags:technical",
"tags:speech to text",
"tags:speech",
"tags:video",
"tags:video search... | https://huggingface.co/datasets/jamescalam/youtube-transcriptions/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Youtube Transcriptions
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- technical
- speech to text
- speech
- video
- video search
- audio
- audio search
task_categories:
- conversational
- question-answering
- text-retrieval
- visual-question-answering
task_ids:
- open-domain-qa
- extractive-qa
- document-retrieval
- visual-question-answering
---
The YouTube transcriptions dataset contains technical tutorials (currently from [James Briggs](https://www.youtube.com/c/jamesbriggs), [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ), and [AI Coffee Break](https://www.youtube.com/c/aicoffeebreak)) transcribed using [OpenAI's Whisper](https://huggingface.co/openai/whisper-large) (large). Each row represents roughly a sentence-length chunk of text alongside the video URL and timestamp.
Note that each item in the dataset contains just a short chunk of text. For most use cases you will likely need to merge multiple rows to create more substantial chunks of text, if you need to do that, this code snippet will help:
```python
from datasets import load_dataset
# first download the dataset
data = load_dataset(
'jamescalam/youtube-transcriptions',
split='train'
)
new_data = [] # this will store adjusted data
window = 6 # number of sentences to combine
stride = 3 # number of sentences to 'stride' over, used to create overlap
for i in range(0, len(data), stride):
i_end = min(len(data)-1, i+window)
if data[i]['title'] != data[i_end]['title']:
# in this case we skip this entry as we have start/end of two videos
continue
# create larger text chunk
text = ' '.join(data[i:i_end]['text'])
# add to adjusted data list
new_data.append({
'start': data[i]['start'],
'end': data[i_end]['end'],
'title': data[i]['title'],
'text': text,
'id': data[i]['id'],
'url': data[i]['url'],
'published': data[i]['published']
})
``` |
gart-labor | null | null | null | false | 6 | false | gart-labor/pumpnli | 2022-10-24T10:31:52.000Z | null | false | bcdc6b6655152b65054bafbf17187008456474f8 | [] | [
"language:en",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"task_categories:text-classification",
"task_categories:sentence-similarity",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring"
] | https://huggingface.co/datasets/gart-labor/pumpnli/resolve/main/README.md | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- sentence-similarity
task_ids:
- natural-language-inference
- semantic-similarity-scoring
---
# Dataset Card for PumpNLI
## Dataset Description
- **Homepage:** [Labor für Regelungstechnik und Gebäudeautomation, TH Köln, Germany](https://www.th-koeln.de/anlagen-energie-und-maschinensysteme/labor-fuer-gebaeudeautomation-und-regelungstechnik_16189.php)
- **Paper:** [tba]()
- **Point of Contact:** [R. Benfer](mailto:rebekka.benfer@th-koeln.de)
### Dataset Summary
The PumpNLI corpus (version 1.0) is a collection of 1725 human-written English building automation name and description pairs for classification of building automation properties of a pump with the label entailment (0). The "premise" contains properties of the [ECLASS standard](https://eclass.eu/eclass-standard). The "hypothesis" consists of associated paraphrases. The dataset still has to be extended by suitable pairs of the label neutral (1) and contradiction (2).
### Supported Task
This dataset is to be used for training and evaluating methods of [Semantic Matching in Heterogenous I4.0 Asset Administration Shells](https://www.degruyter.com/document/doi/10.1515/auto-2021-0050/html) (Both, Müller, Diedrich 2021).
### Data Instances
For each instance, there is a string for the premise, a string for the hypothesis, and an integer for the label. Note that each premise may appear several times with a different hypothesis.
```
{'premise': 'Unique combination of numbers and letters used to identify the device once it has been manufactured ; Serial number'
'hypothesis': 'alphanumeric string defined by the OEM identifying a specific piece of equipment ; Producer serial number'
'label': 0}
```
### Data Splits
The dataset has not been splitted yet, because an extension of the dataset with contradictions and neutral combinations still has to be made.
### Contributors
- [R. Benfer](https://www.linkedin.com/in/rebekka-benfer/)
- J. Schmock
- J. Mosig
- Homepage: [Labor für Regelungstechnik und Gebäudeautomation, TH Köln, Germany](https://www.th-koeln.de/anlagen-energie-und-maschinensysteme/labor-fuer-gebaeudeautomation-und-regelungstechnik_16189.php)
- Instagram: [Helden der GA](https://www.instagram.com/helden_der_ga/?hl=de) |
joey234 | null | null | null | false | 35 | false | joey234/nan-nli | 2022-10-13T23:18:18.000Z | null | false | bb4424259da93902b3ec2ece55a744f23d0793d0 | [] | [
"annotations_creators:expert-generated",
"language:en",
"language_creators:expert-generated",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"tags:negation",
"task_categories:text-classification",
"task_ids:natural-language-inference"
] | https://huggingface.co/datasets/joey234/nan-nli/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: nan-nli
size_categories:
- n<1K
source_datasets:
- original
tags:
- negation
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# 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
Natural Language Inference
Text Classification
### Languages
en
## Dataset Structure
### Data Instances
### Data Fields
premise:
hypothesis:
label:
### Data Splits
Evaluation: 258 samples
## Dataset Creation
### Curation Rationale
Extracting samples corresponding to different linguistics constructions of negation.
