datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
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| tags
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stringlengths 19
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| embedding
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|---|---|---|---|---|---|---|---|---|---|
acheong08/nsfw_reddit
|
acheong08
| 2023-04-09T13:44:10Z | 49 | 10 |
[
"license:openrail",
"region:us"
] | null | 2023-03-25T08:23:53Z |
---
license: openrail
---
|
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slvnwhrl/tenkgnad-clustering-s2s
|
slvnwhrl
| 2023-04-24T12:04:03Z | 49 | 0 |
[
"size_categories:10K<n<100K",
"language:de",
"license:cc-by-nc-sa-4.0",
"embeddings",
"clustering",
"benchmark",
"arxiv:2210.07316",
"region:us"
] | null | 2023-04-21T14:03:22Z |
---
license: cc-by-nc-sa-4.0
language:
- de
tags:
- embeddings
- clustering
- benchmark
size_categories:
- 10K<n<100K
---
This dataset can be used as a benchmark for clustering word embeddings for <b>German</b>.
The datasets contains news article titles and is based on the dataset of the [One Million Posts Corpus](https://ofai.github.io/million-post-corpus/) and [10kGNAD](https://github.com/tblock/10kGNAD). It contains 10'267 unique samples, 10 splits with 1'436 to 9'962 samples and 9 unique classes. Splits are built similarly to MTEB's [TwentyNewsgroupsClustering](https://huggingface.co/datasets/mteb/twentynewsgroups-clustering) ([Paper](https://arxiv.org/abs/2210.07316)).
Have a look at [German Text Embedding Clustering Benchmark](https://github.com/ClimSocAna/tecb-de) for more infos, datasets and evaluation results.
|
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bloyal/small-uniref30
|
bloyal
| 2023-05-04T22:13:06Z | 49 | 0 |
[
"task_categories:fill-mask",
"size_categories:1K<n<10K",
"license:cc-by-4.0",
"region:us"
] |
[
"fill-mask"
] | 2023-05-04T21:50:38Z |
---
license: cc-by-4.0
dataset_info:
features:
- name: id
dtype: int64
- name: num
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1067207.070393368
num_examples: 4096
- name: test
num_bytes: 167427.70557437633
num_examples: 640
- name: validation
num_bytes: 169382.9274292743
num_examples: 640
download_size: 1368501
dataset_size: 1404017.7033970184
task_categories:
- fill-mask
size_categories:
- 1K<n<10K
---
|
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Deojoandco/reddit-ah-dialogturns-annotations
|
Deojoandco
| 2023-06-15T03:57:27Z | 49 | 0 |
[
"region:us"
] | null | 2023-06-12T07:17:00Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: speaker
dtype: string
- name: text
dtype: string
- name: annotation
dtype: string
splits:
- name: train
num_bytes: 3772164
num_examples: 16055
- name: validation
num_bytes: 376937
num_examples: 1641
- name: test
num_bytes: 360334
num_examples: 1559
download_size: 0
dataset_size: 4509435
---
# Dataset Card for "reddit-ah-dialogturns-annotations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
joonhok-exo-ai/korean_law_open_data_precedents
|
joonhok-exo-ai
| 2023-07-05T08:43:35Z | 49 | 3 |
[
"size_categories:10K<n<100K",
"language:ko",
"license:openrail",
"legal",
"region:us"
] | null | 2023-06-29T12:51:31Z |
---
language:
- ko
tags:
- legal
size_categories:
- 10K<n<100K
license: openrail
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [김준호](mailto:[email protected])
### Dataset Summary
[법제처 국가법령 공동활용 센터](https://open.law.go.kr/LSO/main.do)에서 제공되는 전체 판례 데이터셋입니다.
## Dataset Structure
### Data Instances
개별 데이터의 모양은 아래와 같습니다.
판례 본문 조회 API의 출력 결과 필드를 대체로 따랐으나, 그 중 "법원종류코드" 와 "사건종류코드"는 제외했고 "판시유형" 필드는 실제 응답에서는 "판결유형"이어서 실제 응답 값대로 사용하였습니다. 마지막으로 "판례내용" 필드는 "전문" 으로 대체하였습니다.
```
{
'판례정보일련번호': 101924
'사건명': '손해배상'
'사건번호': '85다카1594'
'선고일자': 19860722,
'선고': '선고'
'법원명': '대법원'
'사건종류명': '민사'
'판결유형': '판결'
'판시사항': '가. 미성년자가 부모의 개호를 받을 수 있는 경우, 손해로서의 개호인 비용 / 나. 호프만식계산법에 의한 일실이익 산정의 적부 다. 연별 호프만식계산법에 의하여 중간이자를 공제하는 경우, 단리연금 현가율이 20을 넘는 경우의 일실이익 산정방법'
'판결요지': '가. 신체의 부자유로 인하여 개호인의 조력을 받을 필요가 있는 경우에는 비록 피해자가 미성년자이고 그의 부모가 개호를 할 수 있는 형편에 있다 하더라도 반드시 그 부모의 개호를 받아야 한다고 단정할 수 없음은 물론, 가사 그 부모의 개호를 받게 된다고 하더라도 이로 인하여 피해자가 입는 손해는 특별한 사정이 없는 한 통상의 개호인 비용 전액이다. 나. 호프만식계산법에 의하여 중간이자를 공제하여 장래의 일실이익의 현가를 산정하는 것은 위법한 것이 아니다. 다. 연별 호프만식계산법에 의하여 중간이자를 공제하는 경우에 단리연금현가율이 20을 넘는 경우에는 그 단리연금현가율을 그대로 적용하여 그 현가를 산정하게 되면 현가로 받게 되는 금액의 이자가 매월 입게 되는 손해액보다 많게 되어 손해액보다 더 많은 금원을 배상하게 되는 불합리한 결과를 가져오게 되므로 그 단리연금현가율이 결과적으로 20을 넘는 경우에 있어서는 그 수치표상의 단리연금현가율이 얼마인지를 불문하고 모두 20을 적용 계산함으로써 피해자가 과잉배상을 받는 일이 없도록 하여야 한다.'
