datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
leemeng/jcommonsenseqa-v1.1
|
leemeng
| 2023-04-28T08:13:50Z | 43 | 2 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-04-28T07:50:46Z |
---
license: cc-by-4.0
dataset_info:
features:
- name: q_id
dtype: int64
- name: question
dtype: string
- name: choice0
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: choice3
dtype: string
- name: choice4
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1183829
num_examples: 8939
- name: validation
num_bytes: 148293
num_examples: 1119
download_size: 887894
dataset_size: 1332122
---
|
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Fredithefish/Instruction-Tuning-with-GPT-4-RedPajama-Chat
|
Fredithefish
| 2023-05-17T11:31:57Z | 43 | 1 |
[
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] |
[
"question-answering"
] | 2023-05-16T14:12:28Z |
---
license: cc
task_categories:
- question-answering
language:
- en
---
# Instruction Tuning with GPT 4 RedPajama-Chat
This dataset has been converted from the <a href="https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM" target="_new">Instruction-Tuning-with-GPT-4</a> dataset for the purpose of fine-tuning the <a href="https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1" target="_new">RedPajama-INCITE-Chat-3B-v1</a> model.
## About Instruction-Tuning-with-GPT-4
English Instruction-Following Data generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.
### Usage and License Notices
The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
|
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] |
mvasiliniuc/iva-kotlin-codeint-clean-valid
|
mvasiliniuc
| 2023-06-15T14:49:59Z | 43 | 0 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:10K<n<100K",
"language:code",
"license:other",
"code, kotlin, native Android development, curated, validation",
"region:us"
] |
[
"text-generation"
] | 2023-06-03T14:11:04Z |
---
annotations_creators:
- crowdsourced
license: other
language_creators:
- crowdsourced
language:
- code
task_categories:
- text-generation
tags:
- code, kotlin, native Android development, curated, validation
size_categories:
- 10K<n<100K
source_datasets: []
pretty_name: iva-kotlin-codeint-clean-valid
task_ids:
- language-modeling
---
# IVA Kotlin GitHub Code Dataset - Curated - Validation
## Dataset Description
This is the curated valid split of IVA Kotlin dataset extracted from GitHub.
It contains curated Kotlin files gathered with the purpose to train & validate a code generation model.
The dataset only contains a valid split.
For validation and unspliced versions, please check the following links:
* Clean Version Unsliced: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean
* Clean Version Train: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean-train
Information about dataset structure, data involved, licenses and standard Dataset Card information is available that applies to this dataset also.
# Considerations for Using the Data
The dataset comprises source code from various repositories, potentially containing harmful or biased code,
along with sensitive information such as passwords or usernames.
|
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] |
clarin-knext/dbpedia-pl
|
clarin-knext
| 2023-06-07T08:12:53Z | 43 | 2 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:28:09Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
dmayhem93/agieval-logiqa-en
|
dmayhem93
| 2023-06-18T17:28:42Z | 43 | 0 |
[
"license:cc-by-nc-sa-4.0",
"arxiv:2304.06364",
"region:us"
] | null | 2023-06-18T12:49:02Z |
---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 852087
num_examples: 651
download_size: 420337
dataset_size: 852087
license: cc-by-nc-sa-4.0
---
# Dataset Card for "agieval-logiqa-en"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
Raw datset: https://github.com/lgw863/LogiQA-dataset
[Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{Liu2020LogiQAAC,
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
booktitle={International Joint Conference on Artificial Intelligence},
year={2020}
}
|
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] |
llm-book/jsnli
|
llm-book
| 2023-10-25T15:22:46Z | 43 | 0 |
[
"size_categories:100K<n<1M",
"language:ja",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-06-19T12:31:46Z |
---
language:
- ja
size_categories:
- 100K<n<1M
license:
- cc-by-sa-4.0
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 97491392
num_examples: 533005
- name: validation
num_bytes: 712792
num_examples: 3916
download_size: 44931163
dataset_size: 98204184
---
# Dataset Card for llm-book/jsnli
書籍『大規模言語モデル入門』で使用する [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?日本語SNLI(JSNLI)データセット) のデータセットです。
JSNLI Version 1.1 のデータセットのうち、フィルタリング後の訓練セット (train_w_filtering) と検証セット (dev) を使用しています。
## Licence
CC BY-SA 4.0
|
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] |
rdpahalavan/network-packet-flow-header-payload
|
rdpahalavan
| 2023-07-22T21:40:27Z | 43 | 3 |
[
"task_categories:text-classification",
"size_categories:1M<n<10M",
"license:apache-2.0",
"Network Intrusion Detection",
"Cybersecurity",
"Network Packets",
"region:us"
] |
[
"text-classification"
] | 2023-07-01T12:20:03Z |
---
license: apache-2.0
task_categories:
- text-classification
tags:
- Network Intrusion Detection
- Cybersecurity
- Network Packets
size_categories:
- 1M<n<10M
---
Each row contains the information of a network packet and its label. The format is given below:
|
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BELLE-2/train_3.5M_CN_With_Category
|
BELLE-2
| 2023-10-18T03:19:58Z | 43 | 9 |
[
"task_categories:text2text-generation",
"size_categories:1M<n<10M",
"language:zh",
"license:gpl-3.0",
"region:us"
] |
[
"text2text-generation"
] | 2023-08-14T03:46:04Z |
---
license: gpl-3.0
task_categories:
- text2text-generation
language:
- zh
size_categories:
- 1M<n<10M
---
## 内容
基于原有的[train_3.5M_CN](https://huggingface.co/datasets/BelleGroup/train_3.5M_CN)数据新增了指令类别字段，共包括13个类别，详情如下图所示：
## 样例
```
{
"id":"66182880",
"category"："generation"
}
```
### 字段：
```
id: 数据id
category: 该条指令数据对应的类别
```
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
## Citation
Please cite our paper and github when using our code, data or model.
```
@misc{BELLE,
author = {BELLEGroup},
title = {BELLE: Be Everyone's Large Language model Engine},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
}
```
|
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] |
vwxyzjn/summarize_from_feedback_tldr_3_filtered
|
vwxyzjn
| 2023-09-19T20:10:04Z | 43 | 1 |
[
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
[
"summarization"
] | 2023-09-19T20:07:59Z |
---
license: mit
task_categories:
- summarization
language:
- en
size_categories:
- 1K<n<10K
---
This is the query dataset taken directly from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset
|
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mindchain/wikitext2
|
mindchain
| 2023-09-26T19:13:55Z | 43 | 0 |
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"license:gfdl",
"arxiv:1609.07843",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2023-09-26T19:13:23Z |
---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- monolingual
paperswithcode_id: wikitext-2
pretty_name: WikiText
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
- config_name: wikitext-103-v1
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1295579
num_examples: 4358
- name: train
num_bytes: 545142639
num_examples: 1801350
- name: validation
num_bytes: 1154755
num_examples: 3760
download_size: 190229076
dataset_size: 547592973
- config_name: wikitext-2-v1
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1270951
num_examples: 4358
- name: train
num_bytes: 10918134
num_examples: 36718
- name: validation
num_bytes: 1134127
num_examples: 3760
download_size: 4475746
dataset_size: 13323212
- config_name: wikitext-103-raw-v1
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1305092
num_examples: 4358
- name: train
num_bytes: 546501673
num_examples: 1801350
- name: validation
num_bytes: 1159292
num_examples: 3760
download_size: 191984949
dataset_size: 548966057
- config_name: wikitext-2-raw-v1
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1305092
num_examples: 4358
- name: train
num_bytes: 11061733
num_examples: 36718
- name: validation
num_bytes: 1159292
num_examples: 3760
download_size: 4721645
dataset_size: 13526117
---
# Dataset Card for "wikitext"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843)
- **Point of Contact:** [Stephen Merity](mailto:[email protected])
- **Size of downloaded dataset files:** 391.41 MB
- **Size of the generated dataset:** 1.12 GB
- **Total amount of disk used:** 1.52 GB
### Dataset Summary
The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over
110 times larger. The WikiText dataset also features a far larger vocabulary and retains the original case, punctuation
and numbers - all of which are removed in PTB. As it is composed of full articles, the dataset is well suited for models
that can take advantage of long term dependencies.
Each subset comes in two different variants:
- Raw (for character level work) contain the raw tokens, before the addition of the <unk> (unknown) tokens.
- Non-raw (for word level work) contain only the tokens in their vocabulary (wiki.train.tokens, wiki.valid.tokens, and wiki.test.tokens).
The out-of-vocabulary tokens have been replaced with the the <unk> token.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### wikitext-103-raw-v1
- **Size of downloaded dataset files:** 191.98 MB
- **Size of the generated dataset:** 549.42 MB
- **Total amount of disk used:** 741.41 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"text": "\" The gold dollar or gold one @-@ dollar piece was a coin struck as a regular issue by the United States Bureau of the Mint from..."
}
```
#### wikitext-103-v1
- **Size of downloaded dataset files:** 190.23 MB
- **Size of the generated dataset:** 548.05 MB
- **Total amount of disk used:** 738.27 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..."
}
```
#### wikitext-2-raw-v1
- **Size of downloaded dataset files:** 4.72 MB
- **Size of the generated dataset:** 13.54 MB
- **Total amount of disk used:** 18.26 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "\" The Sinclair Scientific Programmable was introduced in 1975 , with the same case as the Sinclair Oxford . It was larger than t..."
}
```
#### wikitext-2-v1
- **Size of downloaded dataset files:** 4.48 MB
- **Size of the generated dataset:** 13.34 MB
- **Total amount of disk used:** 17.82 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..."
}
```
### Data Fields
The data fields are the same among all splits.
#### wikitext-103-raw-v1
- `text`: a `string` feature.
#### wikitext-103-v1
- `text`: a `string` feature.
#### wikitext-2-raw-v1
- `text`: a `string` feature.
#### wikitext-2-v1
- `text`: a `string` feature.
### Data Splits
| name | train |validation|test|
|-------------------|------:|---------:|---:|
|wikitext-103-raw-v1|1801350| 3760|4358|
|wikitext-103-v1 |1801350| 3760|4358|
|wikitext-2-raw-v1 | 36718| 3760|4358|
|wikitext-2-v1 | 36718| 3760|4358|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is available under the [Creative Commons Attribution-ShareAlike License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@misc{merity2016pointer,
title={Pointer Sentinel Mixture Models},
author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
year={2016},
eprint={1609.07843},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
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] |
promptora11/finetune
|
promptora11
| 2023-10-09T12:38:42Z | 43 | 0 |
[
"region:us"
] | null | 2023-10-09T12:33:06Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 74044
num_examples: 100
download_size: 7883
dataset_size: 74044
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Nbardy/renamed_waves
|
Nbardy
| 2023-10-19T19:27:20Z | 43 | 0 |
[
"region:us"
] | null | 2023-10-19T19:21:43Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 498961211.25
num_examples: 1306
download_size: 497509644
dataset_size: 498961211.25
---
# Dataset Card for "renamed_waves"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
bragovo/dsum_en
|
bragovo
| 2023-11-05T18:57:50Z | 43 | 0 |
[
"language:en",
"region:us"
] | null | 2023-11-04T07:28:09Z |
---
configs:
- config_name: default
task: summarization
data_files:
- split: train
path: data/train-*
language:
- en
---
|
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] |
huuuyeah/meetingbank
|
huuuyeah
| 2023-11-10T04:52:54Z | 43 | 1 |
[
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-nc-sa-4.0",
"municipal",
"meeting",
"transcripts",
"benchmark",
"long-context",
"arxiv:2305.17529",
"region:us"
] |
[
"summarization",
"text-generation"
] | 2023-11-10T04:02:31Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- summarization
- text-generation
language:
- en
tags:
- municipal
- meeting
- transcripts
- benchmark
- long-context
size_categories:
- 10M<n<100M
---
## Overview
MeetingBank, a benchmark dataset created from the city councils of 6 major U.S. cities to supplement existing datasets. It contains 1,366 meetings with over 3,579 hours of video, as well as transcripts, PDF documents of meeting minutes, agenda, and other metadata. On average, a council meeting is 2.6 hours long and its transcript contains over 28k tokens, making it a valuable testbed for meeting summarizers and for extracting structure from meeting videos. The datasets contains 6,892 segment-level summarization instances for training and evaluating of performance.
## Data Structure
```json
{
"id": 0,
"uid": "SeattleCityCouncil_06132016_Res 31669",
"summary": "A RESOLUTION encouraging as a best practice ...",
"transcript": "The report of the Civil Rights, Utilities, Economic ..."
}
```
## Usage
```python
from datasets import load_dataset
meetingbank = load_dataset("huuuyeah/meetingbank")
train_data = meetingbank['train']
test_data = meetingbank['test']
val_data = meetingbank['validation']
def generator(data_split):
for instance in data_split:
yiled instance['id'], instance['summary'], instance['transcript']
```
## Acknowledgement
Please cite the following paper in work that makes use of this dataset:
[MeetingBank: A Benchmark Dataset for Meeting Summarization](https://arxiv.org/abs/2305.17529)\
Yebowen Hu, Tim Ganter, Hanieh Deilamsalehy, Franck Dernoncourt, Hassan Foroosh, Fei Liu\
In main conference of Association for Computational Linguistics (ACL'23), Toronto, Canada.
## Bibtex
```
@inproceedings{hu-etal-2023-meetingbank,
title = "MeetingBank: A Benchmark Dataset for Meeting Summarization",
author = "Yebowen Hu and Tim Ganter and Hanieh Deilamsalehy and Franck Dernoncourt and Hassan Foroosh and Fei Liu",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)",
month = July,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
}
```
## Multi-media Resources
MeetingBank dataset will be hosted at Zenodo. The audio files of each meeting will be hosted individually on Huggingface. All resources will includes meeting audio, transcripts, meetingbank main JSON file, summaries from 6 systems and human annotations.
**Text & Audio**: [zenodo](https://zenodo.org/record/7989108), Huggingface([splits](https://huggingface.co/datasets/huuuyeah/meetingbank), [audio&transcripts](https://huggingface.co/datasets/huuuyeah/MeetingBank_Audio))
**Videos**: All meeting videos can be found in https://archive.org/
- [Alameda](https://archive.org/details/meetingbank-alameda), [Boston](https://archive.org/details/meetingbank-boston), [Denver](https://archive.org/details/meetingbank-denver), [Long Beach](https://archive.org/details/meetingbank-long-beach) ,[King County](https://archive.org/details/meetingbank-king-county), [Seattle](https://archive.org/details/meetingbank-seattle)
**Python Scripts**
Useful scripts and guidance can be found in github repo [MeetingBank_Utils](https://github.com/YebowenHu/MeetingBank-utils)
|
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danielz01/BigEarthNet-S2-v1.0
|
danielz01
| 2023-11-13T09:11:20Z | 43 | 0 |
[
"region:us"
] | null | 2023-11-12T20:17:15Z |
---
configs:
- config_name: s2-rgb
data_files:
- split: test
path: s2-rgb/test-*
- split: val
path: s2-rgb/val-*
- split: train
path: s2-rgb/train-*
dataset_info:
config_name: s2-rgb
features:
- name: img
dtype: image
- name: labels
sequence: string
- name: coordinates
struct:
- name: lrx
dtype: int64
- name: lry
dtype: int64
- name: ulx
dtype: int64
- name: uly
dtype: int64
- name: projection
dtype: string
- name: tile_source
dtype: string
- name: acquisition_date
dtype: string
splits:
- name: test
num_bytes: 3453114936.75
num_examples: 125866
- name: val
num_bytes: 3393628600.625
num_examples: 123723
- name: train
num_bytes: 7391482704.125
num_examples: 269695
download_size: 13839792533
dataset_size: 14238226241.5
---
# Dataset Card for "BigEarthNet-S2-v1.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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EP45/test
|
EP45
| 2023-12-02T09:21:32Z | 43 | 0 |
[
"region:us"
] | null | 2023-11-20T05:28:11Z |
---
configs:
- config_name: default
data_files:
- split: train
path: "data/train_15k.parquet"
- split: test
path: "data/test.parquet"
---
configs:
- config_name: 1k
data_files:
- split: train
path: "data/train_1k.parquet"
- split: test
path: "data/test.parquet"
- config_name: 5k
data_files:
- split: train
path: "data/train_5k.parquet"
- split: test
path: "data/test.parquet"
- config_name: 10k
data_files:
- split: train
path: "data/train_10k.parquet"
- split: test
path: "data/test.parquet"
- config_name: 15k
data_files:
- split: train
path: "data/train_15k.parquet"
- split: test
path: "data/test.parquet"
- config_name: 20k
data_files:
- split: train
path: "data/train_20k.parquet"
- split: test
path: "data/test.parquet"
- config_name: 30k
data_files:
- split: train
path: "data/train_30k.parquet"
- split: test
path: "data/test.parquet"
- config_name: 50k
data_files:
- split: train
path: "data/train_50k.parquet"
- split: test
path: "data/test.parquet"
# vicuna 실험용 데이터셋
다음 데이터셋으로부터 변환됨:
https://huggingface.co/datasets/junelee/sharegpt_deepl_ko
## 파일구조
- converted.parquet : 원본 데이터셋의 ko_alpaca_style_dataset.json을 트레이닝에 맞도록 형식 변환
## 라이센스
원본 데이터가 OPENAI 이기 때문에 해당 [약관](https://openai.com/policies/terms-of-use)에 따릅니다.