### Source Data
Geoffrey K. Pullum and Rodney Huddleston. 2002. Negation, chapter 9. Cambridge University Press.
#### 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?
The annotators are the authors of the papers, one of whom holds a graduate degree in linguistics.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@joey234](https://github.com/joey234) for adding this dataset. |
annaludicode | null | null | null | false | null | false | annaludicode/ladiesInColoredWaterStyle | 2022-10-13T23:43:33.000Z | null | false | cacce71315e1bbff74962098ea588386b63ee60c | [] | [
"license:artistic-2.0"
] | https://huggingface.co/datasets/annaludicode/ladiesInColoredWaterStyle/resolve/main/README.md | ---
license: artistic-2.0
---
|
zhenzi | null | null | null | false | null | false | zhenzi/test | 2022-10-18T02:03:54.000Z | null | false | c8468b5b341979f7e59f79c048a2ab61870f6c98 | [] | [] | https://huggingface.co/datasets/zhenzi/test/resolve/main/README.md |
## test |
VanessaSchenkel | null | null | null | false | 1 | false | VanessaSchenkel/pt-inflections | 2022-11-07T03:44:23.000Z | null | false | 1eeb1fb9c1d9e3c8c6c9e5becd15a560e2ab29c5 | [] | [
"annotations_creators:found",
"language:pt",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia"
] | https://huggingface.co/datasets/VanessaSchenkel/pt-inflections/resolve/main/README.md | ---
annotations_creators:
- found
language:
- pt
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: "dicion\xE1rio de portugu\xEAs"
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
tags: []
task_categories: []
task_ids: []
---
# Dataset Card for Dicionário Português
It is a list of 53138 portuguese words with its inflections.
How to use it:
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/pt-inflections", field="data")
remote_dataset
```
Output:
```
DatasetDict({
train: Dataset({
features: ['word', 'pos', 'forms'],
num_rows: 53138
})
})
```
Exemple:
```
remote_dataset["train"][42]
```
Output:
```
{'word': 'numeral',
'pos': 'noun',
'forms': [{'form': 'numerais', 'tags': ['plural']}]}
```
|
recapper | null | null | null | false | 1 | false | recapper/Course_summaries_dataset | 2022-10-25T16:03:24.000Z | null | false | 2af016d62b5b4de22045d3385ff117b9c2d11ce5 | [] | [
"language:en",
"license:apache-2.0",
"size_categories:1M<n<10M",
"task_categories:summarization",
"task_categories:text2text-generation",
"tags:conditional-text-generation"
] | https://huggingface.co/datasets/recapper/Course_summaries_dataset/resolve/main/README.md | ---
language:
- en
license: apache-2.0
size_categories:
- 1M<n<10M
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# About Dataset
The dataset consists of data from a bunch of youtube videos ranging from videos from fastai lessons, FSDL lesson to random videos teaching something.
In total this dataset contains 600 chapter markers in youtube and contains 25, 000 lesson transcript.
This dataset can be used for NLP tasks like summarization, topic segmentation etc. You can refer to some of the models we have trained with this dataset
in [github repo link](https://github.com/ohmeow/fsdl_2022_course_project) for Full stack deep learning 2022 projects.
|
bburns | null | null | null | false | null | false | bburns/celeb-identities | 2022-10-14T15:20:20.000Z | null | false | aaaa35d10817ea9ca2550c3970aa413f9fb30bd4 | [] | [] | https://huggingface.co/datasets/bburns/celeb-identities/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: Geohot
1: Grimes
2: Kanye
3: PG
4: Riva
5: Trump
splits:
- name: train
num_bytes: 4350264.0
num_examples: 18
download_size: 4342420
dataset_size: 4350264.0
---
# Dataset Card for "celeb-identities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jamescalam | null | null | null | false | 70 | false | jamescalam/channel-metadata | 2022-10-26T01:05:55.000Z | null | false | bfbba48d89b4213fa5cd9df07b675ba461d51d4f | [] | [
"annotations_creators:no-annotation",
"language:en",
"language_creators:found",
"license:afl-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"tags:youtube",
"tags:video",
"tags:video metadata",
"tags:tech",
"tags:science and tech",
"task_categor... | https://huggingface.co/datasets/jamescalam/channel-metadata/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Tech Channels Metadata
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- video
- video metadata
- tech
- science and tech
task_categories:
- other
task_ids: []
---
Dataset containing video metadata from a few tech channels, i.e.