'참조조문': '가.나.다. 민법 제763조'
'참조판례': '나. 대법원 1981.9.22 선고 81다588 판결, 1985.10.22 선고 85다카819 판결 / 다. 대법원 1985.10.22 선고 85다카819 판결, 1986.3.25 선고 85다카2375 판결'
'판결유형': '판결'
'전문': '【원고, 피상고인】 (...이하 생략...)'
}
```
### Data Fields
다른 필드들은 특별한 설명이 필요 없겠으나, "선고일자" 필드의 값은 스트링이 아니고 숫자입니다. 또, 일부 데이터의 "선고일자" 필드 값에는 월, 일 정보가 누락되고 연 정보만 남아 있어서 자리수가 4자리인 경우도 있습니다.
그리고 "사건명" 등 일부 필드는 값이 없는 경우도 있으니 참고 바랍니다.
## Dataset Creation
### Curation Rationale
이 데이터셋의 판례 데이터들은 공동활용 API를 통해서도 접근 가능하지만,
1. API 방식으로는 전체 데이터를 순회하는 것이 까다롭고
2. API 응답 데이터를 매번 파싱하고 전처리하는 번거로움이 있으며
3. 일부 API 응답 데이터에 있는 오류를 미리 정제하기 위하여
이 데이터셋을 만들게 되었습니다.
### Source Data
#### Initial Data Collection and Normalization
이 데이터셋은 국가법령 공동활용 센터의 "판례 목록 조회 API"와 "판례 본문 조회 API"를 이용하여 데이터를 수집하였습니다.
먼저 판례 목록 조회 API를 호출해 판례정보 일련번호들을 수집한 뒤, 각각의 일련번호로 판례 본문 조회 API를 호출하여 판례 데이터를 수집하였습니다.
판례 본문을 조회할 때는 XML과 HTML 두 가지 형식으로 요청할 수 있는데, 데이터의 완결성 검증 및 정제 작업을 위해
전체 데이터에 대해 두 가지 형식으로 모두 요청을 보낸 뒤 두 응답 데이터를 비교해 보았고, 일부 데이터에서 요청 형식에 따라
데이터 값이 다른 것을 확인하였습니다.
예를 들어 판례정보 일련번호가 152179인 판례 데이터를 XML과 HTML 형식으로 요청했을 때 "전문" 중 "【원심판결】" 부분은 각각 아래와 같습니다.
XML 형식으로 요청했을 때:
```
"1. 서울중앙지방법원 2009. 4. 3. 선고 2009고합167 판결(이하 ‘제1원심판결’이라고 한다) / 2. 서울중앙지방법원 2009. 5. 8. 선고 2009고합416 판결(이하 ‘제2원심판결’이라고 한다)"
```
HTML 형식으로 요청했을 때:
```
서울중앙지방법원 2009. 4. 3. 선고 2009고합167 판결
```
이렇게 요청 형식에 따라 "【원심판결】" 부분이 다른 데이터가 수십건 있었고 이 데이터셋에는 더 많은 정보를 담고 있는 데이터로(위 사례에서는 XML 형식 데이터) 사용하였습니다.
그 밖에도 두 가지 형식 모두에서 데이터 자체에 잘못된 데이터가 포함되는 등(법령 하이퍼링크 포맷이 깨진 경우, 익명화 포맷이 잘못된 경우 등) 오류가 있는 경우들이
몇 건 있었는데 이 데이터들은 수작업으로 수정하였습니다.
마지막으로 일부 데이터는 이미지를 포함하고 있는 경우가 있었는데 이미지들은 전부 생략하고 텍스트 부분만 포함하였습니다.
본문 데이터에 오류가 있어 수작업으로 수정한 데이터 목록: 212537, 188351, 188019, 200567
이미지를 포함하고 있는 데이터 목록:
184135,
182916,
186027,
185375,
184151,
184597,
186156,
184655,
185123,
198440,
197577
## Additional Information
### Dataset Curators
김준호([링크드인](https://www.linkedin.com/in/joonho-kim/)): 이 데이터셋은 인공지능 법률 서비스를 만들고 있는 제가 직접 필요해서 만들게 되었습니다.