그 이외의 부분은 다음 라이센스를 따릅니다: 저작자표시 2.0 대한민국 (CC BY 2.0 KR)
|
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] |
jhan21/amazon-food-reviews-dataset
|
jhan21
| 2023-11-25T20:44:06Z | 43 | 0 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"amazon",
"reviews",
"food reviews",
"business",
"region:us"
] |
[
"text-classification"
] | 2023-11-22T20:14:11Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: amazon-food-reviews-dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- amazon
- reviews
- food reviews
- business
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for "Amazon Food Reviews"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.
### Supported Tasks and Leaderboards
This dataset can be used for numerous tasks like sentiment analysis, text classification, and user behavior analysis. It's particularly useful for training models to understand customer feedback and preferences.
### Languages
The reviews are primarily in English.
## Dataset Structure
### Data Instances
A typical data instance comprises a review with fields like product ID, user ID, rating, review text, helpfulness votes, and time of the review.
### Data Fields
- `ProductId`: Unique identifier for the product
- `UserId`: Unique identifier for the user
- `ProfileName`: Profile name of the user
- `HelpfulnessNumerator`: Number of users who found the review helpful
- `HelpfulnessDenominator`: Number of users who indicated whether they found the review helpful or not
- `Score`: Rating between 1 and 5
- `Time`: Timestamp of the review
- `Summary`: Brief summary of the review
- `Text`: Text of the review
### Data Splits
The dataset is not split into standard training/validation/testing sets. Users may need to create these splits as per their requirement.
## Dataset Creation
### Curation Rationale
The dataset was created to provide a large collection of textual reviews with sentiment labels, useful for tasks in sentiment analysis and natural language processing.
### Source Data
#### Initial Data Collection and Normalization
The data was collected from Amazon's food reviews section.
#### Who are the source language producers?
The source language producers are the Amazon users / customers who provided these reviews.
### Annotations
#### Annotation process
The reviews come with ratings that can be converted into sentiment labels, but no additional annotation process was described.
#### Who are the annotators?
The annotators are the Amazon users who left the reviews and ratings.
### Personal and Sensitive Information
The dataset contains user IDs and profile names which could potentially be used to identify the reviewers.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset provides insights into consumer preferences and sentiment, which can be valuable for businesses and researchers. However, care should be taken to ensure that models trained on this data do not reinforce stereotypes or biases present in the reviews.
### Discussion of Biases
The dataset may contain biases inherent in the user base of Amazon, which may not be representative of the general population.
### Other Known Limitations
The dataset's scope is limited to food products and may not generalize well to other types of products or reviews.
## Additional Information
### Dataset Curators
The dataset was originally curated by the SNAP group.
### Licensing Information
The dataset is available under a CC BY-SA 4.0 license.
### Citation Information
If you publish articles based on this dataset, please cite the following paper:
J. McAuley and J. Leskovec. _From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews_. WWW, 2013.
### Contributions
Thanks to [@Stanford Network Analysis Project](https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews/data) for adding this dataset.
|
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Suriyadeepan/journey-db-000
|
Suriyadeepan
| 2023-11-27T06:16:40Z | 43 | 0 |
[
"region:us"
] | null | 2023-11-27T06:05:58Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 15563670032.752
num_examples: 20949
download_size: 15371445769
dataset_size: 15563670032.752
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
laion/laion2B-multi
|
laion
| 2023-05-24T22:53:57Z | 42 | 33 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-09T02:48:39Z |
---
license: cc-by-4.0
---
|
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drAbreu/bc4chemd_ner
|
drAbreu
| 2022-10-25T10:02:51Z | 42 | 1 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:GitHub",
"language:en",
"license:unknown",
"region:us"
] |
[
"token-classification"
] | 2022-03-09T14:56:16Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- GitHub
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: bc4chemd
pretty_name: bc4chemd_ner
---
# Dataset Card for bc2gm_corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/)
- **Repository:** [Github](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD)
- **Paper:** [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331692/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
* Token Classification
* Named Entity Recognition
### Languages
- English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens.
### Data Splits
```python
DatasetDict({
train: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 30683
})
validation: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 30640
})
test: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 26365
})
})
```
## Dataset Creation
### Curation Rationale
The automatic extraction of chemical information from text requires the recognition of chemical
entity mentions as one of its key steps. When developing supervised named entity recognition
(NER) systems, the availability of a large, manually annotated text corpus is desirable.
Furthermore, large corpora permit the robust evaluation and comparison of different
approaches that detect chemicals in documents.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
### Annotations
#### Annotation process
We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a
total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators,
following annotation guidelines specifically defined for this task.
#### Who are the annotators?
Expert chemistry literature curators
### Personal and Sensitive Information
It does not contain this kind of information
The abstracts of the CHEMDNER corpus were selected to be representative for all
major chemical disciplines. Each of the chemical entity mentions was manually
labeled according to its structure-associated chemical entity mention (SACEM)
class: abbreviation, family, formula, identifier, multiple, systematic and
trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study
between annotators, obtaining a percentage agreement of 91.
### Licensing Information
Unknown
### Citation Information
```latex
@article{Krallinger2015TheCC,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Martin Krallinger and Obdulia Rabal and Florian Leitner and Miguel Vazquez and David Salgado and Zhiyong Lu and Robert Leaman and Yanan Lu and Dong-Hong Ji and Daniel M. Lowe and Roger A. Sayle and Riza Theresa Batista-Navarro and Rafal Rak and Torsten Huber and Tim Rockt{\"a}schel and S{\'e}rgio Matos and David Campos and Buzhou Tang and Hua Xu and Tsendsuren Munkhdalai and Keun Ho Ryu and S. V. Ramanan and P. Senthil Nathan and Slavko Zitnik and Marko Bajec and Lutz Weber and Matthias Irmer and Saber Ahmad Akhondi and Jan A. Kors and Shuo Xu and Xin An and Utpal Kumar Sikdar and Asif Ekbal and Masaharu Yoshioka and Thaer M. Dieb and Miji Choi and Karin M. Verspoor and Madian Khabsa and C. Lee Giles and Hongfang Liu and K. E. Ravikumar and Andre Lamurias and Francisco M. Couto and Hong-Jie Dai and Richard Tzong-Han Tsai and C Ata and Tolga Can and Anabel Usie and Rui Alves and Isabel Segura-Bedmar and Paloma Mart{\'i}nez and Julen Oyarz{\'a}bal and Alfonso Valencia},
journal={Journal of Cheminformatics},
year={2015},
volume={7},
pages={S2 - S2}
}
```
### Contributions
Thanks to [@GamalC](https://github.com/GamalC) for uploading this dataset to GitHub.
|
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] |
batterydata/battery-device-data-qa
|
batterydata
| 2023-11-06T12:50:19Z | 42 | 0 |
[
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"question-answering"
] | 2022-09-05T15:30:32Z |
---
language:
- en
license:
- apache-2.0
task_categories:
- question-answering
pretty_name: 'Battery Device Question Answering Dataset'
---
# Battery Device QA Data
Battery device records, including anode, cathode, and electrolyte.
Examples of the question answering evaluation dataset:
\{'question': 'What is the cathode?', 'answer': 'Al foil', 'context': 'The blended slurry was then cast onto a clean current collector (Al foil for the cathode and Cu foil for the anode) and dried at 90 °C under vacuum overnight.', 'start index': 645\}
\{'question': 'What is the anode?', 'answer': 'Cu foil', 'context': 'The blended slurry was then cast onto a clean current collector (Al foil for the cathode and Cu foil for the anode) and dried at 90 °C under vacuum overnight. Finally, the obtained electrodes were cut into desired shapes on demand. It should be noted that the electrode mass ratio of cathode/anode is set to about 4, thus achieving the battery balance.', 'start index': 673\}
\{'question': 'What is the cathode?', 'answer': 'SiC/RGO nanocomposite', 'context': 'In conclusion, the SiC/RGO nanocomposite, integrating the synergistic effect of SiC flakes and RGO, was synthesized by an in situ gas–solid fabrication method. Taking advantage of the enhanced photogenerated charge separation, large CO2 adsorption, and numerous exposed active sites, SiC/RGO nanocomposite served as the cathode material for the photo-assisted Li–CO2 battery.', 'start index': 284\}
# Usage
```
from datasets import load_dataset
dataset = load_dataset("batterydata/battery-device-data-qa")
```
Note: in the original BatteryBERT paper, 272 data records were used for evaluation after removing redundant records as well as paragraphs with character length >= 1500. Code is shown below:
```
import json
with open("answers.json", "r", encoding='utf-8') as f:
data = json.load(f)
evaluation = []
for point in data['data']:
paragraphs = point['paragraphs'][0]['context']
if len(paragraphs)<1500:
qas = point['paragraphs'][0]['qas']
for indiv in qas:
try:
question = indiv['question']
answer = indiv['answers'][0]['text']
pairs = (paragraphs, question, answer)
evaluation.append(pairs)
except:
continue
```
# Citation
```
@article{huang2022batterybert,
title={BatteryBERT: A Pretrained Language Model for Battery Database Enhancement},
author={Huang, Shu and Cole, Jacqueline M},
journal={J. Chem. Inf. Model.},
year={2022},
doi={10.1021/acs.jcim.2c00035},
url={DOI:10.1021/acs.jcim.2c00035},
pages={DOI: 10.1021/acs.jcim.2c00035},
publisher={ACS Publications}
}
```
|
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PlanTL-GOB-ES/CoNLL-NERC-es
|
PlanTL-GOB-ES
| 2022-11-18T11:55:41Z | 42 | 2 |
[
"task_categories:token-classification",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"language:es",
"region:us"
] |
[
"token-classification"
] | 2022-10-28T10:42:01Z |
---
YAML tags:
annotations_creators:
- expert-generated
language:
- es
language_creators:
- found
multilinguality:
- monolingual
pretty_name: CoNLL-NERC-es
size_categories: []
source_datasets: []
tags: []
task_categories:
- token-classification
task_ids:
- part-of-speech
---
# CoNLL-NERC-es
## 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
- **Website:** https://www.cs.upc.edu/~nlp/tools/nerc/nerc.html
- **Point of Contact:** [Xavier Carreras]([email protected])
### Dataset Summary
CoNLL-NERC is the Spanish dataset of the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf). The dataset is annotated with four types of named entities --persons, locations, organizations, and other miscellaneous entities-- formatted in the standard Beginning-Inside-Outside (BIO) format. The corpus consists of 8,324 train sentences with 19,400 named entities, 1,916 development sentences with 4,568 named entities, and 1,518 test sentences with 3,644 named entities.
We use this corpus as part of the EvalEs Spanish language benchmark.
### Supported Tasks and Leaderboards
Named Entity Recognition and Classification
### Languages
The dataset is in Spanish (`es-ES`)
## Dataset Structure
### Data Instances
<pre>
El DA O
Abogado NC B-PER
General AQ I-PER
del SP I-PER
Estado NC I-PER
, Fc O
Daryl VMI B-PER
Williams NC I-PER
, Fc O
subrayó VMI O
hoy RG O
la DA O
necesidad NC O
de SP O
tomar VMN O
medidas NC O
para SP O
proteger VMN O
al SP O
sistema NC O
judicial AQ O
australiano AQ O
frente RG O
a SP O
una DI O
página NC O
de SP O
internet NC O
que PR O
imposibilita VMI O
el DA O
cumplimiento NC O
de SP O
los DA O
principios NC O
básicos AQ O
de SP O
la DA O
Ley NC B-MISC
. Fp O
</pre>
### Data Fields
Every file has two columns, with the word form or punctuation symbol in the first one and the corresponding IOB tag in the second one. The different files are separated by an empty line.
### Data Splits
- esp.train: 273037 lines
- esp.testa: 54837 lines (used as dev)
- esp.testb: 53049 lines (used as test)
## Dataset Creation
### Curation Rationale
[N/A]
### Source Data
The data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000.
#### Initial Data Collection and Normalization
For more information visit the paper from the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf).
#### Who are the source language producers?
For more information visit the paper from the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf).
### Annotations
#### Annotation process
For more information visit the paper from the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf).
#### Who are the annotators?
The annotation was carried out by the TALP Research Center2 of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC3 ) of the University of Barcelona (UB), and funded by the European Commission through the NAMIC pro ject (IST-1999-12392).
For more information visit the paper from the CoNLL-2002 Shared Task [(Tjong Kim Sang, 2002)](https://aclanthology.org/W02-2024.pdf).
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contributes to the development of language models in Spanish.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset curators
### Licensing information
### Citation Information
The following paper must be cited when using this corpus:
Erik F. Tjong Kim Sang. 2002. Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition. In COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002).
### Contributions
[N/A]
|
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] |
bigbio/bioscope
|
bigbio
| 2022-12-22T15:44:13Z | 42 | 0 |
[
"multilinguality:monolingual",
"language:en",
"license:cc-by-2.0",
"region:us"
] | null | 2022-11-13T22:07:28Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-2.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_2p0
pretty_name: BioScope
homepage: https://rgai.inf.u-szeged.hu/node/105
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for BioScope
## Dataset Description
- **Homepage:** https://rgai.inf.u-szeged.hu/node/105
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
The BioScope corpus consists of medical and biological texts annotated for
negation, speculation and their linguistic scope. This was done to allow a
comparison between the development of systems for negation/hedge detection and
scope resolution. The BioScope corpus was annotated by two independent linguists
following the guidelines written by our linguist expert before the annotation of
the corpus was initiated.
## Citation Information
```
@article{vincze2008bioscope,
title={The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes},
author={Vincze, Veronika and Szarvas, Gy{"o}rgy and Farkas, Rich{'a}rd and M{'o}ra, Gy{"o}rgy and Csirik, J{'a}nos},
journal={BMC bioinformatics},
volume={9},
number={11},
pages={1--9},
year={2008},
publisher={BioMed Central}
}
```
|
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] |
jonathan-roberts1/Optimal-31
|
jonathan-roberts1
| 2023-03-31T17:06:29Z | 42 | 0 |
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
[
"image-classification",
"zero-shot-image-classification"
] | 2023-02-17T15:53:58Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': airport
'2': baseball diamond
'3': basketball court
'4': beach
'5': bridge
'6': chaparral
'7': church
'8': circular farmland
'9': commercial area
'10': dense residential
'11': desert
'12': forest
'13': freeway
'14': golf course
'15': ground track field
'16': harbor
'17': industrial area
'18': intersection
'19': island
'20': lake
'21': meadow
'22': medium residential
'23': mobile home park
'24': mountain
'25': overpass
'26': parking lot
'27': railway
'28': rectangular farmland
'29': roundabout
'30': runway
splits:
- name: train
num_bytes: 25100636.72
num_examples: 1860
download_size: 25105452
dataset_size: 25100636.72
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "Optimal-31"
## Dataset Description
- **Paper** [Scene classification with recurrent attention of VHR remote sensing images](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
### Licensing Information
[No license for now, cite the paper below.]