* [James Briggs](https://youtube.com/c/JamesBriggs)
* [Yannic Kilcher](https://www.youtube.com/c/YannicKilcher)
* [sentdex](https://www.youtube.com/c/sentdex)
* [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ)
* [AI Coffee Break with Letitia](https://www.youtube.com/c/AICoffeeBreak)
* [Alex Ziskind](https://youtube.com/channel/UCajiMK_CY9icRhLepS8_3ug) |
ratishsp | null | null | null | false | 34 | false | ratishsp/newshead | 2022-10-14T07:42:08.000Z | null | false | 8f7568a6bea2403221f304edd9212a7d00a980a2 | [] | [
"license:mit"
] | https://huggingface.co/datasets/ratishsp/newshead/resolve/main/README.md | ---
license: mit
---
|
dpasch01 | null | null | null | false | 1 | false | dpasch01/leaflet_offers-clone | 2022-10-14T06:11:34.000Z | null | false | 2d78d4a8000795b3520df6d58966673ae099e912 | [] | [] | https://huggingface.co/datasets/dpasch01/leaflet_offers-clone/resolve/main/README.md | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 5623867.0
num_examples: 4
download_size: 5356712
dataset_size: 5623867.0
---
# Dataset Card for "leaflet_offers-clone"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ndxbxrme | null | null | null | false | 3 | false | ndxbxrme/audio-diffusion-256-isolated-drums | 2022-10-14T07:06:35.000Z | null | false | f3e50ecc00155232eda7815b4a26796130c91bc6 | [] | [] | https://huggingface.co/datasets/ndxbxrme/audio-diffusion-256-isolated-drums/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: audio_file
dtype: string
- name: slice
dtype: int16
splits:
- name: train
num_bytes: 367170599.374
num_examples: 8589
download_size: 366838959
dataset_size: 367170599.374
---
# Dataset Card for "audio-diffusion-256-isolated-drums"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pandaman2020 | null | null | null | false | null | false | pandaman2020/SDTraining | 2022-10-14T09:28:46.000Z | null | false | 2435e2e0aa336fb639ddc66f4497ac35f1b82572 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/pandaman2020/SDTraining/resolve/main/README.md | ---
license: cc-by-4.0
---
|
zhenzi | null | @software{2022,
title=数据集标题,
author=zhenzi,
year={2022},
month={March},
publisher = {GitHub}
} | 数据集描述. | false | 3 | false | zhenzi/data_process | 2022-10-18T02:13:05.000Z | null | false | 72659de0f473e99331c92038be331d7c864a7439 | [] | [] | https://huggingface.co/datasets/zhenzi/data_process/resolve/main/README.md | |
pcoloc | null | null | null | false | null | false | pcoloc/autotrain-data-trackerlora_less_data | 2022-10-14T12:06:37.000Z | null | false | bc167f78800fbaa9da3c7d66e28c3d24f6fd00ee | [] | [] | https://huggingface.co/datasets/pcoloc/autotrain-data-trackerlora_less_data/resolve/main/README.md | ---
{}
---
# AutoTrain Dataset for project: trackerlora_less_data
## Dataset Description
This dataset has been automatically processed by AutoTrain for project trackerlora_less_data.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"id": 444,
"feat_rssi": -113.0,
"feat_snr": -9.25,
"feat_spreading_factor": 7,
"feat_potencia": 14,
"target": 308.0
},
{
"id": 144,
"feat_rssi": -77.0,
"feat_snr": 8.800000190734863,
"feat_spreading_factor": 7,
"feat_potencia": 14,
"target": 126.0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"id": "Value(dtype='int64', id=None)",
"feat_rssi": "Value(dtype='float64', id=None)",
"feat_snr": "Value(dtype='float64', id=None)",
"feat_spreading_factor": "Value(dtype='int64', id=None)",
"feat_potencia": "Value(dtype='int64', id=None)",
"target": "Value(dtype='float32', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 139 |
| valid | 40 |
|
gregkowal | null | null | null | false | null | false | gregkowal/crime-time-game-style | 2022-10-14T12:14:15.000Z | null | false | 678e10f1ea8f5995950f72f9abac070c00759051 | [] | [
"license:other"
] | https://huggingface.co/datasets/gregkowal/crime-time-game-style/resolve/main/README.md | ---
license: other
---
|
AndyChiang | null | null | null | false | 3 | false | AndyChiang/cloth | 2022-10-14T14:10:37.000Z | null | false | 205ca64c78a48e01e0ba211163c89e77c027a4ff | [] | [
"multilinguality:monolingual",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"tags:cloze",
"tags:mid-school",
"tags:high-school",
"tags:exams",
"task_categories:fill-mask"
] | https://huggingface.co/datasets/AndyChiang/cloth/resolve/main/README.md | ---
pretty_name: cloth
multilinguality:
- monolingual
language:
- en
license:
- mit
size_categories:
- 10K<n<100K
tags:
- cloze
- mid-school
- high-school
- exams
task_categories:
- fill-mask
---
# cloth
**CLOTH** is a dataset which is a collection of nearly 100,000 cloze questions from middle school and high school English exams. The detail of CLOTH dataset is shown below.