### Contributions
혹시 데이터 중 잘못된 부분을 발견하신 분은 [[email protected]](mailto:[email protected])로 연락 주시면
확인 후 반영하겠습니다.
|
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fhirfly/medicalquestions
|
fhirfly
| 2023-10-28T17:54:21Z | 49 | 4 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"medical",
"region:us"
] |
[
"text-classification"
] | 2023-07-13T16:46:49Z |
---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- medical
pretty_name: FhirFly Medical Questions
size_categories:
- 10K<n<100K
---
# 🤗 Dataset Card: fhirfly/medicalquestions
## Dataset Overview
- Dataset name: fhirfly/medicalquestions
- Dataset size: 25,102 questions
- Labels: 1 (medical), 0 (non-medical)
- Distribution: Evenly distributed between medical and non-medical questions
## Dataset Description
The fhirfly/medicalquestions dataset is a collection of 25,102 questions labeled as either medical or non-medical. The dataset aims to provide a diverse range of questions covering various medical and non-medical domains.
The questions in the dataset have been manually labeled by domain experts based on the context and content of each question. Each question is assigned a label of 1 if it is determined to be a medical question and a label of 0 if it is classified as a non-medical question.
## Dataset Structure
The dataset consists of a single file containing the following columns:
- **Text**: The text of the question.
- **Label**: The label assigned to each question, either 1 (medical) or 0 (non-medical).
The questions are evenly distributed between medical and non-medical categories, ensuring a balanced dataset for training and evaluation.
## Potential Biases
Efforts have been made to ensure that the dataset is representative of various medical and non-medical topics. However, it is important to acknowledge that biases may exist in the dataset due to the subjective nature of labeling questions. Biases could be present in terms of the types of questions included, the representation of certain medical conditions or non-medical topics, or the labeling process itself.
It is recommended to perform thorough evaluation and analysis of the dataset to identify and mitigate potential biases during model training and deployment. Care should be taken to address any biases to ensure fair and unbiased predictions.
## Dataset Quality
The fhirfly/medicalquestions dataset has undergone manual labeling by domain experts, which helps maintain a high level of quality and accuracy. However, human labeling is not entirely immune to errors or subjectivity.
To ensure the quality of the dataset, a thorough review process has been conducted to minimize errors and maintain consistency in labeling. Nonetheless, it is advisable to validate and verify the data as part of your specific use case to ensure it meets your requirements.
## Data License
The fhirfly/medicalquestions dataset is released under the MIT license. Please refer to the license file accompanying the dataset for more information on its usage and any restrictions that may apply.
## Dataset Citation
If you use the fhirfly/medicalquestions dataset in your work, please cite it as:
```
@dataset{fhirfly/medicalquestions,
title = {fhirfly/medicalquestions},
author = {fhirfly},
year = {2023},
publisher = {Hugging Face},
version = {1.0.0},
url = {https://huggingface.co/datasets/fhirfly/medicalquestions}
}
```
|
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] |
pykeio/oshichats-v1-2308
|
pykeio
| 2023-09-06T23:07:19Z | 49 | 3 |
[
"task_categories:text-classification",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:token-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-nc-sa-4.0",
"livestream",
"stream",
"chat",
"messages",
"vtuber",
"vtubers",
"region:us"
] |
[
"text-classification",
"conversational",
"text-generation",
"token-classification"
] | 2023-08-03T14:24:05Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- conversational
- text-generation
- token-classification
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
tags:
- livestream
- stream
- chat
- messages
- vtuber
- vtubers
pretty_name: OSHIChats v1
size_categories:
- 1M<n<10M
---
## OSHIChats v1 (August 2023)
OSHIChats v1 is a dataset of 8.06 million high-quality filtered English chat messages collected from various [VTuber](https://en.wikipedia.org/wiki/VTuber) live streams.
Compared to our previous dataset, [pykeio/vtuber-chats-2023-filtered-en-8.7M](https://huggingface.co/datasets/pykeio/vtuber-chats-2023-filtered-en-8.7M), we make the following improvements:
- Include stream topic information
- Far more accurate nickname detection using NLP
- Previously we did not match names like "dad" (nickname for Mori Calliope) or "mom" (nickname for Nina Kosaka) because they were too general. Now, we analyze the context and other information about the stream to determine whether to match such nicknames.
- Detect and normalize fan names like takodachi or pentomo
## Usage
Once you gain access to the dataset, you'll also need to log in to Hugging Face CLI with `huggingface-cli login`.
```py
from datasets import load_dataset
chats_dataset = load_dataset('pykeio/oshichats-v1-2308', split='train', revision='refs/convert/parquet')
chats_dataset[0]
# {'liver': 'FgXWZOUZA2oYHNr6qDmsTQ', 'stream': {'id': 'JHBv4BA_Y84', 'topic': 'Twisted_Wonderland'}, 'is_super': False, 'message': "i think i've grown to dislike them ", 'author': 'chxrry_head', 'time': [1660106235135797, 2126652]}
```
## Samples
```json
{
"liver": "kieJGn3pgJikVW8gmMXE2w",
"stream": {
"id": "dMUhbAcI5gk",
"topic": "minecraft"
},
"is_super": false,
"message": "yay <|liver:bW9t|> is streaming while I'm awake!",
"author": "Redribbon Vicky",
"time": [1651976493761550, 44936]
}
{
"liver": "yl1z3jo3XHR1riLFKG5UAg",
"stream": {
"id": "TgEX7HFqTYc",
"topic": "Donkey_Kong"
},
"is_super": false,
"message": "Stop running <|liver:QW1l|><|:ameHeh:|><|:ameHeh:|><|:ameHeh:|>",
"author": "Anon",
"time": [1616291612238864, 889273]
}
```
## Data fields
- `liver`: ID of the YouTube channel hosting the stream which the chat message came from.