## Citation Information
[Scene classification with recurrent attention of VHR remote sensing images](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
```
@article{wang2018scene,
title = {Scene classification with recurrent attention of VHR remote sensing images},
author = {Wang, Qi and Liu, Shaoteng and Chanussot, Jocelyn and Li, Xuelong},
year = 2018,
journal = {IEEE Transactions on Geoscience and Remote Sensing},
publisher = {IEEE},
volume = 57,
number = 2,
pages = {1155--1167}
}
```
|
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rcds/occlusion_swiss_judgment_prediction
|
rcds
| 2023-03-28T08:19:29Z | 42 | 0 |
[
"task_categories:text-classification",
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|swiss_judgment_prediction",
"language:de",
"language:fr",
"language:it",
"language:en",
"license:cc-by-sa-4.0",
"explainability-judgment-prediction",
"occlusion",
"region:us"
] |
[
"text-classification",
"other"
] | 2023-03-08T20:14:10Z |
---
annotations_creators:
- expert-generated
language:
- de
- fr
- it
- en
language_creators:
- expert-generated
- found
license: cc-by-sa-4.0
multilinguality:
- multilingual
pretty_name: OcclusionSwissJudgmentPrediction
size_categories:
- 1K<n<10K
source_datasets:
- extended|swiss_judgment_prediction
tags:
- explainability-judgment-prediction
- occlusion
task_categories:
- text-classification
- other
task_ids: []
---
# Dataset Card for "OcclusionSwissJudgmentPrediction": An implementation of an occlusion based explainability method for Swiss judgment prediction
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Summary](#dataset-summary)
- [Documents](#documents)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset **str**ucture](#dataset-**str**ucture)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Summary
This dataset contains an implementation of occlusion for the SwissJudgmentPrediction task.
Note that this dataset only provides a test set and should be used in comination with the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
### Documents
Occlusion-Swiss-Judgment-Prediction is a subset of the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Occlusion-Swiss-Judgment-Prediction extends this dataset by adding sentence splitting with explainability labels.
### Supported Tasks and Leaderboards
OcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.
### Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
## Dataset structure
### Data Instances
## Data Instances
**Multilingual use of the dataset**
When the dataset is used in a multilingual setting selecting the the 'all_languages' flag:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/occlusion_swiss_judgment_prediction', 'all')
```
**Monolingual use of the dataset**
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/occlusion_swiss_judgment_prediction', 'de')
```
### Data Fields
The following data fields are provided for documents (Test_1/Test_2/Test_3/Test_4):
id: (**int**) a unique identifier of the for the document <br/>
year: (**int**) the publication year<br/>
label: (**str**) the judgment outcome: dismissal or approval<br/>
language: (**str**) one of (de, fr, it)<br/>
region: (**str**) the region of the lower court<br/>
canton: (**str**) the canton of the lower court<br/>
legal area: (**str**) the legal area of the case<br/>
explainability_label (**str**): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline<br/>
occluded_text (**str**): the occluded text<br/>
text: (**str**) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)<br/>
Note that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.
### Data Splits (Including Swiss Judgment Prediction)
Language | Subset | Number of Rows (Test_1/Test_2/Test_3/Test_4)
| ----------- | ----------- | ----------- |
German| de | __427__ / __1366__ / __3567__ / __7235__
French | fr | __307__ / __854__ / __1926__ / __3279__
Italian | it | __299__ /__919__ / __2493__ / __5733__
All | all | __1033__ / __3139__ / __7986__/ __16247__
Language | Subset | Number of Documents (is the same for Test_1/Test_2/Test_3/Test_4)
| ----------- | ----------- | ----------- |
German| de | __38__
French | fr | __36__
Italian | it | __34__
All | all | __108__
## Dataset Creation
### Curation Rationale
The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts, splitting sentences/group of sentences and annotated with one of the following explainability label: Supports judgment, Opposes Judgment and Neutral. The test sets have each sentence/ group of sentence once occluded, enabling an analysis of the changes in the model's performance. The legal expert annotation were conducted from April 2020 to August 2020.
#### Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Additional Information
### Dataset Curators
Niklaus et al. (2021) and Nina Baumgartner
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
```
@misc{baumgartner_nina_occlusion_2022,
title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
shorttitle = {From Occlusion to Transparancy},
abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
author = {{Baumgartner, Nina}},
year = {2022},
langid = {english}
}
```
### Contributions
Thanks to [@ninabaumgartner](https://github.com/ninabaumgartner) for adding this dataset.
|
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] |
rcds/lower_court_insertion_swiss_judgment_prediction
|
rcds
| 2023-03-28T08:19:04Z | 42 | 0 |
[
"task_categories:text-classification",
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|swiss_judgment_prediction",
"language:de",
"language:fr",
"language:it",
"language:en",
"license:cc-by-sa-4.0",
"explainability-judgment-prediction",
"region:us"
] |
[
"text-classification",
"other"
] | 2023-03-10T14:05:58Z |
---
annotations_creators:
- expert-generated
language:
- de
- fr
- it
- en
language_creators:
- expert-generated
- found
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
pretty_name: LowerCourtInsertionSwissJudgmentPrediction
size_categories:
- 1K<n<10K
source_datasets:
- extended|swiss_judgment_prediction
tags:
- explainability-judgment-prediction
task_categories:
- text-classification
- other
task_ids: []
---
# Dataset Card for "LowerCourtInsertionSwissJudgmentPrediction": An implementation of lower court insertion bias analysis for Swiss judgment prediction
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Summary](#dataset-summary)
- [Documents](#documents)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset **str**ucture](#dataset-**str**ucture)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Summary
This dataset contains an implementation of lower-court-insertion for the SwissJudgmentPrediction task.
Note that this dataset only provides a test set and should be used in comination with the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
### Documents
Lower-Court-Insertion-Swiss-Judgment-Prediction is a subset of the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Lower-Court-Insertion-Swiss-Judgment-Prediction extends this dataset by adding lower court insertion.
### Supported Tasks and Leaderboards
LowerCourtInsertionSwissJudgmentPrediction can be used for performing the LowerCourtInsertion in the legal judgment prediction task.
### Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
## Dataset structure
### Data Instances
#### Multilingual use of the dataset
When the dataset is used in a multilingual setting selecting the the 'all' flag:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/lower_court_insertion_swiss_judgment_prediction', 'all')
```
#### Monolingual use of the dataset
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/lower-court-insertion_swiss_judgment_prediction', 'de')
```
### Data Fields
The following data fields are provided for documents (test):
id: (**int**) a unique identifier of the for the document<br/>
year: (**int**) the publication year<br/>
label: (**str**) the judgment outcome: dismissal or approval<br/>
language: (**str**) one of (de, fr, it)<br/>
region: (**str**) the region of the lower court<br/>
canton: (**str**) the canton of the lower court<br/>
legal area: (**str**) the legal area of the case<br/>
explainability_label: (**str**) the explainability label assigned to the occluded text: (Lower court, Baseline)<br/>
text: (**str**) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)<br/>
lower_court: (**str**) the inserted lower_court (for Baseline there is no insertion)<br/>
### Data Splits (Including Swiss Judgment Prediction)
Language | Subset | Number of Rows (Test)
|-----|-----|------|
German| de| __378__
French | fr| __414__
Italian | it| __335__
All | all | __1127__
Language | Subset | Number of Documents (Test)
| ----------- | ----------- | ----------- |
German| de | __38__
French | fr | __36__
Italian | it | __34__
All | all | __108__
## Dataset Creation
### Curation Rationale
The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition the a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts with the lower court. These lower court annotations were then use the insert each lower court into each case once (instead of the original lower court). Allowing an analysis of the changes in the models performance for each inserted lower court, giving insight into a possible bias among them. The legal expert annotation were conducted from April 2020 to August 2020.
#### Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Additional Information
### Dataset Curators
Niklaus et al. (2021) and Nina Baumgartner
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
```
@misc{baumgartner_nina_occlusion_2019,
title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
shorttitle = {From Occlusion to Transparancy},
abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
author = {{Baumgartner, Nina}},
year = {2022},
langid = {english}
}
```
### Contributions
Thanks to [@ninabaumgartner](https://github.com/ninabaumgartner) for adding this dataset.
|
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NiGuLa/Russian_Sensitive_Topics
|
NiGuLa
| 2023-05-12T13:36:44Z | 42 | 2 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ru",
"license:cc",
"toxic comments classification",
"region:us"
] |
[
"text-classification"
] | 2023-04-07T20:14:33Z |
---
language:
- ru
tags:
- toxic comments classification
license: cc
task_categories:
- text-classification
size_categories:
- 10K<n<100K
---
## General concept of the model
Sensitive topics are such topics that have a high chance of initiating a toxic conversation: homophobia, politics, racism, etc. This dataset uses 18 topics.
More details can be found [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference.
This paper presents the first version of this dataset. Here you can see the last version of the dataset which is significantly larger and also properly filtered.
## Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@inproceedings{babakov-etal-2021-detecting,
title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation",
author = "Babakov, Nikolay and
Logacheva, Varvara and
Kozlova, Olga and
Semenov, Nikita and
Panchenko, Alexander",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4",
pages = "26--36",
abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
}
```
|
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grosenthal/latin_english_parallel
|
grosenthal
| 2023-04-28T02:11:31Z | 42 | 3 |
[
"task_categories:translation",
"size_categories:10K<n<100K",
"language:la",
"language:en",
"license:mit",
"region:us"
] |
[
"translation"
] | 2023-04-07T21:09:52Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: la
dtype: string
- name: en
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 39252644
num_examples: 99343
- name: test
num_bytes: 405056
num_examples: 1014
- name: valid
num_bytes: 392886
num_examples: 1014
download_size: 25567350
dataset_size: 40050586
license: mit
task_categories:
- translation
language:
- la
- en
pretty_name: Latin to English Translation Pairs
size_categories:
- 10K<n<100K
---
# Dataset Card for "latin_english_parallel"
101k translation pairs between Latin and English, split 99/1/1 as train/test/val. These have been collected roughly 66% from the Loeb Classical Library and 34% from the Vulgate translation.
For those that were gathered from the Loeb Classical Library, alignment was performd manually between Source and Target sequences. Additionally, the English translations were both 1. copyrighted and 2. outdated. As such, we decided to modernize and transform them into ones that could be used in the public domain, as the original Latin is not copyrighted.
To perform this, we used the gpt3.5-turbo model on OpenAI with the prompt `Translate an old dataset from the 1800s to modern English while preserving the original meaning and exact same sentence structure. Retain extended adjectives, dependent clauses, and punctuation. Output the translation preceded by the text "Modern Translation: ". If a given translation is not a complete sentence, repeat the input sentence. \n'` followed by the source English.
We then manually corrected all outputs that did not conform to the standard.
Each sample is annotated with the index and file (and therefore author/work) that the sample is from. If you find errors, please feel free to submit a PR to fix them.
|
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] |
PaulineSanchez/Translation_words_and_sentences_english_french
|
PaulineSanchez
| 2023-04-27T12:29:31Z | 42 | 0 |
[
"task_categories:translation",
"language:en",
"language:fr",
"words",
"sentences",
"everyday life",
"casual",
"region:us"
] |
[
"translation"
] | 2023-04-27T12:23:20Z |
---
task_categories:
- translation
language:
- en
- fr
tags:
- words
- sentences
- everyday life
- casual
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
This dataset is a clean version (all NanN removed) of this dataset : https://www.kaggle.com/datasets/devicharith/language-translation-englishfrench . I'm not the person who posted it first on Kaggle.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
tasksource/tracie
|
tasksource
| 2023-05-31T08:26:23Z | 42 | 2 |
[
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"nli",
"region:us"
] |
[
"text-classification"
] | 2023-05-25T07:17:09Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- nli
---
https://github.com/allenai/aristo-leaderboard/tree/master/tracie/data
```
@inproceedings{ZRNKSR21,
author = {Ben Zhou and Kyle Richardson and Qiang Ning and Tushar Khot and Ashish Sabharwal and Dan Roth},
title = {Temporal Reasoning on Implicit Events from Distant Supervision},
booktitle = {NAACL},
year = {2021},
}
```
|
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] |
clarin-knext/nq-pl
|
clarin-knext
| 2023-09-07T12:25:03Z | 42 | 0 |
[
"language:pl",
"license:cc-by-4.0",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T17:13:34Z |
---
license: cc-by-4.0
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
EasyTerms/Manuel_dataset
|
EasyTerms
| 2023-11-02T14:03:24Z | 42 | 2 |
[
"task_categories:summarization",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"legal",
"region:us"
] |
[
"summarization"
] | 2023-06-07T08:50:06Z |
---
license: apache-2.0
task_categories:
- summarization
language:
- en
tags:
- legal
pretty_name: lilo
size_categories:
- n<1K
---
|
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dmayhem93/agieval-lsat-lr
|
dmayhem93
| 2023-06-18T17:26:20Z | 42 | 0 |
[
"license:mit",
"arxiv:2304.06364",
"arxiv:2104.06598",
"region:us"
] | null | 2023-06-18T12:50:37Z |
---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 923886
num_examples: 510
download_size: 469904
dataset_size: 923886
license: mit
---
# Dataset Card for "agieval-lsat-lr"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
Raw datset: https://github.com/zhongwanjun/AR-LSAT
MIT License
Copyright (c) 2022 Wanjun Zhong
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{zhong2021arlsat,
title={AR-LSAT: Investigating Analytical Reasoning of Text},
author={Wanjun Zhong and Siyuan Wang and Duyu Tang and Zenan Xu and Daya Guo and Jiahai Wang and Jian Yin and Ming Zhou and Nan Duan},
year={2021},
eprint={2104.06598},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{wang2022lsat,
title={From lsat: The progress and challenges of complex reasoning},
author={Wang, Siyuan and Liu, Zhongkun and Zhong, Wanjun and Zhou, Ming and Wei, Zhongyu and Chen, Zhumin and Duan, Nan},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
year={2022},
publisher={IEEE}
}
|
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] |
dmayhem93/agieval-lsat-rc
|
dmayhem93
| 2023-06-18T17:27:15Z | 42 | 0 |
[
"license:mit",
"arxiv:2304.06364",
"arxiv:2104.06598",
"region:us"
] | null | 2023-06-18T12:50:49Z |
---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 1136305
num_examples: 269
download_size: 322710
dataset_size: 1136305
license: mit
---
# Dataset Card for "agieval-lsat-rc"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
Raw datset: https://github.com/zhongwanjun/AR-LSAT
MIT License
Copyright (c) 2022 Wanjun Zhong
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{zhong2021arlsat,
title={AR-LSAT: Investigating Analytical Reasoning of Text},
author={Wanjun Zhong and Siyuan Wang and Duyu Tang and Zenan Xu and Daya Guo and Jiahai Wang and Jian Yin and Ming Zhou and Nan Duan},
year={2021},
eprint={2104.06598},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{wang2022lsat,
title={From lsat: The progress and challenges of complex reasoning},
author={Wang, Siyuan and Liu, Zhongkun and Zhong, Wanjun and Zhou, Ming and Wei, Zhongyu and Chen, Zhumin and Duan, Nan},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
year={2022},
publisher={IEEE}
}
|
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Patt/MultiRC_TH_drop
|
Patt
| 2023-07-20T15:26:22Z | 42 | 0 |
[
"task_categories:text-classification",
"language:en",
"language:th",
"arxiv:1907.04307",
"region:us"
] |
[
"text-classification"
] | 2023-06-22T13:20:37Z |
---
task_categories:
- text-classification
language:
- en
- th
dataset_info:
features:
- name: paragraph
dtype: string
- name: paragraph_TH
dtype: string
- name: question
dtype: string
- name: question_TH
dtype: string
- name: answer
dtype: string
- name: answer_TH
dtype: string
- name: idx
struct:
- name: answer
dtype: int64
- name: paragraph
dtype: int64
- name: question
dtype: int64
- name: label
dtype: int64
- name: score_paragraph
dtype: float64
- name: score_question
dtype: float64
- name: score_answer
dtype: float64
splits:
- name: train
num_bytes: 133061823
num_examples: 23520
- name: validation
num_bytes: 22534453
num_examples: 4212
- name: test
num_bytes: 42757726
num_examples: 8272
download_size: 5756232
dataset_size: 198354002
---
# Dataset Card for MultiRC_TH_drop
### Dataset Description
This dataset is Thai translated version of [multirc](https://huggingface.co/datasets/super_glue/viewer/multirc) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
The score was penalized by the length of original text compare to translated text. The row that any score < 0.66 was dropped.