| Number of questions | Train | Valid | Test |
| ------------------- | ----- | ----- | ----- |
| **Middle school** | 22056 | 3273 | 3198 |
| **High school** | 54794 | 7794 | 8318 |
| **Total** | 76850 | 11067 | 11516 |
Source: https://www.cs.cmu.edu/~glai1/data/cloth/ |
alfredodeza | null | null | null | false | 2 | false | alfredodeza/wine-ratings | 2022-10-15T13:09:06.000Z | null | false | 830447e72563191bcd52dce78495d7153f02c757 | [] | [] | https://huggingface.co/datasets/alfredodeza/wine-ratings/resolve/main/README.md | ---
dataset_info:
features:
- name: name
dtype: string
- name: region
dtype: string
- name: variety
dtype: string
- name: rating
dtype: float32
- name: notes
dtype: string
splits:
- name: test
num_bytes: 82422
num_examples: 200
- name: train
num_bytes: 13538613
num_examples: 32780
- name: validation
num_bytes: 83047
num_examples: 200
download_size: 0
dataset_size: 13704082
---
# wine-ratings
Processing, EDA, and ML on wine ratings |
FSDL-Fashion | null | null | null | false | 1 | false | FSDL-Fashion/dummy_swin_pipe_5k | 2022-10-14T12:46:02.000Z | null | false | 60582e99b1ebd35b4ba41cf11b19a6aaa87db726 | [] | [] | https://huggingface.co/datasets/FSDL-Fashion/dummy_swin_pipe_5k/resolve/main/README.md | ---
dataset_info:
features:
- name: path
dtype: string
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 20800000
num_examples: 5000
download_size: 21312459
dataset_size: 20800000
---
# Dataset Card for "dummy_swin_pipe_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AndyChiang | null | null | null | false | 2 | false | AndyChiang/dgen | 2022-10-14T14:19:16.000Z | null | false | 104c7e6a9c489be3b34bfdb905cf124063473ea7 | [] | [
"multilinguality:monolingual",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"tags:cloze",
"tags:sciq",
"tags:mcql",
"tags:ai2 science questions",
"task_categories:fill-mask"
] | https://huggingface.co/datasets/AndyChiang/dgen/resolve/main/README.md | ---
pretty_name: dgen
multilinguality:
- monolingual
language:
- en
license:
- mit
size_categories:
- 1K<n<10K
tags:
- cloze
- sciq
- mcql
- ai2 science questions
task_categories:
- fill-mask
---
# dgen
**DGen** is a cloze questions dataset which covers multiple domains including science, vocabulary, common sense and trivia. It is compiled from a wide variety of datasets including SciQ, MCQL, AI2 Science Questions, etc. The detail of DGen dataset is shown below.
| DGen dataset | Train | Valid | Test | Total |
| ----------------------- | ----- | ----- | ---- | ----- |
| **Number of questions** | 2321 | 300 | 259 | 2880 |
Source: https://github.com/DRSY/DGen |
randomwalksky | null | null | null | false | null | false | randomwalksky/cup | 2022-10-14T13:49:09.000Z | null | false | 86d7547dd834ab89cc6715b07eb8bef15a8ee9f3 | [] | [
"license:openrail"
] | https://huggingface.co/datasets/randomwalksky/cup/resolve/main/README.md | ---
license: openrail
---
|
ellabettison | null | null | null | false | 43 | false | ellabettison/processed_roberta_dataset_padded_small | 2022-10-14T14:22:13.000Z | null | false | f3ff431690b8ccbd5b07e3d5b47179b2143427d9 | [] | [] | https://huggingface.co/datasets/ellabettison/processed_roberta_dataset_padded_small/resolve/main/README.md | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 54006000.0
num_examples: 500000
download_size: 8754966
dataset_size: 54006000.0
---
# Dataset Card for "processed_roberta_dataset_padded_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FSDL-Fashion | null | null | null | false | 5 | false | FSDL-Fashion/dummy_swin_pipe | 2022-10-14T14:33:52.000Z | null | false | 41b0cc22d1bf22ab270d99a902d0e349eb766d8e | [] | [] | https://huggingface.co/datasets/FSDL-Fashion/dummy_swin_pipe/resolve/main/README.md | ---
dataset_info:
features:
- name: path
dtype: string
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 416000000
num_examples: 100000
download_size: 420001566
dataset_size: 416000000
---
# Dataset Card for "dummy_swin_pipe"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anonymous20221014 | null | null | null | false | null | false | anonymous20221014/submissiondata2022 | 2022-10-14T15:20:32.000Z | null | false | 35ff44404677b7ca0a48ef10f85eefdc19b17c18 | [] | [
"language:en",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"tags:business",
"tags:company websites",
"task_categories:fill-mask",
"task_categories:other",
"task_ids:masked-language-modeling"
] | https://huggingface.co/datasets/anonymous20221014/submissiondata2022/resolve/main/README.md | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: CompanyWeb
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- business
- company websites
task_categories:
- fill-mask
- other
task_ids:
- masked-language-modeling
---
# Dataset Card for "CompanyWeb"
## 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:** [PLACEHOLDER]()
- **Repository:** [PLACEHOLDER]()
- **Paper:** [PLACEHOLDER]()
- **Leaderboard:** [PLACEHOLDER]()
- **Point of Contact:** [PLACEHOLDER]()
### Dataset Summary
The dataset contains textual content extracted from 1,788,413 company web pages of 393,542 companies. The companies included in the dataset are small, medium and large international enterprises including publicly listed companies. Additional company information is provided in form of the corresponding Standard Industry Classification (SIC) label `sic4`.