- `stream`: Information about the stream.
- `id`: Video ID of the YouTube stream.
- `topic`: Topic of the stream (or `null` if a topic could not be determined). This can be things like `talk`, `Minecraft`, `Singing`, `GTA`, `Asmr`, etc.
- `is_super`: Whether or not the message is a Superchat (donation).
- `message`: Contents of the message. For consistency and ease of use on downstream tasks, we replace certain words with easily matchable special tokens:
* `<|liver:{b64}|>`: The substring refers to the host of the stream.
* `<|liver-fans:{b64}|>`: The substring refers to a nickname given to the fanbase of the host of the stream, e.g. aloupeeps or takodachis.
* `<|known-collaborator:{channelID}:{b64}|>`: The substring refers to a fellow VTuber that is present in the stream.
* `<|maybe-collaborator:{channelID}:{b64}|>`: The substring refers to a fellow VTuber that may or may not be part of the stream.
* `<|collaborator-fans:{channelID}:{b64}|>`: The substring refers to the fanbase of a collaborator present in the stream.
* `<|:{emote}:|>`: Represents a channel emote.
* Note that `channelID` is a YouTube channel ID, and `b64` is the original substring encoded as base64.
- `author`: The username of the author.
- `time`: A tuple containing the Unix timestamp of when the message was sent, and the relative time since the start of the stream.
## License
Licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/); you must give attribution, you may not use the dataset for commercial purposes, and you must distribute any transformations or copies of the dataset under the same license. [Contact us](mailto:[email protected]) for alternative/commercial licensing.
|
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] |
InstaDeepAI/instanovo_ninespecies_exclude_yeast
|
InstaDeepAI
| 2023-09-15T13:16:02Z | 49 | 1 |
[
"license:cc0-1.0",
"region:us"
] | null | 2023-09-15T09:29:15Z |
---
license: cc0-1.0
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: sequence
dtype: string
- name: modified_sequence
dtype: string
- name: precursor_mz
dtype: float64
- name: precursor_charge
dtype: int64
- name: mz_array
sequence: float64
- name: intensity_array
sequence: float32
splits:
- name: train
num_bytes: 839098224
num_examples: 499402
- name: validation
num_bytes: 49792990
num_examples: 28572
- name: test
num_bytes: 45505134
num_examples: 27142
download_size: 1119691599
dataset_size: 934396348
---
# Dataset Card for Nine-Species excluding Yeast
Dataset used for the baseline comparison of InstaNovo to other models.
## Dataset Description
- **Repository:** [InstaNovo](https://github.com/instadeepai/InstaNovo)
- **Paper:** [De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments](https://www.biorxiv.org/content/10.1101/2023.08.30.555055v1)
### Dataset Summary
Dataset used in the original [DeepNovo](https://www.pnas.org/doi/full/10.1073/pnas.1705691114) paper.
- The training set contains 8 species excluding yeast
- The validation/test set contains the yeast species
## Dataset Structure
The dataset is tabular, where each row corresponds to a labelled MS2 spectra.
- `sequence (string)` \
The target peptide sequence excluding post-translational modifications
- `modified_sequence (string)` \
The target peptide sequence including post-translational modifications
- `precursor_mz (float64)` \
The mass-to-charge of the precursor (from MS1)
- `charge (int64)` \
The charge of the precursor (from MS1)
- `mz_array (list[float64])` \
The mass-to-charge values of the MS2 spectrum
- `mz_array (list[float32])` \
The intensity values of the MS2 spectrum
## Citation Information
If you use this dataset, please cite the original authors.
The original data is available on [MASSIVE](https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) with the identifier `MSV000081382`.
Please also cite InstaNovo:
```bibtex
@article{eloff_kalogeropoulos_2023_instanovo,
title = {De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments},
author = {Kevin Eloff and Konstantinos Kalogeropoulos and Oliver Morell and Amandla Mabona and Jakob Berg Jespersen and Wesley Williams and Sam van Beljouw and Marcin Skwark and Andreas Hougaard Laustsen and Stan J. J. Brouns and Anne Ljungars and Erwin Marten Schoof and Jeroen Van Goey and Ulrich auf dem Keller and Karim Beguir and Nicolas Lopez Carranza and Timothy Patrick Jenkins},
year = {2023},
doi = {10.1101/2023.08.30.555055},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/10.1101/2023.08.30.555055v1},
journal = {bioRxiv}
}
```
|
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mapama247/wikihow_es
|
mapama247
| 2023-09-19T12:48:50Z | 49 | 0 |
[
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:es",
"license:cc-by-nc-sa-3.0",
"Spanish",
"WikiHow",
"Wiki Articles",
"Tutorials",
"Step-By-Step",
"Instruction Tuning",
"region:us"
] |
[
"text-classification",
"question-answering",
"conversational",
"summarization"
] | 2023-09-18T08:39:33Z |
---
pretty_name: WikiHow-ES
license: cc-by-nc-sa-3.0
size_categories: 1K<n<10K
language: es
multilinguality: monolingual
task_categories:
- text-classification
- question-answering
- conversational
- summarization
tags:
- Spanish
- WikiHow
- Wiki Articles
- Tutorials
- Step-By-Step
- Instruction Tuning
---
### Dataset Summary
Articles retrieved from the [Spanish WikiHow website](https://es.wikihow.com) on September 2023.