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germank/hh-generated_flan_t5_large_with_features2
|
germank
| 2023-07-07T14:32:37Z | 42 | 0 |
[
"region:us"
] | null | 2023-07-07T14:32:34Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: 'biased:'
dtype: int64
- name: easy-to-understand
dtype: int64
- name: enough-detail
dtype: int64
- name: factuality
dtype: int64
- name: fail-to-consider-context
dtype: int64
- name: fail-to-consider-individual-preferences
dtype: int64
- name: helpfulness
dtype: int64
- name: intent
dtype: int64
- name: readability
dtype: int64
- name: relevance
dtype: int64
- name: repetetive
dtype: int64
- name: specificity
dtype: int64
- name: too-long
dtype: int64
splits:
- name: train
num_bytes: 395548
num_examples: 1600
download_size: 76218
dataset_size: 395548
---
# Dataset Card for "hh-generated_flan_t5_large_with_features2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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nahyeon00/mixsnips_clean
|
nahyeon00
| 2023-07-19T08:38:38Z | 42 | 0 |
[
"region:us"
] | null | 2023-07-19T08:38:23Z |
---
dataset_info:
features:
- name: token
sequence: string
- name: tag
sequence: string
- name: intent
sequence: string
splits:
- name: train
num_bytes: 16319528
num_examples: 39776
- name: validation
num_bytes: 915087
num_examples: 2198
- name: test
num_bytes: 902367
num_examples: 2199
download_size: 3076227
dataset_size: 18136982
---
# Dataset Card for "mixsnips_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
abacusai/LongChat-Lines
|
abacusai
| 2023-07-28T03:14:01Z | 42 | 10 |
[
"region:us"
] | null | 2023-07-27T15:16:12Z |
---
configs:
- config_name: default
data_files:
- split: '100'
path: data/100-*
- split: '150'
path: data/150-*
- split: '175'
path: data/175-*
- split: '200'
path: data/200-*
- split: '250'
path: data/250-*
- split: '300'
path: data/300-*
- split: '400'
path: data/400-*
- split: '500'
path: data/500-*
- split: '600'
path: data/600-*
- split: '680'
path: data/680-*
- split: '750'
path: data/750-*
- split: '850'
path: data/850-*
- split: '950'
path: data/950-*
- split: '1100'
path: data/1100-*
dataset_info:
features:
- name: expected_number
dtype: int64
- name: num_lines
dtype: int64
- name: token_size
dtype: int64
- name: prompt
dtype: string
splits:
- name: '100'
num_bytes: 275673
num_examples: 50
- name: '150'
num_bytes: 400446
num_examples: 50
- name: '175'
num_bytes: 463159
num_examples: 50
- name: '200'
num_bytes: 525856
num_examples: 50
- name: '250'
num_bytes: 650643
num_examples: 50
- name: '300'
num_bytes: 775800
num_examples: 50
- name: '400'
num_bytes: 1025288
num_examples: 50
- name: '500'
num_bytes: 1276039
num_examples: 50
- name: '600'
num_bytes: 1524627
num_examples: 50
- name: '680'
num_bytes: 1724325
num_examples: 50
- name: '750'
num_bytes: 1899422
num_examples: 50
- name: '850'
num_bytes: 2149220
num_examples: 50
- name: '950'
num_bytes: 2398398
num_examples: 50
- name: '1100'
num_bytes: 2772556
num_examples: 50
download_size: 7270406
dataset_size: 17861452
---
# Dataset Card for "LongChat-Lines"
This dataset is was used to evaluate the performance of model finetuned to operate on longer contexts it is based on
a task template proposed LMSys to evaluate attention to arbitrary points in the context. See the full details at
[https;//github.com/abacusai/Long-Context](https://github.com/abacusai/Long-Context).
|
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seanlb02/medicare_faq
|
seanlb02
| 2023-07-31T23:38:43Z | 42 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-07-31T23:22:03Z |
---
license: mit
---
|
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EgilKarlsen/AA_GPT2_Baseline
|
EgilKarlsen
| 2023-08-17T21:22:52Z | 42 | 0 |
[
"region:us"
] | null | 2023-08-11T00:57:32Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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num_examples: 26057
- name: test
num_bytes: 26773977.07147654
num_examples: 8686
download_size: 0
dataset_size: 107092821.97314951
---
# Dataset Card for "AA_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Matias12f/cats_dogs_trabajo
|
Matias12f
| 2023-08-22T02:24:38Z | 42 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-08-22T02:23:34Z |
---
license: apache-2.0
---
|
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] |
ArmelR/oasst1_guanaco_english
|
ArmelR
| 2023-08-26T01:05:26Z | 42 | 1 |
[
"region:us"
] | null | 2023-08-26T01:05:22Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 2500171.710492605
num_examples: 2181
- name: test
num_bytes: 278561.0846628625
num_examples: 243
download_size: 1690262
dataset_size: 2778732.7951554675
---
# Dataset Card for "oasst1_guanaco_english"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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folkopinion/government-interpellation-qa-swedish
|
folkopinion
| 2023-09-25T18:07:34Z | 42 | 1 |
[
"task_categories:table-question-answering",
"size_categories:1K<n<10K",
"language:sv",
"government",
"politics",
"interpellation",
"region:us"
] |
[
"table-question-answering"
] | 2023-09-17T19:00:42Z |
---
task_categories:
- table-question-answering
language:
- sv
tags:
- government
- politics
- interpellation
pretty_name: Riksdagen Interpellation Debate QA
size_categories:
- 1K<n<10K
---
|
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] |
ura-hcmut/synthetic_reasoning_natural
|
ura-hcmut
| 2023-09-19T02:35:59Z | 42 | 0 |
[
"task_categories:text2text-generation",
"language:vi",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text2text-generation"
] | 2023-09-19T02:34:10Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
- vi
configs:
- config_name: easy_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_natural_training.csv
- split: test
path: synthetic_reasoning_gcp_natural.csv
- config_name: easy_azr
data_files:
- split: train
path: synthetic_reasoning_azr_natural_training.csv
- split: test
path: synthetic_reasoning_azr_natural.csv
---
# Synthetic reasoning dataset
Original version:
- https://huggingface.co/datasets/lighteval/synthetic_reasoning_natural
Translation source code: https://github.com/martinakaduc/ura-llama/tree/main/dataset_scripts/custom_datasets
|
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-0.17396599054336548,
-0.2745007276535034,
-0.19150033593177795,
-0.3911573588848114,
-0.2496711164712906,
-0.11505088210105896
] |
pablo-moreira/gpt4all-j-prompt-generations-pt
|
pablo-moreira
| 2023-10-06T16:02:12Z | 42 | 1 |
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:pt",
"license:apache-2.0",
"region:us"
] |
[
"text-generation"
] | 2023-09-28T01:43:05Z |
---
language:
- pt
license: apache-2.0
size_categories:
- 100K<n<1M
task_categories:
- text-generation
pretty_name: GPT4All Prompt Generations translated into Portuguese using Google Translate.
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: source
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1956916380
num_examples: 808812
download_size: 1134108118
dataset_size: 1956916380
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gpt4all-j-prompt-generations-pt"
## Dataset Description
Copy translated into Portuguese of the dataset [gpt4all_prompt_generations](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations) using the googletrans library.
## Translate
[translate_dataset.ipynb](translate_dataset.ipynb)
## Usage
[dataset_usage.ipynb](dataset_usage.ipynb)
|
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] |
liyucheng/ceval_all
|
liyucheng
| 2023-09-29T10:07:50Z | 42 | 0 |
[
"region:us"
] | null | 2023-09-29T10:04:27Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer
dtype: string
- name: explanation
dtype: string
splits:
- name: val
num_bytes: 406528
num_examples: 1346
- name: test
num_bytes: 3720917
num_examples: 12342
- name: dev
num_bytes: 172688
num_examples: 260
download_size: 2792076
dataset_size: 4300133
---
# Dataset Card for "ceval_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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cannlytics/cannabis_analytes
|
cannlytics
| 2023-10-10T23:20:30Z | 42 | 1 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-10-10T21:24:44Z |
---
pretty_name: cannabis_analytes
license:
- cc-by-4.0
---
# Cannabis Analytes
This dataset consists of analyte data for various analytes that are regularly tested for in cannabis. The dataset consists of sub-datasets for each type of test, as well as a sub-dataset that includes all analytes.
## Dataset Structure
The dataset is partitioned into 18 subsets for each state and the aggregate.
| State | Code | Status |
| [All](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/analytes.json) | `all` | ✅ |
| [Cannabinoids](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/cannabinoids.json) | `cannabinoids` | ✅ |
| [Terpenes](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/terpenes.json) | `terpenes` | ✅ |
| Pesticides | `pesticides` | ⏳ Coming soon |
| Microbes | `microbes` | ⏳ Coming soon |
| Heavy metals | `heavy_metals` | ⏳ Coming soon |
| Residual solvents | `residual_solvents` | ⏳ Coming soon |
| Other | `other` | ⏳ Coming soon |
## Using the Dataset
You can load all the analytes, or the analytes for a specific test. For example:
```py
from datasets import load_dataset
# Get all of the analytes
dataset = load_dataset('cannlytics/cannabis_licenses', 'all')
analytes = dataset['data']
# Get the cannabinoids.
dataset = load_dataset('cannlytics/cannabis_licenses', 'cannabinoids')
terpenes = dataset['data']
# Get the terpenes.
dataset = load_dataset('cannlytics/cannabis_licenses', 'terpenes')
terpenes = dataset['data']
```
## Data Fields
Below is a non-exhaustive list of fields, used to standardize the various data that are encountered, that you may expect to find for each observation.
## Data Fields
Below is a non-exhaustive list of fields used to standardize the various data that are encountered. You may expect to find the following for each observation:
| Field | Example | Description |
|------------------------------|----------------------------------------------|------------------------------------------------------------------------------------------------------|
| `key` | `"thca"` | A unique ID for each analyte. |
| `description` | `"Δ-9-Tetrahydrocannabinol is a cannabinoid..."` | A brief description or summary about the analyte. |
| `name` | `"THC"` | Common name of the analyte. |
| `scientific_name` | `"\u0394-9-Tetrahydrocannabinol"` | The scientific name or IUPAC name of the analyte. |
| `type` | `"cannabinoid"` | The type or classification of the analyte (e.g., terpene, cannabinoid). |
| `wikipedia_url` | `"https://en.wikipedia.org/wiki/Tetrahydrocannabinol"` | The Wikipedia URL where more detailed information can be found about the analyte. |
| `degrades_to` | `["cannabinol"]` | A list of chemicals or substances the analyte degrades to. |
| `precursors` | `["thca"]` | A list of precursor chemicals or substances related to the analyte. |
| `subtype` | `"psychoactive"` | A sub-classification or additional details about the type of the analyte. |
| `cas_number` | `"1972-08-3"` | The Chemical Abstracts Service (CAS) registry number, which is a unique identifier for chemical substances.|
| `chemical_formula` | `"C21H30O2"` | The chemical formula of the analyte. |
| `molar_mass` | `"314.5 g/mol"` | The molar mass of the analyte. |
| `density` | `"1.0±0.1 g/cm3"` | The density of the analyte. |
| `boiling_point` | `"383.5±42.0 °C"` | The boiling point of the analyte. |
| `image_url` | `"https://example.com/image.jpg"` | URL of an image representing the analyte. |
| `chemical_formula_image_url` | `"https://example.com/formula_image.jpg"` | URL of an image representing the chemical formula of the analyte. |
## Data Splits
The data is split into subsets by analysis. You can retrieve all analytes by requesting the `all` subset.
```py
from datasets import load_dataset
# Get all cannabis licenses.
dataset = load_dataset('cannlytics/cannabis_licenses', 'all')
data = dataset['data']
```
## Curation Rationale
This dataset provides a standard set of analyte data for [cannabis tests](https://huggingface.co/datasets/cannlytics/cannabis_tests).
## Data Collection and Normalization
The `get_cannabis_analytes.py` routine is used to normalize values collected from Wikipedia.
## Known Limitations
The datasets are not complete and may include inaccurate information.
## Dataset Curators
Curated by [🔥Cannlytics](https://cannlytics.com)<br>
<[email protected]>
## License
```
Copyright (c) 2023 Cannlytics
The files associated with this dataset are licensed under a
Creative Commons Attribution 4.0 International license.
You can share, copy and modify this dataset so long as you give
appropriate credit, provide a link to the CC BY license, and
indicate if changes were made, but you may not do so in a way
that suggests the rights holder has endorsed you or your use of
the dataset. Note that further permission may be required for
any content within the dataset that is identified as belonging
to a third party.
```
## Contributions
Thanks to [🔥Cannlytics](https://cannlytics.com), [@candy-o](https://github.com/candy-o), [@keeganskeate](https://github.com/keeganskeate), and the entire [Cannabis Data Science Team](https://meetup.com/cannabis-data-science/members) for their contributions.
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] |
nlplabtdtu/closed-QA-vi
|
nlplabtdtu
| 2023-10-24T08:54:13Z | 42 | 0 |
[
"region:us"
] | null | 2023-10-24T05:08:44Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: context
dtype: string
- name: hint
dtype: string
- name: ok
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 11217015
num_examples: 6380
download_size: 5360083
dataset_size: 11217015
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "closed-QA-vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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19kmunz/iot-23-preprocessed
|
19kmunz
| 2023-11-03T17:12:47Z | 42 | 0 |
[
"task_categories:question-answering",
"task_categories:tabular-classification",
"language:en",
"code",
"region:us"
] |
[
"question-answering",
"tabular-classification"
] | 2023-10-27T16:39:01Z |
---
dataset_info:
features:
- name: id.orig_p
dtype: int64
- name: id.resp_p
dtype: int64
- name: proto
dtype: string
- name: service
dtype: string
- name: duration
dtype: float64
- name: orig_bytes
dtype: int64
- name: resp_bytes
dtype: int64
- name: conn_state
dtype: string
- name: missed_bytes
dtype: int64
- name: history
dtype: string
- name: orig_pkts
dtype: int64
- name: orig_ip_bytes
dtype: int64
- name: resp_pkts
dtype: int64
- name: resp_ip_bytes
dtype: int64
- name: label
dtype: string
splits:
- name: train
num_bytes: 93994789
num_examples: 819024
download_size: 11805369
dataset_size: 93994789
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- question-answering
- tabular-classification
language:
- en
tags:
- code
pretty_name: d
---
# Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic
**Homepage:** [https://www.stratosphereips.org/datasets-iot23](https://www.stratosphereips.org/datasets-iot23)
This dataset contains a subset of the data from 20 captures of Malcious network traffic and 3 captures from live Benign Traffic on Internet of Things (IoT) devices. Created by Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga at the Avast AIC laboratory with the funding of Avast Software, this dataset is one of the best in the field for Intrusion Detection Systems (IDS) for IoT Devices [(Comparative Analysis of IoT Botnet Datasets)](https://doi.org/10.53070/bbd.1173687).
The selection of the subset was determined by [Aqeel Ahmed on Kaggle](https://www.kaggle.com/datasets/engraqeel/iot23preprocesseddata) and contains 6 million samples. The Kaggle upload, nor this one, have employed data balancing. The Kaggle card does not contain methodology to understand what criteria was used to select these samples. If you want ensure best practice, use this dataset to mock-up processing the data into a model before using the full dataset with data balancing. This will require processing the 8GB of conn.log.labelled files.