The text includes all textual information contained on the website with a timeline ranging from 2014 to 2021. The search includes all subsequent pages with links from the homepage containing the company domain name.
We filter the resulting textual data to only include English text utilizing the FastText language detection API [(Joulin et al., 2016)](https://aclanthology.org/E17-2068/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- en
## Dataset Structure
### Data Instances
- **#Instances:** 1789413
- **#Companies:** 393542
- **#Timeline:** 2014-2021
### Data Fields
- `id`: instance identifier `(string)`
- `cid`: company identifier `(string)`
- `text`: website text `(string)`
- `sic4`: 4-digit SIC `(string)`
### 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
```bibtex
@misc{title_year,
title={TITLE},
author={AUTHORS},
year={2022},
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
batchku | null | null | null | false | null | false | batchku/echo | 2022-10-14T17:27:07.000Z | null | false | 4b964f60f7265990c1b72454e48305e460135281 | [] | [] | https://huggingface.co/datasets/batchku/echo/resolve/main/README.md | A few images of Echo |
51la5 | null | null | null | false | 3 | false | 51la5/standup_test | 2022-10-15T09:01:17.000Z | null | false | 88b6656ae0808b0eb698448a0a88e290f71bd0cd | [] | [] | https://huggingface.co/datasets/51la5/standup_test/resolve/main/README.md | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
0: O
1: B-PER
2: I-PER
3: B-ORG
4: I-ORG
5: B-LOC
6: I-LOC
7: B-MISC
8: I-MISC
splits:
- name: train
num_bytes: 30429
num_examples: 147
download_size: 8067
dataset_size: 30429
---
# Dataset Card for "standup_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
facebook | null | null | null | false | 92 | false | facebook/content_rephrasing | 2022-10-14T17:41:05.000Z | null | false | 80e34a787a6c757d2e9cad051ac26c3353b70225 | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/facebook/content_rephrasing/resolve/main/README.md | ---
license: cc-by-sa-4.0
---
## Message Content Rephrasing Dataset
Introduced by Einolghozati et al. in Sound Natural: Content Rephrasing in Dialog Systems https://aclanthology.org/2020.emnlp-main.414/
We introduce a new task of rephrasing for amore natural virtual assistant. Currently, vir-tual assistants work in the paradigm of intent-slot tagging and the slot values are directlypassed as-is to the execution engine. However,this setup fails in some scenarios such as mes-saging when the query given by the user needsto be changed before repeating it or sending itto another user. For example, for queries like‘ask my wife if she can pick up the kids’ or ‘re-mind me to take my pills’, we need to rephrasethe content to ‘can you pick up the kids’ and‘take your pills’. In this paper, we study theproblem of rephrasing with messaging as ause case and release a dataset of 3000 pairs oforiginal query and rephrased query. We showthat BART, a pre-trained transformers-basedmasked language model with auto-regressivedecoding, is a strong baseline for the task, andshow improvements by adding a copy-pointerand copy loss to it. We analyze different trade-offs of BART-based and LSTM-based seq2seqmodels, and propose a distilled LSTM-basedseq2seq as the best practical model.