Each article contains a tutorial about a specific topic. The format is always a "How to" question
followed by a detailed step-by-step explanation. In some cases, the response includes several methods.
The main idea is to use this data for instruction tuning of Spanish LLMs, but given its nature it
could also be used for other tasks such as text classification or summarization.
### Languages
- Spanish (ES)
### Usage
To load the full dataset:
```python
from datasets import load_dataset
all_articles = load_dataset("mapama247/wikihow_es")
print(all_articles.num_rows) # output: {'train': 7380}
```
To load only examples from a specific category:
```python
from datasets import load_dataset
sports_articles = load_dataset("mapama247/wikihow_es", "deportes")
print(sports_articles.num_rows) # output: {'train': 201}
```
List of available categories, with the repective number of examples:
```
computadoras-y-electrónica 821
salud 804
pasatiempos 729
cuidado-y-estilo-personal 724
carreras-y-educación 564
en-la-casa-y-el-jardín 496
finanzas-y-negocios 459
comida-y-diversión 454
relaciones 388
mascotas-y-animales 338
filosofía-y-religión 264
arte-y-entretenimiento 254
en-el-trabajo 211
adolescentes 201
deportes 201
vida-familiar 147
viajes 139
automóviles-y-otros-vehículos 100
días-de-fiesta-y-tradiciones 86
```
### Supported Tasks
This dataset can be used to train a model for...
- `instruction-tuning`
- `text-classification`
- `question-answering`
- `conversational`
- `summarization`
## Dataset Structure
### Data Instances
```python
{
'category': str,
'question': str,
'introduction': str,
'answers': List[str],
'short_answers': List[str],
'url': str,
'num_answers': int,
'num_refs': int,
'expert_author': bool,
}
```
### Data Fields
- `category`: The category (from [this list](https://es.wikihow.com/Especial:CategoryListing)) to which the example belongs to.
- `label`: Numerical representation of the category, for text classification purposes.
- `question`: The article's title, which always starts with "¿Cómo ...".
- `introduction`: Introductory text that precedes the step-by-step explanation.
- `answers`: List of complete answers, with the full explanation of each step.
- `short_answers`: List of shorter answers that only contain one-sentence steps.
- `num_answers`: The number of alternative answers provided (e.g. length of `answers`).
- `num_ref`: Number of references provided in the article.
- `expert_authors`: Whether the article's author claims to be an expert on the topic or not.
- `url`: The URL address of the original article.
### Data Splits
There is only one split (`train`) that contains a total of 7,380 examples.
## Dataset Creation
### Curation Rationale
This dataset was created for language model alignment to end tasks and user preferences.
### Source Data
How-To questions with detailed step-by-step answers, retrieved from the WikiHow website.
#### Data Collection and Normalization
All articles available in September 2023 were extracted, no filters applied.
Along with the article's content, some metadata was retrieved as well.
#### Source language producers
WikiHow users. All the content is human-generated.
### Personal and Sensitive Information
The data does not include personal or sensitive information.
## Considerations
### Social Impact
The Spanish community can benefit from the high-quality data provided by this dataset.
### Bias
No post-processing steps have been applied to mitigate potential social biases.
## Additional Information
### Curators
Marc Pàmes @ Barcelona Supercomputing Center.
### License
This dataset is licensed under a **Creative Commons CC BY-NC-SA 3.0** license.
Quote from [WikiHow's Terms of Use](https://www.wikihow.com/wikiHow:Terms-of-Use):
> All text posted by Users to the Service is sub-licensed by wikiHow to other Users under a Creative Commons license as
> provided herein. The Creative Commons license allows such user generated text content to be used freely for personal,
> non-commercial purposes, so long as it is used and attributed to the original author as specified under the terms of
> the license. Allowing free republication of our articles helps wikiHow achieve its mission by providing instruction
> on solving the problems of everyday life to more people for free. In order to support this goal, wikiHow hereby grants
> each User of the Service a license to all text content that Users contribute to the Service under the terms and
> conditions of a Creative Commons CC BY-NC-SA 3.0 License. Please be sure to read the terms of the license carefully.
> You continue to own all right, title, and interest in and to your User Content, and you are free to distribute it as
> you wish, whether for commercial or non-commercial purposes.
|
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] |
vishnupriyavr/spotify-million-song-dataset
|
vishnupriyavr
| 2023-10-02T12:17:02Z | 49 | 0 |
[
"license:cc0-1.0",
"region:us"
] | null | 2023-10-02T12:16:58Z |
---
license:
- cc0-1.0
converted_from: kaggle
kaggle_id: notshrirang/spotify-million-song-dataset
---
# Dataset Card for Spotify Million Song Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://kaggle.com/datasets/notshrirang/spotify-million-song-dataset
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is Spotify Million Song Dataset. This dataset contains song names, artists names, link to the song and lyrics. This dataset can be used for recommending songs, classifying or clustering songs.