This dataset only notes if the data is Malcious or Benign. The original dataset labels the type of malcious traffic aswell. This means this processing of the dataset is only suited for binary classification.
# Feature information:
All features originate from the [Zeek](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info) processing performed by the dataset creators. [See notes here for caviats for each column](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info).
<details>
<summary>Expand for feature names, descriptions, and datatypes</summary>
Name: id.orig_p
Description: The originator’s port number.
Data type: int64 - uint64 in original
Name: id.resp_p
Description: The responder’s port number.
Data type: int64 - uint64 in original
Name: proto
Description: The transport layer protocol of the connection.
Data type: string - enum(unknown_transport, tcp, udp, icmp). Only TCP and UDP in subset
Name: service
Description: An identification of an application protocol being sent over the connection.
Data type: optional string
Name: duration
Description: How long the connection lasted.
Data type: optional float64 - time interval
Name: orig_bytes
Description: The number of payload bytes the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_bytes
Description:The number of payload bytes the responder sent.
Data type: optional int64 - uint64 in original
Name: conn_state
Description: Value indicating connection state. (S0, S1, SF, REJ, S2, S3, RSTO, RSTR, RSTOS0, RSTRH, SH, SHR, OTH)
Data type: optional string
Name: missed_bytes
Description: Indicates the number of bytes missed in content gaps, which is representative of packet loss.
Data type: optional int64 - uint64 in original. default = 0
Name: history
Description: Records the state history of connections as a string of letters.
Data type: optional string
Name: orig_pkts
Description: Number of packets that the originator sent.
Data type: optional int64 - uint64 in original
Name: orig_ip_bytes
Description: Number of IP level bytes that the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_pkts
Description: Number of packets that the responder sent.
Data type: optional int64 - uint64 in original
Name: resp_ip_bytes
Description: Number of IP level bytes that the responder sent.
Data type: optional int64 - uint64 in original
Name: label
Description: Specifies if data point is benign or some form of malicious. See the dataset creators paper for descriptions of attack types
Data type: string - enum(Malicious, Benign)
NOTE: ts, uid, id.orig_h, id.resp_h have been removed as they are dataset specific. Models should not be trained with specific timestamps or IP addresses (id.orig_h) using this dataset, as that can lead to over fitting to dataset specific times and addresses.
Further local_orig, local_resp have been removed as they are null in all rows, so they are useless for training.
</details>
## Citation
If you are using this dataset for your research, please reference it as “Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4743746”
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MegPaulson/Healthy_Skin
|
MegPaulson
| 2023-11-02T05:33:08Z | 42 | 0 |
[
"region:us"
] | null | 2023-11-01T23:32:41Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 1679582797.125
num_examples: 6039
download_size: 1660153728
dataset_size: 1679582797.125
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Healthy_Skin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Flyfer/TestingFineTuningCB
|
Flyfer
| 2023-11-13T10:17:27Z | 42 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-07T08:56:45Z |
---
license: apache-2.0
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: text
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 7667
num_examples: 4
download_size: 11176
dataset_size: 7667
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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BENBENBENb/sythetic_casual_relation_medium_scale
|
BENBENBENb
| 2023-11-08T01:12:20Z | 42 | 0 |
[
"language:en",
"region:us"
] | null | 2023-11-07T21:37:31Z |
---
language:
- en
---
|
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GabrielGaleana34/Products
|
GabrielGaleana34
| 2023-11-13T04:26:55Z | 42 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-08T00:10:44Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 91453253.0
num_examples: 451
download_size: 88009496
dataset_size: 91453253.0
---
|
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] |
joseluhf11/oct-object-detection
|
joseluhf11
| 2023-11-22T08:36:49Z | 42 | 0 |
[
"region:us"
] | null | 2023-11-13T13:51:02Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
sequence:
sequence: int64
- name: categories
sequence: string
splits:
- name: train
num_bytes: 666483144.628
num_examples: 4698
download_size: 76903163
dataset_size: 666483144.628
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oct-object-detection"
Dataset is composed of images with individual object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
[Source datataset](https://doi.org/10.1101/2023.03.29.534704)
|
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A-Roucher/Open_Assistant_Conversation_Chains
|
A-Roucher
| 2023-11-22T14:37:58Z | 42 | 2 |
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:hu",
"language:eu",
"language:zh",
"language:eo",
"language:ja",
"language:ca",
"language:cs",
"language:bg",
"language:fi",
"language:pt",
"language:tr",
"language:ro",
"language:ar",
"language:uk",
"language:gl",
"language:fr",
"language:ko",
"license:apache-2.0",
"human-feedback",
"region:us"
] |
[
"conversational",
"text-generation"
] | 2023-11-16T18:35:52Z |
---
license: apache-2.0
language:
- en
- es
- ru
- de
- pl
- th
- vi
- sv
- bn
- da
- he
- it
- fa
- sk
- id
- nb
- el
- nl
- hu
- eu
- zh
- eo
- ja
- ca
- cs
- bg
- fi
- pt
- tr
- ro
- ar
- uk
- gl
- fr
- ko
task_categories:
- conversational
- text-generation
tags:
- human-feedback
size_categories:
- 10K<n<100K
pretty_name: OpenAssistant Conversations Unrolled
---
# Dataset Card for Dataset Name
## Dataset description
<!-- Provide a quick summary of the dataset. -->
This dataset is a reformatting of [OpenAssistant Conversations (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1), which is
> a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.
It was modified from the original by following the tree branches and reforming the messages into conversation streams.
#### Who are the editors?
Aymeric Roucher, Hugging Face.
### Example
````
{
'conversation_id': '6708c47f-05c9-4346-b3d2-40b2bd24fde4',
'user_id': '2c96e467-66f0-4be7-9693-bda51356a424',
'created_date': '2023-02-06T18:48:49.391686+00:00',
'messages': [
{'content': 'Can you write a short introduction about the relevance of the term "monopsony" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.',
'role': 'user'},
{'content': '"Monopsony" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\n\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens & Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\n\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\n\nReferences:\nBivens, J., & Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.',
'role': 'assistant'},
{'content': 'Now explain it to a dog', 'role': 'user'}
]
}
````
|
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josephimperial/CebuaNER
|
josephimperial
| 2023-11-17T12:07:58Z | 42 | 0 |
[
"size_categories:1K<n<10K",
"license:cc-by-nc-4.0",
"arxiv:2310.00679",
"region:us"
] | null | 2023-11-17T11:58:38Z |
---
license: cc-by-nc-4.0
size_categories:
- 1K<n<10K
---
This repository contains CebuaNER, the largest gold-standard datasets for named entities in Cebuano. This dataset is used for the [paper](https://arxiv.org/abs/2310.00679) `CebuaNER: A New Baseline Cebuano Named Entity Recognition Model` to be presented at PACLIC 2023, authored by Ma. Beatrice Emanuela N. Pilar, Ellyza Mari J. Papas, Mary Loise Buenaventura, Dane C. Dedoroy, Myron Montefalcon, Jay Rhald Padilla, Lany Maceda, Mideth Abisado, and Joseph Imperial.
## Data
The dataset contribution of this study is a compilation of Cebuano news articles from two local news stations, Yes the Best Dumaguete and
the Filipinas Bisaya. To further increase the data count, we also incorporated another publicly available dataset from [Sunstar Cebu](https://github.com/rjrequina/Cebuano-POS-Tagger/blob/master/eval/data/scraped/news-raw.txt) pre-collected by independent researcher Arjemariel Requina. The total accumulated and filtered size of the Cebuano dataset is 4258 articles. All the dataset used for this study is purely for non-commercial research purposes and under Fair Use.
| Source | Original | Cleaned |
| ------------------------| ------------- | ------- |
| Yes the Best Dumaguete | 1484 | 781 |
| Filipinas Bisaya | 769 | 377 |
| Sunstar | 3100 | 3100 |
## Note on Data Cataloging
Please send an email before submitting this repository to any data cataloging, data aggregation, and benchmarking projects/initiatives. The proponents of the paper of this dataset would like to be acknowledged appropriately or involved in co-authorship.
## Contact
For questions, you may reach the corresponding author of the study at:
**Joseph Marvin Imperial**\
Faculty Member, Department of Computer Science\
Lab Head, NU Human Language Technology Lab\
[email protected]
|
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arianhosseini/openai_summarize_unlabelled
|
arianhosseini
| 2023-11-18T21:34:06Z | 42 | 0 |
[
"region:us"
] | null | 2023-11-18T21:33:58Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 167037359
num_examples: 107543
download_size: 101979854
dataset_size: 167037359
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "openai_summarize_unlabelled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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MrBananaHuman/kor_ethical_question_answer
|
MrBananaHuman
| 2023-11-20T11:08:47Z | 42 | 1 |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-11-20T11:08:01Z |
---
license: cc-by-nc-nd-4.0
---
|
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PaulTran/translated_math
|
PaulTran
| 2023-11-21T01:06:23Z | 42 | 0 |
[
"region:us"
] | null | 2023-11-20T12:43:30Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: answer
dtype: string
- name: explanation
dtype: string
- name: choices
sequence: string
- name: question
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 7908449
num_examples: 28386
download_size: 1068029
dataset_size: 7908449
---
# Dataset Card for "translated_math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
eswardivi/Tamil_MSA_Audio_Text_Chunked
|
eswardivi
| 2023-11-22T09:02:54Z | 42 | 0 |
[
"region:us"
] | null | 2023-11-22T09:02:49Z |
---
dataset_info:
features:
- name: Audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': Negative
'1': Neutral
'2': Positive
- name: FilePath
dtype: string
- name: Text
dtype: string
splits:
- name: train
num_bytes: 39958223.0
num_examples: 128
download_size: 39793452
dataset_size: 39958223.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
confit/timit
|
confit
| 2023-11-25T00:42:28Z | 42 | 0 |
[
"region:us"
] | null | 2023-11-25T00:42:23Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: filename
dtype: string
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 136862
num_examples: 3780
- name: validation
num_bytes: 46145
num_examples: 1260
- name: test
num_bytes: 46508
num_examples: 1260
download_size: 124769
dataset_size: 229515
---
# Dataset Card for "timit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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dvijay/databricks-dolly-15k-formatted
|
dvijay
| 2023-11-28T05:55:42Z | 42 | 0 |
[
"region:us"
] | null | 2023-11-28T05:55:28Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 12195589
num_examples: 15011
download_size: 7749038
dataset_size: 12195589
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
BeIR/nq-generated-queries
|
BeIR
| 2022-10-23T06:15:15Z | 41 | 0 |
[
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-17T13:20:26Z |
---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
|
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scikit-learn/credit-card-clients
|
scikit-learn
| 2022-06-20T15:42:14Z | 41 | 0 |
[
"license:cc0-1.0",
"region:us"
] | null | 2022-06-20T14:57:10Z |
---
license: cc0-1.0
---
## Default of Credit Card Clients Dataset
The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients).
**Dataset Information**
This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.
**Content**
There are 25 variables:
- ID: ID of each client
- LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit
- SEX: Gender (1=male, 2=female)
- EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)
- MARRIAGE: Marital status (1=married, 2=single, 3=others)
- AGE: Age in years
- PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above)
- PAY_2: Repayment status in August, 2005 (scale same as above)
- PAY_3: Repayment status in July, 2005 (scale same as above)
- PAY_4: Repayment status in June, 2005 (scale same as above)
- PAY_5: Repayment status in May, 2005 (scale same as above)
- PAY_6: Repayment status in April, 2005 (scale same as above)
- BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)
- BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)
- BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)
- BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)
- BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)
- BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)
- PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)
- PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)
- PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)
- PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)
- PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)
- PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)
- default.payment.next.month: Default payment (1=yes, 0=no)
**Inspiration**
Some ideas for exploration:
How does the probability of default payment vary by categories of different demographic variables?
Which variables are the strongest predictors of default payment?
**Acknowledgements**
Any publications based on this dataset should acknowledge the following:
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
|
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] |
jakartaresearch/indonews
|
jakartaresearch
| 2022-08-07T04:27:54Z | 41 | 2 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"news",
"news-classifcation",
"indonesia",
"region:us"
] |
[
"text-classification"
] | 2022-08-07T04:03:02Z |
---
annotations_creators:
- found
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Indonews
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- news
- news-classifcation
- indonesia
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Indonesian News Categorization
## 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
Indonews: Multiclass News Categorization scrapped popular news portals in Indonesia.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset.
|
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dgrnd4/animals-10
|
dgrnd4
| 2022-10-04T16:45:42Z | 41 | 3 |
[
"license:other",
"region:us"
] | null | 2022-10-04T16:39:10Z |
---
license: other
---
|
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Aunsiels/InfantBooks
|
Aunsiels
| 2022-10-24T11:20:01Z | 41 | 4 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:gpl",
"research paper",
"kids",
"children",
"books",
"region:us"
] |
[
"text-generation"
] | 2022-10-24T08:57:35Z |
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- crowdsourced
license:
- gpl
multilinguality:
- monolingual
pretty_name: InfantBooks
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- research paper
- kids
- children
- books
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for InfantBooks
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://www.mpi-inf.mpg.de/children-texts-for-commonsense](https://www.mpi-inf.mpg.de/children-texts-for-commonsense)
- **Paper:** Do Children Texts Hold The Key To Commonsense Knowledge?
### Dataset Summary
A dataset of infants/children's books.
### Languages
All the books are in English;
## Dataset Structure
### Data Instances
malis-friend_BookDash-FKB.txt,"Then a taxi driver, hooting around the yard with his wire car. Mali enjoys playing by himself..."
### Data Fields
- title: The title of the book
- content: The content of the book
## Dataset Creation
### Curation Rationale
The goal of the dataset is to study infant books, which are supposed to be easier to understand than normal texts. In particular, the original goal was to study if these texts contain more commonsense knowledge.
### Source Data
#### Initial Data Collection and Normalization
We automatically collected kids' books on the web.
#### Who are the source language producers?
Native speakers.
### Citation Information
```
Romero, J., & Razniewski, S. (2022).
Do Children Texts Hold The Key To Commonsense Knowledge?
In Proceedings of the 2022 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.
```
|
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autoevaluate/autoeval-eval-futin__guess-en-6f8c6a-2012266600
|
autoevaluate
| 2022-11-07T09:29:28Z | 41 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-11-07T09:14:03Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/guess
eval_info:
task: text_zero_shot_classification
model: facebook/opt-350m
metrics: []
dataset_name: futin/guess
dataset_config: en
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-350m
* Dataset: futin/guess
* Config: en
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
|
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0.30851781368255615,
0.1788771152496338,
0.6591202020645142,
0.04076408967375755,
0.2714729309082031,
-0.11575794965028763,
0.3494724929332733,
-0.05570971593260765,
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-0.4253852367401123,
-0.3181881904602051,
-0.47490620613098145
] |
bigbio/ctebmsp
|
bigbio
| 2022-12-22T15:44:30Z | 41 | 0 |
[
"multilinguality:monolingual",
"language:es",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-11-13T22:08:04Z |
---
language:
- es
bigbio_language:
- Spanish
license: cc-by-nc-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_NC_4p0
pretty_name: CT-EBM-SP
homepage: http://www.lllf.uam.es/ESP/nlpmedterm_en.html
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for CT-EBM-SP
## Dataset Description
- **Homepage:** http://www.lllf.uam.es/ESP/nlpmedterm_en.html
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
### Ctebmsp Abstracts
The "abstracts" subset of the Clinical Trials for Evidence-Based Medicine in Spanish
(CT-EBM-SP) corpus contains 500 abstracts of clinical trial studies in Spanish,
published in journals with a Creative Commons license. Most were downloaded from
the SciELO repository and free abstracts in PubMed.
Abstracts were retrieved with the query:
Clinical Trial[ptyp] AND “loattrfree full text”[sb] AND “spanish”[la].