|
arbml | null | null | null | false | null | false | arbml/Quran_Hadith | 2022-10-14T17:45:37.000Z | null | false | d114b6fff871e11d1bb5835432f461cd3148e452 | [] | [] | https://huggingface.co/datasets/arbml/Quran_Hadith/resolve/main/README.md | ---
dataset_info:
features:
- name: SS
dtype: string
- name: SV
dtype: string
- name: Verse1
dtype: string
- name: TS
dtype: string
- name: TV
dtype: string
- name: Verse2
dtype: string
- name: Label
dtype: string
splits:
- name: train
num_bytes: 7351452
num_examples: 8144
download_size: 2850963
dataset_size: 7351452
---
# Dataset Card for "Quran_Hadith"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
eXR3d | null | null | null | false | null | false | eXR3d/Ko-byung-jun | 2022-10-14T18:48:27.000Z | null | false | ef22943f8b32551ca304ac81060f6cd0dda112c3 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/eXR3d/Ko-byung-jun/resolve/main/README.md | ---
license: unknown
---
|
prajjwal1 | null | \ | Discosense | false | 1 | false | prajjwal1/discosense | 2022-11-11T00:40:03.000Z | null | false | 9316aabaffb446d1c327e4a88edbd4790eada3aa | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/prajjwal1/discosense/resolve/main/README.md | ---
license: apache-2.0
---
|
arbml | null | null | null | false | null | false | arbml/AlRiyadh_Newspaper_Covid | 2022-10-14T19:20:34.000Z | null | false | c3f6bd8acd77dc0d3f4e8df3961f2f82aedbb7d2 | [] | [] | https://huggingface.co/datasets/arbml/AlRiyadh_Newspaper_Covid/resolve/main/README.md | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: string
- name: ID
dtype: string
- name: Category
dtype: string
- name: Source
dtype: string
- name: Title
dtype: string
- name: Subtitle
dtype: string
- name: Image
dtype: string
- name: Caption
dtype: string
- name: Text
dtype: string
- name: URL
dtype: string
- name: FullText
dtype: string
- name: FullTextCleaned
dtype: string
- name: FullTextWords
dtype: string
- name: WordsCounts
dtype: string
- name: Date
dtype: string
- name: Time
dtype: string
- name: Images
dtype: string
- name: Captions
dtype: string
- name: Terms
dtype: string
splits:
- name: train
num_bytes: 376546224
num_examples: 24084
download_size: 164286254
dataset_size: 376546224
---
# Dataset Card for "AlRiyadh_Newspaper_Covid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
daparasyte | null | null | null | false | 3 | false | daparasyte/SQuAD_Hindi | 2022-10-16T06:18:33.000Z | null | false | 3ded52588975a96bbce202da4cdf605278e88274 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/daparasyte/SQuAD_Hindi/resolve/main/README.md | ---
license: unknown
---
This dataset is created by translating a part of the Stanford QA dataset.
It contains 5k QA pairs from the original SQuad dataset translated to Hindi using the googletrans api. |
rick012 | null | null | null | false | null | false | rick012/celeb-identities | 2022-10-14T19:48:57.000Z | null | false | c2c253732cadc497dd41ab0029779f7735060e52 | [] | [] | https://huggingface.co/datasets/rick012/celeb-identities/resolve/main/README.md | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: Cristiano_Ronaldo
1: Jay_Z
2: Nicki_Minaj
3: Peter_Obi
4: Roger_Federer
5: Serena_Williams
splits:
- name: train
num_bytes: 195536.0
num_examples: 18
download_size: 193243
dataset_size: 195536.0
---
# Dataset Card for "celeb-identities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml | null | null | null | false | null | false | arbml/BRAD | 2022-10-14T19:38:36.000Z | null | false | e56902acc46a67a5f18623dd73a38d6685672a3f | [] | [] | https://huggingface.co/datasets/arbml/BRAD/resolve/main/README.md | ---
dataset_info:
features:
- name: review_id
dtype: string
- name: book_id
dtype: string
- name: user_id
dtype: string
- name: review
dtype: string
- name: label
dtype:
class_label:
names:
0: 1
1: 2
2: 3
3: 4
4: 5
splits:
- name: train
num_bytes: 407433642
num_examples: 510598
download_size: 211213150
dataset_size: 407433642
---
# Dataset Card for "BRAD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
spktsagar | null | @inproceedings{kjartansson-etal-sltu2018,
title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}},
author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
year = {2018},
address = {Gurugram, India},
month = aug,
pages = {52--55},
URL = {http://dx.doi.org/10.21437/SLTU.2018-11}
} | This data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.
The data set has been manually quality checked, but there might still be errors.
The audio files are sampled at rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection. | false | 58 | false | spktsagar/openslr-nepali-asr-cleaned | 2022-10-23T18:15:15.000Z | null | false | 4b2ea7773f47fa46fef6408a38620fd08d19e055 | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/README.md | ---
license: cc-by-sa-4.0
dataset_info:
- config_name: original
features:
- name: utterance_id
dtype: string
- name: speaker_id
dtype: string
- name: utterance
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: num_frames
dtype: int32
splits:
- name: train
num_bytes: 40925646
num_examples: 157905
download_size: 9340083067
dataset_size: 40925646
- config_name: cleaned
features:
- name: utterance_id
dtype: string
- name: speaker_id
dtype: string
- name: utterance
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: num_frames
dtype: int32
splits:
- name: train
num_bytes: 40925646
num_examples: 157905
download_size: 5978669282
dataset_size: 40925646
---
# Dataset Card for OpenSLR Nepali Large ASR Cleaned
## Table of Contents
- [Dataset Card for OpenSLR Nepali Large ASR Cleaned](#dataset-card-for-openslr-nepali-large-asr-cleaned)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [How to use?](#how-to-use)
- [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 Description
- **Homepage:** [Original OpenSLR Large Nepali ASR Dataset link](https://www.openslr.org/54/)
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Sagar Sapkota](mailto:spkt.sagar@gmail.com)
### Dataset Summary
This data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.
The data set has been manually quality-checked, but there might still be errors.