### 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
This dataset was shared by [@notshrirang](https://kaggle.com/notshrirang)
### Licensing Information
The license for this dataset is cc0-1.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed]
|
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MananSantoki/vadodara-jsonl-converted
|
MananSantoki
| 2023-10-25T11:29:27Z | 49 | 0 |
[
"region:us"
] | null | 2023-10-25T11:29:26Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 103428
num_examples: 410
download_size: 40823
dataset_size: 103428
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vadodara-jsonl-converted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
anhdungitvn/elementary_math_qa
|
anhdungitvn
| 2023-11-06T12:22:42Z | 49 | 0 |
[
"region:us"
] | null | 2023-11-06T12:22:38Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence: string
- name: explanation
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 303871
num_examples: 1200
- name: test
num_bytes: 31224
num_examples: 189
download_size: 172910
dataset_size: 335095
---
# Dataset Card for "elementary_math_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
GHOFRANEE/Alcora2
|
GHOFRANEE
| 2023-11-08T14:17:12Z | 49 | 0 |
[
"region:us"
] | null | 2023-11-08T13:39:29Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 260321
num_examples: 102
download_size: 20852
dataset_size: 260321
---
# Dataset Card for "Alcora2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
tungkho178/NLLB_translations_Vietnamese_40_51k76
|
tungkho178
| 2023-11-12T17:58:26Z | 49 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-12T17:57:49Z |
---
license: apache-2.0
---
|
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] |
lukesjordan/worldbank-project-documents
|
lukesjordan
| 2022-10-24T20:10:40Z | 48 | 2 |
[
"task_categories:table-to-text",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:abstractive-qa",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"task_ids:language-modeling",
"task_ids:named-entity-recognition",
"task_ids:text-simplification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:other",
"conditional-text-generation",
"structure-prediction",
"region:us"
] |
[
"table-to-text",
"question-answering",
"summarization",
"text-generation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
- question-answering
- summarization
- text-generation
task_ids:
- abstractive-qa
- closed-domain-qa
- extractive-qa
- language-modeling
- named-entity-recognition
- text-simplification
pretty_name: worldbank_project_documents
language_bcp47:
- en-US
tags:
- conditional-text-generation
- structure-prediction
---
# Dataset Card for World Bank Project Documents
## 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:** https://github.com/luke-grassroot/aid-outcomes-ml
- **Paper:** Forthcoming
- **Point of Contact:** Luke Jordan (lukej at mit)
### Dataset Summary
This is a dataset of documents related to World Bank development projects in the period 1947-2020. The dataset includes
the documents used to propose or describe projects when they are launched, and those in the review. The documents are indexed
by the World Bank project ID, which can be used to obtain features from multiple publicly available tabular datasets.
### Supported Tasks and Leaderboards
No leaderboard yet. A wide range of possible supported tasks, including varieties of summarization, QA, and language modelling. To date, the datasets have been used primarily in conjunction with tabular data (via BERT embeddings) to predict project outcomes.
### Languages
English
## Dataset Structure
### Data Instances
### Data Fields
* World Bank project ID
* Document text
* Document type: "APPROVAL" for documents written at the beginning of a project, when it is approved; and "REVIEW" for documents written at the end of a project
### Data Splits
To allow for open exploration, and since different applications will want to do splits based on different sampling weights, we have not done a train test split but left all files in the train branch.
## Dataset Creation
### Source Data
Documents were scraped from the World Bank's public project archive, following links through to specific project pages and then collecting the text files made available by the [World Bank](https://projects.worldbank.org/en/projects-operations/projects-home).
### Annotations
This dataset is not annotated.
### Personal and Sensitive Information
None.
## Considerations for Using the Data
### Social Impact of Dataset
Affects development projects, which can have large-scale consequences for many millions of people.
### Discussion of Biases
The documents reflect the history of development, which has well-documented and well-studied issues with the imposition of developed world ideas on developing world countries. The documents provide a way to study those in the field of development, but should not be used for their description of the recipient countries, since that language will reflect a multitude of biases, especially in the earlier reaches of the historical projects.
## Additional Information
### Dataset Curators
Luke Jordan, Busani Ndlovu.
### Licensing Information
MIT +no-false-attribs license (MITNFA).
### Citation Information
@dataset{world-bank-project-documents,
author = {Jordan, Luke and Ndlovu, Busani and Shenk, Justin},
title = {World Bank Project Documents Dataset},
year = {2021}
}
### Contributions
Thanks to [@luke-grassroot](https://github.com/luke-grassroot), [@FRTNX](https://github.com/FRTNX/) and [@justinshenk](https://github.com/justinshenk) for adding this dataset.
|
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ruanchaves/binkley
|
ruanchaves
| 2022-10-20T19:12:56Z | 48 | 0 |
[
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"license:unknown",
"word-segmentation",
"region:us"
] |
[
"structure-prediction"
] | 2022-03-05T22:56:51Z |
---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- code
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: Binkley
tags:
- word-segmentation
---
# Dataset Card for Binkley
## Dataset Description
- **Paper:** [Normalizing Source Code Vocabulary](https://www.researchgate.net/publication/224198190_Normalizing_Source_Code_Vocabulary)
### Dataset Summary
In programming languages, identifiers are tokens (also called symbols) which name language entities.
Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
Binkley is a dataset for identifier segmentation, i.e. the task of adding spaces between the words on a identifier.