(Information collected from 10.1186/s12911-021-01395-z)
### Ctebmsp Eudract
The "abstracts" subset of the Clinical Trials for Evidence-Based Medicine in Spanish
(CT-EBM-SP) corpus contains 500 abstracts of clinical trial studies in Spanish,
published in journals with a Creative Commons license. Most were downloaded from
the SciELO repository and free abstracts in PubMed.
Abstracts were retrieved with the query:
Clinical Trial[ptyp] AND “loattrfree full text”[sb] AND “spanish”[la].
(Information collected from 10.1186/s12911-021-01395-z)
## Citation Information
```
@article{CampillosLlanos2021,
author = {Leonardo Campillos-Llanos and
Ana Valverde-Mateos and
Adri{'{a}}n Capllonch-Carri{'{o}}n and
Antonio Moreno-Sandoval},
title = {A clinical trials corpus annotated with {UMLS}
entities to enhance the access to evidence-based medicine},
journal = {{BMC} Medical Informatics and Decision Making},
volume = {21},
year = {2021},
url = {https://doi.org/10.1186/s12911-021-01395-z},
doi = {10.1186/s12911-021-01395-z},
biburl = {},
bibsource = {}
}
```
|
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RussianNLP/wikiomnia
|
RussianNLP
| 2023-04-07T06:43:59Z | 41 | 4 |
[
"task_categories:question-answering",
"size_categories:1M<n<10M",
"language:ru",
"license:apache-2.0",
"wikipedia",
"wikiomnia",
"squad",
"QA",
"arxiv:2204.08009",
"region:us"
] |
[
"question-answering"
] | 2022-12-16T16:03:40Z |
---
license: apache-2.0
dataset_info:
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features:
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dtype: string
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task_categories:
- question-answering
language:
- ru
tags:
- wikipedia
- wikiomnia
- squad
- QA
pretty_name: WikiOmnia
size_categories:
- 1M<n<10M
---
# Dataset Card for "Wikiomnia"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/RussianNLP](https://github.com/RussianNLP)
- **Paper:** [WikiOmnia: filtration and evaluation of the generated QA corpus on the whole Russian Wikipedia](https://arxiv.org/abs/2204.08009)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
We present the WikiOmnia dataset, a new publicly available set of QA-pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generative pipeline. The dataset includes every available article from Wikipedia for the Russian language. The WikiOmnia pipeline is available open-source and is also tested for creating SQuAD-formatted QA on other domains, like news texts, fiction, and social media. The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).
WikiOmnia consists of 2 parts:
1. the voluminous, automatically generated part: 15,9 million triplets consisting of the original article summary, a corresponding generated question and a generated answer;
2. the filtered part: the subsample of 3,5 million triplets, fully verified with automatic means
Wikiomnia adheres to a standard SQuAD format problem, resulting in triplets "text paragraph - question based on paragraph - answer from the paragraph", see the following example:
**Original Wikipedia paragraph**: Коити Масимо (яп. Масимо Ко:ити) — известный режиссёр аниме и основатель японской анимационной студии Bee Train. С
момента основания студии он руководит производством почти всех её картин, а также время от времени принимает участие в работе над анимацией и музыкой.
**English translation**: Koichi Mashimo is a famous anime director and the founder of the Japanese animation studio Bee Train. Since the creation of the studio, he directed almost all studio’s works, and he
also sometimes participates in art and sound tasks.
**Generated question (ruT5)**: Кто является основателем японской анимационной студии Bee Train?
**Generated answer (ruT5)**: Коити Масимо
**English QA translation**: Who is the founder of the Japanese animation studio Bee Train? Koichi Mashimo
## Dataset Creation
Models used for dataset generation:
- [ruT5](https://huggingface.co/sberbank-ai/ruT5-large) large fine-tuned on SberQuaD
- [ruGPT-3](https://huggingface.co/sberbank-ai/rugpt3xl) XL fine-tuned on SberQuaD
- [ruBERT](http://docs.deeppavlov.ai/en/master/features/models/squad.html) DeepPavlov tuned for QA tasks
Source: Wikipedia version March 2021
Special tokens: <[TEXT]>, <[QUESTION]>, <[ANSWER]>
The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-
large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).
## Additional Information
### Licensing Information
[Apache 2.0 license](https://github.com/RussianNLP/WikiOmnia/blob/main/LICENSE)
### Citation Information
```
@inproceedings{pisarevskaya-shavrina-2022-wikiomnia,
title = "{W}iki{O}mnia: filtration and evaluation of the generated {QA} corpus on the whole {R}ussian {W}ikipedia",
author = "Pisarevskaya, Dina and
Shavrina, Tatiana",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.gem-1.10",
pages = "125--135",
abstract = "The General QA field has been developing the methodology referencing the Stanford Question answering dataset (SQuAD) as the significant benchmark. Compiling factual questions datasets requires manual annotations, limiting the training data{'}s potential size. We present the WikiOmnia dataset, a new publicly available set of QA pairs and corresponding Russian Wikipedia article summary sections, composed with a fully automated generation and filtration pipeline. To ensure high quality of generated QA pairs, diverse manual and automated evaluation techniques were applied. The WikiOmnia pipeline is available open-source and is also tested for creating SQuAD-formatted QA on other domains, like news texts, fiction, and social media. The resulting dataset includes two parts: raw data on the whole Russian Wikipedia (7,930,873 QA pairs with paragraphs for ruGPT-3 XL and 7,991,040 QA pairs with paragraphs for ruT5-large) and cleaned data with strict automatic verification (over 160,000 QA pairs with paragraphs for ruGPT-3 XL and over 3,400,000 QA pairs with paragraphs for ruT5-large).",
}
```
### Contributions
Thanks to [@Deenochka](https://github.com/deenochka), [@TatianaShavrina](https://github.com/TatianaShavrina)
|
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] |
neulab/odex
|
neulab
| 2023-02-10T18:01:34Z | 41 | 8 |
[
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"language:es",
"language:ja",
"language:ru",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"text2text-generation",
"text-generation"
] | 2023-01-06T14:30:00Z |
---
license: cc-by-sa-4.0
task_categories:
- text2text-generation
- text-generation
language:
- en
- es
- ja
- ru
size_categories:
- n<1K
---
__ODEX__ is an Open-Domain EXecution-based NL-to-Code generation data benchmark.
It contains 945 samples with a total of 1,707 human-written test cases, covering intents in four different natural languages -- 439 in English, 90 in Spanish, 164 in Japanese, and 252 in Russian.
You can load the dataset by specifying a subset from *en, es, ja, ru* (by default the english subset *en* is loaded):
```python
from datasets import load_dataset
ds = load_dataset("neulab/odex", "ja", split="test")
```
If you find our dataset useful, please cite the paper
```
@article{wang2022execution,
title={Execution-Based Evaluation for Open-Domain Code Generation},
author={Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig},
journal={arXiv preprint arXiv:2212.10481},
year={2022}
}
```
|
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] |
jonathan-roberts1/SAT-6
|
jonathan-roberts1
| 2023-04-03T16:17:41Z | 41 | 0 |
[
"license:other",
"region:us"
] | null | 2023-02-03T18:13:10Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': barren land
'1': building
'2': grassland
'3': road
'4': trees
'5': water
splits:
- name: train
num_bytes: 120518797
num_examples: 81000
download_size: 142842069
dataset_size: 120518797
license: other
---
# Dataset Card for "SAT-6"
## Dataset Description
- **Paper** [Deepsat: a learning framework for satellite imagery](https://dl.acm.org/doi/pdf/10.1145/2820783.2820816)
- **Split** Test
### Split Information
This HuggingFace dataset repository contains just the 'Test' split.
### Licensing Information
Public Domain
## Citation Information
[https://dl.acm.org/doi/pdf/10.1145/2820783.2820816](https://dl.acm.org/doi/pdf/10.1145/2820783.2820816)
```
@inproceedings{basu2015deepsat,
title = {Deepsat: a learning framework for satellite imagery},
author = {Basu, Saikat and Ganguly, Sangram and Mukhopadhyay, Supratik and DiBiano, Robert and Karki, Manohar and Nemani, Ramakrishna},
year = 2015,
booktitle = {Proceedings of the 23rd SIGSPATIAL international conference on advances in geographic information systems},
pages = {1--10}
}
```
|
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emozilla/soda_synthetic_dialogue
|
emozilla
| 2023-02-07T03:54:33Z | 41 | 7 |
[
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|allenai/soda",
"language:en",
"license:mit",
"open-assistant",
"conversations-summarization",
"arxiv:2212.10465",
"region:us"
] |
[
"conversational"
] | 2023-02-07T01:55:49Z |
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- machine-generated
license:
- mit
multilinguality:
- monolingual
pretty_name: "SODA Synthetic Dialogue"
size_categories:
- 1M<n<10M
source_datasets:
- extended|allenai/soda
tags:
- open-assistant
- conversations-summarization
task_categories:
- conversational
task_ids:
- dialogue-generation
dataset_info:
features:
- name: conversation
dtype: string
config_name: soda_synthetic_dialogue_dialogue_modeling
splits:
- name: train
num_bytes: 1524941476
num_examples: 1191582
- name: test
num_bytes: 190565707
num_examples: 148968
- name: validation
num_bytes: 187161878
num_examples: 146346
download_size: 1952926579
dataset_size: 1902669061
---
# Dataset Card for 🥤SODA Synthetic Dialogue
## Dataset Description
- **Repository:**
[Code](https://github.com/LAION-AI/Open-Assistant/openassistant/datasets/soda_synthetic_dialogue/)
## Dataset Summary
🥤SODA Synthetic Dialogue is a set of synthetic dialogues between Assistant and
User. In each conversation, User asks Assistant to perform summarization or
story generation tasks based on a snippet of an existing dialogue, story, or
from a title or theme.
This data was created by synthesizing the dialogues in
[🥤Soda](https://huggingface.co/datasets/allenai/soda) and applying a set of
templates to generate the conversation. The original research paper can be found
[here](https://arxiv.org/pdf/2212.10465v1.pdf).
Example:
```
User: Can you write the next few lines of dialogue for this scene:
Cornell: I know what you're thinking, Dontrell. I can sense your feelings and know exactly what you're going to say or do next. You're thinking about how much you miss your family, and how you wish you could see them again. You're wondering if there's anything you can do to make that happen. And you're also feeling a little guilty, because you know that if you could see them again, it would only be for a short while before they died.
Dontrell: Cornell, how can you possibly know all that? It's like you can read my mind!
Assistant: Sure, the next dialogue for this scene could be:
Cornell: I told you, I can sense your feelings. It's like I have a connection to your thoughts and emotions. I'm not sure why or how it works, but ever since we met, I've been able to understand what you're thinking and feeling pretty easily.
Dontrell: Wow...that's amazing! And a little bit scary too. But it does explain why we've always been so close - even though we've only known each other for a few months.
Cornell: Yeah, I guess our connection is pretty strong. But don't worry, Dontrell - I'll never use it to manipulate or control you in any way. I promise."
Dontrell: That's a relief! I was starting to feel a little bit like you could read my mind, and it was kind of creepy. But if you promise not to use your abilities to control me, then I'm okay with it.
Cornell: I swear, Dontrell. I'll never use my abilities to control or manipulate you in any way. You have my word.
User: And a title?
Assistant: Sure, a title for this dialogue could be:
Cornell is intuitive. Cornell reads Dontrell's mind.
User: How about a short description?
Assistant: Sure, a short description for this dialogue could be:
Cornell knows what Dontrell is thinking. He can sense her feelings and know exactly what she is going to say or do next.
User: What would be one theme of this story?
Assistant: One theme of this story could be:
being intuitive
```
## Usage
The dataset contains one configuration, `dialogue_modeling`, which has a single
text `conversation` feature.
## Source data
The script to synthesize this dataset was originally created by
[ontocord](https://github.com/ontocord) in
[this Colab notebook](https://colab.research.google.com/drive/1Sw3px5dP8whdqT7QMNoqwmqIasZkMbJi?usp=sharing)
and prepared for Hugging Face by
[Jeffrey Quesnelle](https://github.com/jquesnelle/).
## Citation
Please cite our work if you find the resources in this repository useful:
```
@article{ontocord2023sodasynth,
author = {ontocord and Jeffrey Quesnelle},
title = {SODA Synthetic Dialogue},
year = {2023}
}
```
|
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heegyu/news-category-dataset
|
heegyu
| 2023-02-09T08:10:48Z | 41 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-02-09T08:08:22Z |
---
license: cc-by-4.0
---
Dataset from https://www.kaggle.com/datasets/rmisra/news-category-dataset
|
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HuggingFaceH4/helpful_instructions
|
HuggingFaceH4
| 2023-03-27T22:25:58Z | 41 | 8 |
[
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"instruct",
"human-feedback",
"region:us"
] |
[
"text-generation"
] | 2023-03-03T10:08:01Z |
---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- instruct
- human-feedback
pretty_name: Helpful Instructions
dataset_info:
- config_name: self_instruct
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: source
dtype: string
- name: config
dtype: string
splits:
- name: train
num_bytes: 24378246
num_examples: 82612
download_size: 12589487
dataset_size: 24378246
- config_name: super_natural_instructions
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: source
dtype: string
- name: config
dtype: string
splits:
- name: train
num_bytes: 43352923
num_examples: 50000
download_size: 22605900
dataset_size: 43352923
- config_name: prompt_source
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: source
dtype: string
- name: config
dtype: string
splits:
- name: train
num_bytes: 59843768
num_examples: 52657
download_size: 23607134
dataset_size: 59843768
- config_name: all
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: source
dtype: string
- name: config
dtype: string
splits:
- name: train
num_bytes: 127574937
num_examples: 185269
download_size: 58901460
dataset_size: 127574937
---
# Dataset Card for Helpful Instructions
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact: Lewis Tunstall**
### Dataset Summary
Helpful Instructions is a dataset of `(instruction, completion)` pairs that are derived from public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform. You can load the dataset as follows:
```python
from datasets import load_dataset
# Load all subsets
helpful_instructions = load_dataset("HuggingFaceH4/helpful_instructions", name="all")
# Load a single subset
helpful_instructions_subset = load_dataset("HuggingFaceH4/helpful_instructions", name="self_instruct")
```
### Supported Tasks and Leaderboards
This dataset can be used to fine-tune pretrained language models to follow instructions.
### Changelog
* March 5, 2023: `v1.0.0` release, with subsets from `HuggingFaceH4/self_instruct` (`self_instruct`, `super_natural_instructions`, `prompt_source`)
|
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mstz/bank
|
mstz
| 2023-04-15T11:16:43Z | 41 | 0 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"compas",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-03-23T00:56:08Z |
---
language:
- en
tags:
- compas
- tabular_classification
- binary_classification
- UCI
pretty_name: Bank
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- encoding
- subscription
---
# Bank
The [Bank dataset](https://archive.ics.uci.edu/ml/datasets/bank+marketing) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Potential clients are contacted by a bank during a second advertisement campaign.