The audio files are sampled at a rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection.
For your reference, following was the function applied on each of the original openslr utterances.
```python
import torchaudio
SAMPLING_RATE = 16000
def process_audio_file(orig_path, new_path):
"""Read and process file in `orig_path` and save it to `new_path`"""
waveform, sampling_rate = torchaudio.load(orig_path)
if sampling_rate != SAMPLING_RATE:
waveform = torchaudio.functional.resample(waveform, sampling_rate, SAMPLING_RATE)
# trim end silences with Voice Activity Detection
waveform = torchaudio.functional.vad(waveform, sample_rate=SAMPLING_RATE)
torchaudio.save(new_path, waveform, sample_rate=SAMPLING_RATE)
```
### How to use?
There are two configurations for the data: one to download the original data and the other to download the preprocessed data as described above.
1. First, to download the original dataset with HuggingFace's [Dataset](https://huggingface.co/docs/datasets/) API:
```python
from datasets import load_dataset
dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="original", split='train')
```
2. To download the preprocessed dataset:
```python
from datasets import load_dataset
dataset = load_dataset("spktsagar/openslr-nepali-asr-cleaned", name="cleaned", split='train')
```
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition.
### Languages
Nepali
## Dataset Structure
### Data Instances
```js
{
'utterance_id': 'e1c4d414df',
'speaker_id': '09da0',
'utterance': {
'path': '/root/.cache/huggingface/datasets/downloads/extracted/e3cf9a618900289ecfd4a65356633d7438317f71c500cbed122960ab908e1e8a/cleaned/asr_nepali/data/e1/e1c4d414df.flac',
'array': array([-0.00192261, -0.00204468, -0.00158691, ..., 0.00323486, 0.00256348, 0.00262451], dtype=float32),
'sampling_rate': 16000
},
'transcription': '२००५ मा बिते',
'num_frames': 42300
}
```
### Data Fields
- utterance_id: a string identifying the utterances
- speaker_id: obfuscated unique id of the speaker whose utterances is in the current instance
- utterance:
- path: path to the utterance .flac file
- array: numpy array of the utterance
- sampling_rate: sample rate of the utterance
- transcription: Nepali text which spoken in the utterance
- num_frames: length of waveform array
### Data Splits
The dataset is not split. The consumer should split it as per their requirements. |
Ariela | null | null | null | false | 1 | false | Ariela/muneca-papel | 2022-10-15T19:56:12.000Z | null | false | da93d7ca5f81aaae854ade8bcaf8147a6d0a0cb5 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/Ariela/muneca-papel/resolve/main/README.md | ---
license: unknown
---
from datasets import load_dataset
dataset = load_dataset("Ariela/muneca-papel")
|
arbml | null | null | null | false | null | false | arbml/OSACT4_hatespeech | 2022-10-14T19:48:40.000Z | null | false | c4a17a7a5dbacb594c23e8ff0aafca7250121013 | [] | [] | https://huggingface.co/datasets/arbml/OSACT4_hatespeech/resolve/main/README.md | ---
dataset_info:
features:
- name: tweet
dtype: string
- name: offensive
dtype: string
- name: hate
dtype: string
splits:
- name: train
num_bytes: 1417732
num_examples: 6838
- name: validation
num_bytes: 204725
num_examples: 999
download_size: 802812
dataset_size: 1622457
---
# Dataset Card for "OSACT4_hatespeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml | null | null | null | false | null | false | arbml/Sentiment_Lexicons | 2022-10-14T19:57:04.000Z | null | false | 37c7175b2b6f07d4c749f7390ce9784e999aa1d5 | [] | [] | https://huggingface.co/datasets/arbml/Sentiment_Lexicons/resolve/main/README.md | ---
dataset_info:
features:
- name: Term
dtype: string
- name: bulkwalter
dtype: string
- name: sentiment_score
dtype: string
- name: positive_occurrence_count
dtype: string
- name: negative_occurrence_count
dtype: string
splits:
- name: train
num_bytes: 2039703
num_examples: 43308
download_size: 1068103
dataset_size: 2039703
---
# Dataset Card for "Sentiment_Lexicons"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tiagoblima | null | null | null | false | 33 | false | tiagoblima/nilc-school-books | 2022-11-13T01:03:20.000Z | null | false | e43dbe88d29779bc0440e214fc4de451d22392bc | [] | [
"license:mit"
] | https://huggingface.co/datasets/tiagoblima/nilc-school-books/resolve/main/README.md | ---
license: mit
dataset_info:
features:
- name: text_id
dtype: int64
- name: text
dtype: string
- name: level
dtype: string
splits:
- name: test
num_bytes: 1276559.048483246
num_examples: 8321
- name: train
num_bytes: 4595060.28364021
num_examples: 29952
- name: validation
num_bytes: 510715.6678765444
num_examples: 3329
download_size: 3645953
dataset_size: 6382335.0
---
## Córpus de Complexidade Textual para Estágios Escolares do Sistema Educacional Brasileiro
O córpus inclui trechos de: livros-textos cuja lista completa é apresentada abaixo, notícias da Seção Para Seu Filho Ler (PSFL) do jornal Zero Hora que apresenta algumas notícias sobre o mesmo córpus do jornal do Zero Hora, mas escritas para crianças de 8 a 11 anos de idade , Exames do SAEB , Livros Digitais do Wikilivros em Português, Exames do Enem dos anos 2015, 2016 e 2017. Todo o material em português foi disponibilizado para avaliar a tarefa de complexidade textual (readability).