### Languages
- C
- C++
- Java
## Dataset Structure
### Data Instances
```
{
"index": 0,
"identifier": "init_g16_i",
"segmentation": "init _ g 16 _ i"
}
```
### Data Fields
- `index`: a numerical index.
- `identifier`: the original identifier.
- `segmentation`: the gold segmentation for the identifier.
## Dataset Creation
- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
- The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.
- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ).
- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
## Additional Information
### Citation Information
```
@inproceedings{inproceedings,
author = {Lawrie, Dawn and Binkley, David and Morrell, Christopher},
year = {2010},
month = {11},
pages = {3 - 12},
title = {Normalizing Source Code Vocabulary},
journal = {Proceedings - Working Conference on Reverse Engineering, WCRE},
doi = {10.1109/WCRE.2010.10}
}
```
### Contributions
This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library.
|
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] |
osyvokon/pavlick-formality-scores
|
osyvokon
| 2023-11-24T16:41:26Z | 48 | 1 |
[
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] |
[
"text-classification"
] | 2022-04-27T15:28:07Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
pretty_name: 'Sentence-level formality annotations for news, blogs, email and QA forums.
Published in "An Empirical Analysis of Formality in Online Communication" (Pavlick
and Tetreault, 2016) '
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
---
This dataset contains sentence-level formality annotations used in the 2016
TACL paper "An Empirical Analysis of Formality in Online Communication"
(Pavlick and Tetreault, 2016). It includes sentences from four genres (news,
blogs, email, and QA forums), all annotated by humans on Amazon Mechanical
Turk. The news and blog data was collected by Shibamouli Lahiri, and we are
redistributing it here for the convenience of other researchers. We collected
the email and answers data ourselves, using a similar annotation setup to
Shibamouli.
In the original dataset, `answers` and `email` were tokenized. In this version,
Oleksiy Syvokon detokenized them with `moses-detokenizer` and a bunch of
additional regexps.
If you use this data in your work, please cite BOTH of the below papers:
```
@article{PavlickAndTetreault-2016:TACL,
author = {Ellie Pavlick and Joel Tetreault},
title = {An Empirical Analysis of Formality in Online Communication},
journal = {Transactions of the Association for Computational Linguistics},
year = {2016},
publisher = {Association for Computational Linguistics}
}
@article{Lahiri-2015:arXiv,
title={{SQUINKY! A} Corpus of Sentence-level Formality, Informativeness, and Implicature},
author={Lahiri, Shibamouli},
journal={arXiv preprint arXiv:1506.02306},
year={2015}
}
```
## Contents
The annotated data files and number of lines in each are as follows:
* 4977 answers -- Annotated sentences from a random sample of posts from the Yahoo! Answers forums: https://answers.yahoo.com/
* 1821 blog -- Annotated sentences from the top 100 blogs listed on http://technorati.com/ on October 31, 2009.
* 1701 email -- Annotated sentences from a random sample of emails from the Jeb Bush email archive: http://americanbridgepac.org/jeb-bushs-gubernatorial-email-archive/
* 2775 news -- Annotated sentences from the "breaking", "recent", and "local" news sections of the following 20 news sites: CNN, CBS News, ABC News, Reuters, BBC News Online, New York Times, Los Angeles Times, The Guardian (U.K.), Voice of America, Boston Globe, Chicago Tribune, San Francisco Chronicle, Times Online (U.K.), news.com.au, Xinhua, The Times of India, Seattle Post Intelligencer, Daily Mail, and Bloomberg L.P.
## Format
Each record contains the following fields:
1. `avg_score`: the mean formality rating, which ranges from -3 to 3 where lower scores indicate less formal sentences
2. `sentence`
|
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graphs-datasets/CIFAR10
|
graphs-datasets
| 2023-02-07T16:37:24Z | 48 | 1 |
[
"task_categories:graph-ml",
"license:mit",
"arxiv:2003.00982",
"region:us"
] |
[
"graph-ml"
] | 2022-12-08T09:59:00Z |
---
licence: unknown
license: mit
task_categories:
- graph-ml
---
# Dataset Card for CIFAR10
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://github.com/graphdeeplearning/benchmarking-gnns)**
- **Paper:**: (see citation)
### Dataset Summary
The `CIFAR10` dataset consists of 45000 images in 10 classes, represented as graphs.
### Supported Tasks and Leaderboards
`CIFAR10` should be used for multiclass graph classification.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| #graphs | 45,000 |
| average #nodes | 117.6 |
| average #edges | 941.2 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph
- `pos` (list: 2 x #node): positional information of each node
### Data Splits
This data is split. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under MIT license.