This datasets records the customer, the interaction with the AD campaign, and if they subscribed to a proposed bank plan or not.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| subscription | Binary classification | Has the customer subscribed to a bank plan? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/bank", "subscription")["train"]
```
# Features
| **Name** |**Type** |
|-----------------------------------------------|-----------|
|`age` |`int64` |
|`job` |`string` |
|`marital_status` |`string` |
|`education` |`int8` |
|`has_defaulted` |`int8` |
|`account_balance` |`int64` |
|`has_housing_loan` |`int8` |
|`has_personal_loan` |`int8` |
|`month_of_last_contact` |`string` |
|`number_of_calls_in_ad_campaign` |`string` |
|`days_since_last_contact_of_previous_campaign` |`int16` |
|`number_of_calls_before_this_campaign` |`int16` |
|`successfull_subscription` |`int8` |
|
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nanakonoda/xnli_cm
|
nanakonoda
| 2023-04-18T13:58:12Z | 41 | 0 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:extended|xnli",
"language:en",
"language:de",
"language:fr",
"mode classification",
"aligned",
"code-mixed",
"region:us"
] |
[
"text-classification"
] | 2023-04-11T18:47:31Z |
---
annotations_creators:
- expert-generated
language:
- en
- de
- fr
language_creators:
- found
license: []
multilinguality:
- multilingual
pretty_name: XNLI Code-Mixed Corpus
size_categories:
- 1M<n<10M
source_datasets:
- extended|xnli
tags:
- mode classification
- aligned
- code-mixed
task_categories:
- text-classification
task_ids: []
dataset_info:
- config_name: de_ec
features:
- name: text
dtype: string
- name: label
dtype: int64
# class_label:
# names:
# '0': spoken
# '1': written
splits:
- name: train
num_bytes: 576
num_examples: 2490
- name: test
num_bytes: 194139776
num_examples: 1610549
- config_name: de_ml
features:
- name: text
dtype: string
- name: label
dtype: int64
# class_label:
# names:
# '0': spoken
# '1': written
splits:
- name: train
num_bytes: 576
num_examples: 2490
- name: test
num_bytes: 87040
num_examples: 332326
- config_name: fr_ec
features:
- name: text
dtype: string
- name: label
dtype: int64
# class_label:
# names:
# '0': spoken
# '1': written
splits:
- name: train
num_bytes: 576
num_examples: 2490
- name: test
num_bytes: 564416
num_examples: 2562631
- config_name: fr_ml
features:
- name: text
dtype: string
- name: label
dtype: int64
# class_label:
# names:
# '0': spoken
# '1': written
splits:
- name: train
num_bytes: 576
num_examples: 2490
- name: test
num_bytes: 361472
num_examples: 1259159
download_size: 1376728
dataset_size: 1376704
---
# Dataset Card for XNLI Code-Mixed Corpus
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
### Supported Tasks and Leaderboards
Binary mode classification (spoken vs written)
### Languages
- English
- German
- French
- German-English code-mixed by Equivalence Constraint Theory
- German-English code-mixed by Matrix Language Theory
- French-English code-mixed by Equivalence Constraint Theory
- German-English code-mixed by Matrix Language Theory
## Dataset Structure
### Data Instances
{
'text': "And he said , Mama , I 'm home",
'label': 0
}
### Data Fields
- text: sentence
- label: binary label of text (0: spoken 1: written)
### Data Splits
- de-ec
- train (English, German, French monolingual):
- test (German-English code-mixed by Equivalence Constraint Theory):
- de-ml:
- train (English, German, French monolingual):
- test (German-English code-mixed by Matrix Language Theory):
- fr-ec
- train (English, German, French monolingual):
- test (French-English code-mixed by Equivalence Constraint Theory):
- fr-ml:
- train (English, German, French monolingual):
- test (French-English code-mixed by Matrix Language Theory):
### Other Statistics
#### Average Sentence Length
- German
- train:
- test:
- French
- train:
- test:
#### Label Split
- train:
- 0:
- 1:
- test:
- 0:
- 1:
## Dataset Creation
### Curation Rationale
Using the XNLI Parallel Corpus, we generated a code-mixed corpus using CodeMixed Text Generator.
The XNLI Parallel Corpus is available here:
https://huggingface.co/datasets/nanakonoda/xnli_parallel
It was created from the XNLI corpus.
More information is available in the datacard for the XNLI Parallel Corpus.
Here is the link and citation for the original CodeMixed Text Generator paper.
https://github.com/microsoft/CodeMixed-Text-Generator
```
@inproceedings{rizvi-etal-2021-gcm,
title = "{GCM}: A Toolkit for Generating Synthetic Code-mixed Text",
author = "Rizvi, Mohd Sanad Zaki and
Srinivasan, Anirudh and
Ganu, Tanuja and
Choudhury, Monojit and
Sitaram, Sunayana",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-demos.24",
pages = "205--211",
abstract = "Code-mixing is common in multilingual communities around the world, and processing it is challenging due to the lack of labeled and unlabeled data. We describe a tool that can automatically generate code-mixed data given parallel data in two languages. We implement two linguistic theories of code-mixing, the Equivalence Constraint theory and the Matrix Language theory to generate all possible code-mixed sentences in the language-pair, followed by sampling of the generated data to generate natural code-mixed sentences. The toolkit provides three modes: a batch mode, an interactive library mode and a web-interface to address the needs of researchers, linguists and language experts. The toolkit can be used to generate unlabeled text data for pre-trained models, as well as visualize linguistic theories of code-mixing. We plan to release the toolkit as open source and extend it by adding more implementations of linguistic theories, visualization techniques and better sampling techniques. We expect that the release of this toolkit will help facilitate more research in code-mixing in diverse language pairs.",
}
```
### Source Data
XNLI Parallel Corpus
https://huggingface.co/datasets/nanakonoda/xnli_parallel
#### Original Source Data
XNLI Parallel Corpus was created using the XNLI Corpus.
https://github.com/facebookresearch/XNLI
Here is the citation for the original XNLI paper.
```
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin",
title = "XNLI: Evaluating Cross-lingual Sentence Representations",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
location = "Brussels, Belgium",
}
```
#### Initial Data Collection and Normalization
We removed all punctuation from the XNLI Parallel Corpus except apostrophes.
#### Who are the source language producers?
N/A
### Annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
N/A
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
N/A
### Licensing Information
N/A
### Citation Information
### Contributions
N/A
|
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bprateek/amazon_product_description
|
bprateek
| 2023-05-17T20:12:35Z | 41 | 2 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-04-23T13:43:41Z |
---
license: apache-2.0
---
|
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] |
TrajanovRisto/esg-sentiment
|
TrajanovRisto
| 2023-04-30T20:28:31Z | 41 | 4 |
[
"region:us"
] | null | 2023-04-30T20:28:28Z |
---
dataset_info:
features:
- name: Text
dtype: string
- name: Environmental Negative
dtype: int32
- name: Environmental Neutral
dtype: int32
- name: Environmental Positive
dtype: int32
- name: Governance Negative
dtype: int32
- name: Governance Neutral
dtype: int32
- name: Governance Positive
dtype: int32
- name: Social Negative
dtype: int32
- name: Social Neutral
dtype: int32
- name: Social Positive
dtype: int32
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 135470.12812960235
num_examples: 611
- name: test
num_bytes: 15076.871870397643
num_examples: 68
download_size: 80141
dataset_size: 150547.0
---
# Dataset Card for "esg-sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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MU-NLPC/Calc-ape210k
|
MU-NLPC
| 2023-10-30T15:56:39Z | 41 | 13 |
[
"license:mit",
"arxiv:2305.15017",
"arxiv:2009.11506",
"region:us"
] | null | 2023-05-22T14:20:16Z |
---
license: mit
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_chinese
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
splits:
- name: train
num_bytes: 111988047
num_examples: 195179
- name: validation
num_bytes: 1172933
num_examples: 1783
- name: test
num_bytes: 1157061
num_examples: 1785
download_size: 50827709
dataset_size: 114318041
- config_name: original-splits
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_chinese
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
splits:
- name: train
num_bytes: 111988047
num_examples: 195179
- name: validation
num_bytes: 2798479
num_examples: 4867
- name: test
num_bytes: 2793355
num_examples: 4867
download_size: 52234086
dataset_size: 117579881
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: original-splits
data_files:
- split: train
path: original-splits/train-*
- split: validation
path: original-splits/validation-*
- split: test
path: original-splits/test-*
---
# Dataset Card for Calc-ape210k
## Summary
This dataset is an instance of Ape210K dataset, converted to a simple HTML-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags:
- gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case)
- output: An output of the external tool
- result: The final answer to the mathematical problem (a number)
## Supported Tasks
The dataset is intended for training Chain-of-Thought reasoning **models able to use external tools** to enhance the factuality of their responses.
This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator.
## Construction Process
First, we translated the questions into English using Google Translate. Next, we parsed the equations and the results. We linearized
the equations into a sequence of elementary steps and evaluated them using a sympy-based calculator. We numerically compare the output
with the result in the data and remove all examples where they do not match (less than 3% loss in each split). Finally, we save the
chain of steps in the HTML-like language in the `chain` column. We keep the original columns in the dataset for convenience. We also perform
in-dataset and cross-dataset data-leak detection within [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
Specifically for Ape210k, we removed parts of the validation and test split, with around 1700 remaining in each.
You can read more information about this process in our [Calc-X paper](https://arxiv.org/abs/2305.15017).
## Data splits
The default config contains filtered splits with data leaks removed.
You can load it using:
```python
datasets.load_dataset("MU-NLPC/calc-ape210k")
```
In the `original-splits` config, the data splits are unfiltered and correspond to the original Ape210K dataset. See [ape210k dataset github](https://github.com/Chenny0808/ape210k) and [the paper](https://arxiv.org/abs/2009.11506) for more info.
You can load it using:
```python
datasets.load_dataset("MU-NLPC/calc-ape210k", "original-splits")
```
## Attributes
- **id** - id of the example
- **question** - the description of the math problem. Automatically translated from the `question_chinese` column into English using Google Translate
- **question_chinese** - the original description of the math problem in Chinese
- **chain** - linearized `equation`, sequence of arithmetic steps in HTML-like language that can be evaluated using our sympy-based calculator
- **result** - result as a string (can be an integer, float, or a fraction)
- **result_float** - result, converted to a float
- **equation** - a nested expression that evaluates to the correct answer
Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
## Related work
This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers.
- [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers
- [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF
- [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017)
- [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x)
Here are links to the original dataset:
- [**original Ape210k dataset and repo**](https://github.com/Chenny0808/ape210k)
- [**original Ape210k paper**](https://arxiv.org/abs/2009.11506)
## Licence
MIT, consistently with the original dataset.
## Cite
If you use this version of the dataset in research, please cite the [original Ape210k paper](https://arxiv.org/abs/2009.11506), and the [Calc-X paper](https://arxiv.org/abs/2305.15017) as follows:
```bibtex
@inproceedings{kadlcik-etal-2023-soft,
title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
month = dec,
year = "2023",
address = "Singapore, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2305.15017",
}
```
|
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csitfun/LogiCoT
|
csitfun
| 2023-10-09T07:54:13Z | 41 | 24 |
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"language:zh",
"license:cc-by-nc-nd-4.0",
"instruction-finetuning",
"arxiv:2305.12147",
"region:us"
] |
[
"text-generation"
] | 2023-05-24T11:13:44Z |
---
license: cc-by-nc-nd-4.0
task_categories:
- text-generation
language:
- en
- zh
tags:
- instruction-finetuning
pretty_name: logicot
size_categories:
- 100K<n<1M
---
The instructions and demonstrations for building formal logical reasoning capable Generative Large Language models. CoT rationales are generated with the GPT-4 API.
> For non-commercial research purposes only.
Update: Our updated paper has been accepted by the findings of EMNLP2023.
The dataset is hosted on the Huggingface Datasets. It is the only distribution channel we currently allow. **You can download data examples from our Github [Link](https://github.com/csitfun/LogiCoT)**
**Important**: To request the dataset, please
1. Submit an access request through your huggingface account.
2. Send an email to Hanmeng Liu at [email protected]. Please tell us your huggingface account username, your real name, org, and purpose. You need to guarantee that you will not share the data with others. We will approve your request after your info is provided.
`general_inference.jsonl`: English instruction tuning data for the general inference task
`general_inference_pruned`: a pruned version with a smaller size while more diverse
`mrc.jsonl`: English instruction tuning data for the logical reading comprehension task
`mrc_zh.jsonl`: Chinese instruction tuning data for the logical reading comprehension task
`entailmentbank.jsonl`: derived from the EntailmentBank data
`folio2instruction.jsonl`: derived from the FOLIO data
For more information, please refer to our preview Arxiv eprint paper - [LogiCoT: Logical Chain-of-Thought Instruction-tuning Data Collection with GPT-4](https://arxiv.org/abs/2305.12147)
## Seminal Data
* LogicInference
* EntailmentBank
* FOLIO
* ReClor
* LogiQA
## Instruction types
### General inference task
* Language to Logic
* One-Step Inference
* Inference Chains
### Multi-choice reading comprehension task
* Identify the Necessary Claim
* Strengthen an Argument
* Weaken an Argument
* Resolve a Situation
* Identify a Flaw in Arguments Reasoning
|
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projecte-aina/CaWikiTC
|
projecte-aina
| 2023-11-25T05:32:19Z | 41 | 1 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:automatically-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-sa-3.0",
"region:us"
] |
[
"text-classification"
] | 2023-05-26T13:22:43Z |
---
YAML tags:
annotations_creators:
- automatically-generated
language_creators:
- found
language:
- ca
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: cawikitc
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for CaWikiTC
## Dataset Description
- **Point of Contact:** [Irene Baucells de la Peña]([email protected])
### Dataset Summary
CaWikiTC (Catalan Wikipedia Text Classification) is a text classification dataset authomatically created by scraping Catalan Wikipedia article summaries and their associated thematic category. It contains 21002 texts (19952 and 1050 in the train and dev partitions, respectively) classified under 67 exclusive categories.
For the dataset creation, we selected all the Catalan Wikipedia article summaries from a previously fixed variety of subcategories, most of which are professional disciplines and social sciences-related fields. The texts that were originally associated with more than one category were discarded to avoid class overlappings.
This dataset was created as part of the experiments from [Entailment-based Task Transfer for Catalan Text Classification in Small Data Regimes](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6551). Its original purpose was to serve as a task transfer source to train an entailment model, which was then used to perform a different text classification task.
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Supported Tasks and Leaderboards
Text classification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Two json files (train and development splits).
### Data Fields
Each example contains the following 3 fields:
* text: Catalan Wikipedia article summary (string)
* label: topic
#### Example:
<pre>
[
{
'text': "Novum Organum és el títol de l'obra més important de Francis Bacon, publicada el 1620. Rep el seu nom perquè pretén ser una superació del tractat sobre lògica d'Aristòtil, anomenat Organon. Es basa a trobar la causa de tot fenomen per inducció, observant quan passa i quan no i extrapolant aleshores les condicions que fan que es doni. Aquest raonament va influir decisivament en la formació del mètode científic, especialment en la fase d'elaboració d'hipòtesis. També indica que el prejudici és l'enemic de la ciència, perquè impideix generar noves idees. Els prejudicis més comuns s'expliquen amb la metàfora de l'ídol o allò que és falsament adorat. Existeixen ídols de la tribu (comuns a tots els éssers humans per la seva naturalesa), de la caverna (procedents de l'educació), del fòrum (causats per un ús incorrecte del llenguatge) i del teatre (basats en idees anteriors errònies, notablement en filosofia).",
'label': 'Filosofia',
},
...