Lista completa dos Livros Didáticos e suas fontes originais
Esse corpus faz parte dos recursos de meu doutorado na área de Natural Language Processing, sendo realizado no Núcleo Interinstitucional de Linguística Computacional da USP de São Carlos. Esse trabalho foi orientado pela Profa. Sandra Maria Aluísio.
http://nilc.icmc.usp.br
@inproceedings{mgazzola19,
title={Predição da Complexidade Textual de Recursos Educacionais Abertos em Português},
author={Murilo Gazzola, Sidney Evaldo Leal, Sandra Maria Aluisio},
booktitle={Proceedings of the Brazilian Symposium in Information and Human Language Technology},
year={2019}
} |
arbml | null | null | null | false | null | false | arbml/Commonsense_Validation | 2022-10-14T21:52:21.000Z | null | false | c2f48f68766a519e06a81cbc405d36dd4762d785 | [] | [] | https://huggingface.co/datasets/arbml/Commonsense_Validation/resolve/main/README.md | ---
dataset_info:
features:
- name: id
dtype: string
- name: first_sentence
dtype: string
- name: second_sentence
dtype: string
- name: label
dtype:
class_label:
names:
0: 0
1: 1
splits:
- name: train
num_bytes: 1420233
num_examples: 10000
- name: validation
num_bytes: 133986
num_examples: 1000
download_size: 837486
dataset_size: 1554219
---
# Dataset Card for "Commonsense_Validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml | null | null | null | false | null | false | arbml/arastance | 2022-10-14T22:14:25.000Z | null | false | fed92167f9ae45fac1207017212a0c5bc6da02cd | [] | [] | https://huggingface.co/datasets/arbml/arastance/resolve/main/README.md | ---
dataset_info:
features:
- name: filename
dtype: string
- name: claim
dtype: string
- name: claim_url
dtype: string
- name: article
dtype: string
- name: stance
dtype:
class_label:
names:
0: Discuss
1: Disagree
2: Unrelated
3: Agree
- name: article_title
dtype: string
- name: article_url
dtype: string
splits:
- name: test
num_bytes: 5611165
num_examples: 646
- name: train
num_bytes: 29682402
num_examples: 2848
- name: validation
num_bytes: 7080226
num_examples: 569
download_size: 18033579
dataset_size: 42373793
---
# Dataset Card for "arastance"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml | null | null | null | false | null | false | arbml/TUNIZI | 2022-10-14T22:28:45.000Z | null | false | f89f0029a9dd992ff5e43eadde0ac821406d9cbe | [] | [] | https://huggingface.co/datasets/arbml/TUNIZI/resolve/main/README.md | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
0: negative
1: positive
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 188084
num_examples: 2997
download_size: 127565
dataset_size: 188084
---
# Dataset Card for "TUNIZI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml | null | null | null | false | 5 | false | arbml/AQAD | 2022-10-14T22:35:38.000Z | null | false | d25e904472d19ac8cb639bff14cd59f31a90991b | [] | [] | https://huggingface.co/datasets/arbml/AQAD/resolve/main/README.md | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 23343014
num_examples: 17911
download_size: 3581662
dataset_size: 23343014
---
# Dataset Card for "AQAD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml | null | null | null | false | null | false | arbml/MArSum | 2022-10-14T22:42:35.000Z | null | false | e9674e9345c66631d1cd1f89ca1f00d8ae119c4f | [] | [] | https://huggingface.co/datasets/arbml/MArSum/resolve/main/README.md | ---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
splits:
- name: test
num_bytes: 3332778
num_examples: 1981
download_size: 1743254
dataset_size: 3332778
---
# Dataset Card for "MArSum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
omerist | null | null | null | false | 1 | false | omerist/arabicReviews-ds-mini | 2022-10-14T23:53:38.000Z | null | false | d337fbd0337b6eda3282433826f037770ee94f69 | [] | [] | https://huggingface.co/datasets/omerist/arabicReviews-ds-mini/resolve/main/README.md | ---
dataset_info:
features:
- name: source
dtype: string
- name: title
dtype: string
- name: content
dtype: string
- name: content_length
dtype: int64
splits:
- name: train
num_bytes: 11505614.4
num_examples: 3600
- name: validation
num_bytes: 1278401.6
num_examples: 400
download_size: 6325726
dataset_size: 12784016.0
---
# Dataset Card for "arabicReviews-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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