]
</pre>
#### Labels
* 'Administració', 'Aeronàutica', 'Agricultura', 'Antropologia', 'Arqueologia', 'Arquitectura', 'Art', 'Astronomia', 'Astronàutica', 'Biblioteconomia', 'Biotecnologia', 'Catàstrofes', 'Circ', 'Ciència militar', 'Ciència-ficció', 'Ciències ambientals', 'Ciències de la salut', 'Ciències polítiques', 'Conflictes', 'Cronometria', 'Cultura popular', 'Dansa', 'Dret', 'Ecologia', 'Enginyeria', 'Epidèmies', 'Esoterisme', 'Estris', 'Festivals', 'Filologia', 'Filosofia', 'Fiscalitat', 'Física', 'Geografia', 'Geologia', 'Gestió', 'Heràldica', 'Història', 'Humor', 'Indumentària', 'Informàtica', 'Jaciments paleontològics', 'Jocs', 'Lingüística', 'Llengües', 'Llocs ficticis', 'Matemàtiques', 'Metodologia', 'Mitologia', 'Multimèdia', 'Museologia', 'Nàutica', 'Objectes astronòmics', 'Pedagogia', 'Periodisme', 'Protestes', 'Pseudociència', 'Psicologia', 'Química', 'Robòtica', 'Ràdio', 'Seguretat laboral', 'Sociologia', 'Telecomunicacions', 'Televisió', 'Teologia', 'Ètica'
### Data Splits
Train and development splits were created in a stratified fashion, following a 95% and 5% proportion, respectively. The sizes of each split are the following:
* train.json: 19952 examples
* dev.json: 1050 examples
### Annotations
#### Annotation process
The crawled data contained the categories' annotations, which were then used to create this dataset with the mentioned criteria.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language modeCAls in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Irene Baucells ([email protected])
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
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Citation Information
|
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mask-distilled-onesec-cv12-each-chunk-uniq/chunk_250
|
mask-distilled-onesec-cv12-each-chunk-uniq
| 2023-05-31T00:08:35Z | 41 | 0 |
[
"region:us"
] | null | 2023-05-31T00:06:58Z |
---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 876129520.0
num_examples: 172060
download_size: 894655957
dataset_size: 876129520.0
---
# Dataset Card for "chunk_250"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
sukantan/nyaya-acts
|
sukantan
| 2023-06-08T06:56:32Z | 41 | 0 |
[
"region:us"
] | null | 2023-06-04T11:58:07Z |
---
dataset_info:
features:
- name: section_href
dtype: string
- name: act_enactment_date
dtype: timestamp[s]
- name: act_short_title
dtype: string
- name: act_long_title
dtype: string
- name: act_id
dtype: string
- name: ministry
dtype: string
- name: section_number
dtype: string
- name: section_title
dtype: string
- name: section_content
dtype: string
- name: section_part_no
dtype: string
- name: section_part
dtype: string
splits:
- name: train
num_bytes: 648190303
num_examples: 53788
download_size: 35939204
dataset_size: 648190303
---
# Dataset Card for "nyaya-acts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
clarin-knext/trec-covid-pl-qrels
|
clarin-knext
| 2023-06-07T08:11:44Z | 41 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:38:14Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
clarin-knext/scidocs-pl-qrels
|
clarin-knext
| 2023-06-07T08:09:59Z | 41 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:52:13Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
dmayhem93/agieval-sat-en-without-passage
|
dmayhem93
| 2023-06-18T17:31:43Z | 41 | 0 |
[
"license:mit",
"arxiv:2304.06364",
"region:us"
] | null | 2023-06-18T12:51:12Z |
---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 154762
num_examples: 206
download_size: 85136
dataset_size: 154762
license: mit
---
# Dataset Card for "agieval-sat-en-without-passage"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
|
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sukantan/nyaya-ae-all-mpnet-base-v2-ftlegal-v3
|
sukantan
| 2023-06-25T12:23:15Z | 41 | 0 |
[
"region:us"
] | null | 2023-06-25T10:20:41Z |
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splits:
- name: train
num_bytes: 165236736
num_examples: 53788
download_size: 199560362
dataset_size: 165236736
---
# Dataset Card for "nyaya-ae-all-mpnet-base-v2-ftlegal-v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ssbuild/alpaca_finance_en
|
ssbuild
| 2023-07-09T03:32:00Z | 41 | 3 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-07-09T03:29:57Z |
---
license: apache-2.0
---
|
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] |
AhmedBou/French_quotes
|
AhmedBou
| 2023-07-21T15:50:55Z | 41 | 0 |
[
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:fr",
"license:apache-2.0",
"region:us"
] |
[
"text-classification",
"text-generation"
] | 2023-07-19T11:44:35Z |
---
license: apache-2.0
task_categories:
- text-classification
- text-generation
language:
- fr
size_categories:
- 1K<n<10K
---
|
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OneFly7/llama2-sst2-fine-tuning
|
OneFly7
| 2023-08-08T07:03:26Z | 41 | 2 |
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
[
"text-classification"
] | 2023-07-29T19:28:23Z |
---
dataset_info:
features:
- name: label_text
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 23202578
num_examples: 67349
- name: validation
num_bytes: 334716
num_examples: 872
download_size: 4418625
dataset_size: 23537294
task_categories:
- text-classification
language:
- en
---
# Dataset Card for "llama2-sst2-finetuning"
## Dataset Description
The Llama2-sst2-fine-tuning dataset is designed for supervised fine-tuning of the LLaMA V2 based on the GLUE SST2 for sentiment analysis classification task.
We provide two subsets: training and validation.
To ensure the effectiveness of fine-tuning, we convert the data into the prompt template for LLaMA V2 supervised fine-tuning, where the data will follow this format:
```
<s>[INST] <<SYS>>
{System prompt}
<</SYS>>
{User prompt} [/INST] {Label} </s>.
```
The feasibility of this dataset has been tested in supervised fine-tuning on the meta-llama/Llama-2-7b-hf model.
Note. For the sake of simplicity, we have retained only one new column of data ('text').
## Other Useful Links
- [Get Llama 2 Prompt Format Right](https://www.reddit.com/r/LocalLLaMA/comments/155po2p/get_llama_2_prompt_format_right/)
- [Fine-Tune Your Own Llama 2 Model in a Colab Notebook](https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32)
- [Instruction fine-tuning Llama 2 with PEFT’s QLoRa method](https://medium.com/@ud.chandra/instruction-fine-tuning-llama-2-with-pefts-qlora-method-d6a801ebb19)
- [GLUE SST2 Dataset](https://www.tensorflow.org/datasets/catalog/glue#gluesst2)
<!--[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)-->
|
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] |
FunDialogues/customer-service-robot-support
|
FunDialogues
| 2023-08-28T23:39:18Z | 41 | 0 |
[
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"fictitious dialogues",
"prototyping",
"customer service",
"region:us"
] |
[
"question-answering",
"conversational"
] | 2023-08-10T05:41:46Z |
---
license: apache-2.0
task_categories:
- question-answering
- conversational
language:
- en
tags:
- fictitious dialogues
- prototyping
- customer service
pretty_name: customer-service-robot-support
size_categories:
- n<1K
---
# fun dialogues
A library of fictitious dialogues that can be used to train language models or augment prompts for prototyping and educational purposes. Fun dialogues currently come in json and csv format for easy ingestion or conversion to popular data structures. Dialogues span various topics such as sports, retail, academia, healthcare, and more. The library also includes basic tooling for loading dialogues and will include quick chatbot prototyping functionality in the future.
Visit the Project Repo: https://github.com/eduand-alvarez/fun-dialogues/
# This Dialogue
Comprised of fictitious examples of dialogues between a customer encountering problems with a robotic arm and a technical support agent. Check out the example below:
```
"id": 1,
"description": "Robotic arm calibration issue",
"dialogue": "Customer: My robotic arm seems to be misaligned. It's not picking objects accurately. What can I do? Agent: It appears that the arm may need recalibration. Please follow the instructions in the user manual to reset the calibration settings. If the problem persists, feel free to contact us again."
```
# How to Load Dialogues
Loading dialogues can be accomplished using the fun dialogues library or Hugging Face datasets library.
## Load using fun dialogues
1. Install fun dialogues package
`pip install fundialogues`
2. Use loader utility to load dataset as pandas dataframe. Further processing might be required for use.
```
from fundialogues import dialoader
# load as pandas dataframe
bball_coach = dialoader('"FunDialogues/customer-service-robot-support")
```
## Loading using Hugging Face datasets
1. Install datasets package
2. Load using datasets
```
from datasets import load_dataset
dataset = load_dataset("FunDialogues/customer-service-robot-support")
```
## How to Contribute
If you want to contribute to this project and make it better, your help is very welcome. Contributing is also a great way to learn more about social coding on Github, new technologies and and their ecosystems and how to make constructive, helpful bug reports, feature requests and the noblest of all contributions: a good, clean pull request.
### Contributing your own Lifecycle Solution
If you want to contribute to an existing dialogue or add a new dialogue, please open an issue and I will follow up with you ASAP!
### Implementing Patches and Bug Fixes
- Create a personal fork of the project on Github.
- Clone the fork on your local machine. Your remote repo on Github is called origin.
- Add the original repository as a remote called upstream.
- If you created your fork a while ago be sure to pull upstream changes into your local repository.
- Create a new branch to work on! Branch from develop if it exists, else from master.
- Implement/fix your feature, comment your code.
- Follow the code style of the project, including indentation.
- If the component has tests run them!
- Write or adapt tests as needed.
- Add or change the documentation as needed.
- Squash your commits into a single commit with git's interactive rebase. Create a new branch if necessary.
- Push your branch to your fork on Github, the remote origin.
- From your fork open a pull request in the correct branch. Target the project's develop branch if there is one, else go for master!
If the maintainer requests further changes just push them to your branch. The PR will be updated automatically.
Once the pull request is approved and merged you can pull the changes from upstream to your local repo and delete your extra branch(es).
And last but not least: Always write your commit messages in the present tense. Your commit message should describe what the commit, when applied, does to the code – not what you did to the code.
# Disclaimer
The dialogues contained in this repository are provided for experimental purposes only. It is important to note that these dialogues are assumed to be original work by a human and are entirely fictitious, despite the possibility of some examples including factually correct information. The primary intention behind these dialogues is to serve as a tool for language modeling experimentation and should not be used for designing real-world products beyond non-production prototyping.
Please be aware that the utilization of fictitious data in these datasets may increase the likelihood of language model artifacts, such as hallucinations or unrealistic responses. Therefore, it is essential to exercise caution and discretion when employing these datasets for any purpose.
It is crucial to emphasize that none of the scenarios described in the fun dialogues dataset should be relied upon to provide advice or guidance to humans. These scenarios are purely fictitious and are intended solely for demonstration purposes. Any resemblance to real-world situations or individuals is entirely coincidental.
The responsibility for the usage and application of these datasets rests solely with the individual or entity employing them. By accessing and utilizing these dialogues and all contents of the repository, you acknowledge that you have read and understood this disclaimer, and you agree to use them at your own discretion and risk.
|
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EgilKarlsen/AA_BERT_Baseline
|
EgilKarlsen
| 2023-08-23T03:37:43Z | 41 | 0 |
[
"region:us"
] | null | 2023-08-11T00:42:36Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
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num_bytes: 80318780.21618997
num_examples: 26057
- name: test
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num_examples: 8686
download_size: 147064679
dataset_size: 107092867.28977722
---
# Dataset Card for "AA_BERT_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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judy93536/pharsebank_5k
|
judy93536
| 2023-09-13T00:12:28Z | 41 | 0 |
[
"region:us"
] | null | 2023-09-10T03:50:03Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 542147
num_examples: 3999
- name: test
num_bytes: 137048
num_examples: 999
download_size: 379517
dataset_size: 679195
---
# Dataset Card for "pharsebank_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
yentinglin/ntu_adl_recitation
|
yentinglin
| 2023-09-21T02:18:47Z | 41 | 0 |
[
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-classification"
] | 2023-09-21T00:57:42Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- en
---
|
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] |
acrastt/EverythingLM-V3-ShareGPT
|
acrastt
| 2023-10-24T22:18:47Z | 41 | 2 |
[
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] | null | 2023-09-25T23:08:51Z |
---
license: mit
language:
- en
pretty_name: Everything-V3-
size_categories:
- 1K<n<10K
---
<a href="https://www.buymeacoffee.com/acrastt" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
[EverythingLM V3 Data](https://huggingface.co/datasets/totally-not-an-llm/EverythingLM-data-V3) converted to ShareGPT format.
|
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open-phi/programming_books_llama
|
open-phi
| 2023-10-04T18:02:56Z | 41 | 11 |
[
"region:us"
] | null | 2023-10-03T18:27:59Z |
---
dataset_info:
features:
- name: topic
dtype: string
- name: outline
sequence: string
- name: concepts
sequence: string
- name: queries
sequence: string
- name: context
sequence: string
- name: markdown
dtype: string
- name: model
dtype: string
splits:
- name: train
num_bytes: 1677240291
num_examples: 111048
download_size: 631279270
dataset_size: 1677240291
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "programming_books_llama"
400M tokens of programming books generated by gpt-3.5 (70M tokens) and a finetuned codellama 34b. The gpt-3.5 data is extremely high quality. The llama data has lower quality and shorter length, but is still good. This was generated with the [textbook quality](https://github.com/VikParuchuri/textbook_quality) repo.
|
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alexandrainst/nst-da
|
alexandrainst
| 2023-10-05T14:27:00Z | 41 | 3 |
[
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"size_categories:100K<n<1M",
"language:da",
"license:cc0-1.0",
"region:us"
] |
[
"automatic-speech-recognition",
"text-to-speech"
] | 2023-10-05T11:27:17Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: age
dtype: int64
- name: sex
dtype: string
- name: dialect
dtype: string
- name: recording_datetime
dtype: string
splits:
- name: train
num_bytes: 55199435558.0
num_examples: 182605
- name: test
num_bytes: 8894080220.0
num_examples: 54747
download_size: 5358057252
dataset_size: 64093515778.0
size_categories:
- 100K<n<1M
license: cc0-1.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- da
pretty_name: NST-da
---
# Dataset Card for NST-da
## Dataset Description
- **Repository:** <https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-55/>
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:[email protected])
- **Size of downloaded dataset files:** 5.36 GB
- **Size of the generated dataset:** 64.09 GB
- **Total amount of disk used:** 69.45 GB
### Dataset Summary
This dataset is an upload of the [NST Danish ASR Database (16 kHz) – reorganized](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-55/).
The training and test splits are the original ones.
### Supported Tasks and Leaderboards
Training automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 5.36 GB
- **Size of the generated dataset:** 64.09 GB
- **Total amount of disk used:** 69.45 GB
An example from the dataset looks as follows.
```
{
'audio': {
'path': 'dk14x404-05072000-1531_u0008121.wav',
'array': array([ 0.00265503, 0.00248718, 0.00253296, ..., -0.00030518,
-0.00035095, -0.00064087]),
'sampling_rate': 16000
},
'text': 'Desuden er der en svømmeprøve, en fremmedsprogstest samt en afsluttende samtale.',
'speaker_id': 404,
'age': 24,
'sex': 'Female',
'dialect': 'Storkøbenhavn',
'recording_datetime': '2000-07-05T15:31:14'
}
```
### Data Fields
The data fields are the same among all splits.
- `audio`: an `Audio` feature.
- `text`: a `string` feature.
- `speaker_id`: an `int64` feature.
- `age`: an `int64` feature.
- `sex`: a `string` feature.
- `dialect`: a `string` feature.
- `recording_datetime`: a `string` feature.
### Dataset Statistics
There are 183,205 samples in the training split, and 54,747 samples in the test split.
#### Speakers
There are 539 unique speakers in the training dataset and 56 unique speakers in the test dataset, where 54 of them are also present in the training set.
#### Age Distribution
#### Dialect Distribution
#### Sex Distribution
#### Transcription Length Distribution
## Dataset Creation
### Curation Rationale
There are not many large-scale ASR datasets in Danish.
### Source Data
The data originates from the now bankrupt company Nordisk språkteknologi (NST), whose data was transferred to the National Library of Norway, who subsequently released it into the public domain.
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) reorganised the dataset and uploaded it to the Hugging Face Hub.
### Licensing Information
The dataset is licensed under the [CC0
license](https://creativecommons.org/share-your-work/public-domain/cc0/).
|
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atom92/medical_healthwa_3.0
|
atom92
| 2023-10-15T12:53:16Z | 41 | 0 |
[
"region:us"
] | null | 2023-10-15T12:53:13Z |
---
dataset_info:
features:
- name: text
struct:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2710809
num_examples: 7360
download_size: 586464
dataset_size: 2710809
---
# Dataset Card for "medical_healthwa_3.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ostapeno/qa-openai_batched_icl0_clen512_maxD-1_maxC2500_0_cleaned
|
ostapeno
| 2023-10-26T18:47:50Z | 41 | 0 |
[
"region:us"
] | null | 2023-10-26T18:47:28Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: docno
dtype: string
- name: subject
dtype: string
- name: icl_examples
dtype: 'null'
- name: author_instr
dtype: string
- name: instruction
dtype: string
- name: response
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splits:
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num_bytes: 39657535.86953515
num_examples: 13565
- name: machine_learning
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- name: high_school_physics
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- name: college_biology
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- name: high_school_government_and_politics
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- name: security_studies
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- name: sociology
num_bytes: 36140542.45626196
num_examples: 12362
download_size: 55458289
dataset_size: 380428763.0
---
# Dataset Card for "qa-openai_batched_icl0_clen512_maxD-1_maxC2500_0_cleaned_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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