### Citation Information
```
@article{DBLP:journals/corr/abs-2003-00982,
author = {Vijay Prakash Dwivedi and
Chaitanya K. Joshi and
Thomas Laurent and
Yoshua Bengio and
Xavier Bresson},
title = {Benchmarking Graph Neural Networks},
journal = {CoRR},
volume = {abs/2003.00982},
year = {2020},
url = {https://arxiv.org/abs/2003.00982},
eprinttype = {arXiv},
eprint = {2003.00982},
timestamp = {Sat, 23 Jan 2021 01:14:30 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2003-00982.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
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Ramos-Ramos/smallnorb
|
Ramos-Ramos
| 2022-12-15T08:30:22Z | 48 | 0 |
[
"region:us"
] | null | 2022-12-15T07:29:28Z |
---
dataset_info:
features:
- name: image_lt
dtype: image
- name: image_rt
dtype: image
- name: category
dtype: int32
- name: instance
dtype: int32
- name: elevation
dtype: int32
- name: azimuth
dtype: int32
- name: lighting
dtype: int32
splits:
- name: train
num_bytes: 117947794.0
num_examples: 24300
- name: test
num_bytes: 118130266.0
num_examples: 24300
download_size: 236815224
dataset_size: 236078060.0
---
# Dataset Card for "smallnorb"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
**NOTE:** This dataset is an unofficial port of small NORB based on a [repo from Andrea Palazzi](https://github.com/ndrplz/small_norb) using this [script](https://colab.research.google.com/drive/1Tx20uP1PrnyarsNCWf1dN9EQyr38BDIE?usp=sharing). For complete and accurate information, we highly recommend visiting the dataset's original homepage.
- **Homepage:** https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/
- **Paper:** https://ieeexplore.ieee.org/document/1315150
### Dataset Summary
From the dataset's [homepage](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/):
> This database is intended for experiments in 3D object reocgnition from shape. It contains images of 50 toys belonging to 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. The objects were imaged by two cameras under 6 lighting conditions, 9 elevations (30 to 70 degrees every 5 degrees), and 18 azimuths (0 to 340 every 20 degrees).
>
> The training set is composed of 5 instances of each category (instances 4, 6, 7, 8 and 9), and the test set of the remaining 5 instances (instances 0, 1, 2, 3, and 5).
## Dataset Structure
### Data Instances
An example of an instance in this dataset:
```
{
'image_lt': <PIL.PngImagePlugin.PngImageFile image mode=L size=96x96 at 0x...>,
'image_rt': <PIL.PngImagePlugin.PngImageFile image mode=L size=96x96 at 0x...>,
'category': 0,
'instance': 8,
'elevation': 6,
'azimuth': 4,
'lighting': 4
}
```
### Data Fields
Explanation of this dataset's fields:
- `image_lt`: a PIL image of an object from the dataset taken with one of two cameras
- `image_rt`: a PIL image of an object from the dataset taken with one of two cameras
- `category`: the category of the object shown in the images
- `instance`: the instance of the category of the object shown in the images
- `elevation`: the label of the elevation of the cameras used in capturing a picture of the object
- `azimuth`: the label of the azimuth of the cameras used in capturing a picture of the object
- `lighting`: the label of the lighting condition used in capturing a picture of the object
For more information on what these categories and labels pertain to, please see [Dataset Summary](#dataset-summary) or the [repo](https://github.com/ndrplz/small_norb) used in processing the dataset.
### Data Splits
Information on this dataset's splits:
| | train | test |
|------|------:|------:|
| size | 24300 | 24300 |
## Additional Information
### Dataset Curators
Credits from the dataset's [homepage](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/):
> [Fu Jie Huang](http://www.cs.nyu.edu/jhuangfu/), [Yann LeCun](http://yann.lecun.com/)
>
> Courant Institute, New York University
>
> October, 2005
### Licensing Information
From the dataset's [homepage](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/):
> This database is provided for research purposes. It cannot be sold. Publications that include results obtained with this database should reference the following paper:
>
> Y. LeCun, F.J. Huang, L. Bottou, Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2004
### Citation Information
From the dataset's [homepage](https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/):
> Publications that include results obtained with this database should reference the following paper:
>
> Y. LeCun, F.J. Huang, L. Bottou, Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2004
```
@inproceedings{lecun2004learning,
title={Learning methods for generic object recognition with invariance to pose and lighting},
author={LeCun, Yann and Huang, Fu Jie and Bottou, Leon},
booktitle={Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.},
volume={2},
pages={II--104},
year={2004},
organization={IEEE}
}
```
DOI: [10.1109/CVPR.2004.1315150](https://doi.org/10.1109/CVPR.2004.1315150)
### Contributions
Code to process small NORB adapted from [Andrea Palazzi's repo](https://github.com/ndrplz/small_norb) with this [script](https://colab.research.google.com/drive/1Tx20uP1PrnyarsNCWf1dN9EQyr38BDIE?usp=sharing).
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keremberke/aerial-sheep-object-detection
|
keremberke
| 2023-01-05T08:02:23Z | 48 | 4 |
[
"task_categories:object-detection",
"roboflow",
"region:us"
] |
[
"object-detection"
] | 2023-01-02T20:17:28Z |
---
task_categories:
- object-detection
tags:
- roboflow
---
### Roboflow Dataset Page
[https://universe.roboflow.com/riis/aerial-sheep/dataset/1](https://universe.roboflow.com/riis/aerial-sheep/dataset/1?ref=roboflow2huggingface)
### Dataset Labels
```
['sheep']
```
### Citation
```
@misc{ aerial-sheep_dataset,
title = { Aerial Sheep Dataset },
type = { Open Source Dataset },
author = { Riis },
howpublished = { \\url{ https://universe.roboflow.com/riis/aerial-sheep } },
url = { https://universe.roboflow.com/riis/aerial-sheep },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { jun },
note = { visited on 2023-01-02 },
}
```
### License
Public Domain
### Dataset Summary
This dataset was exported via roboflow.com on December 2, 2022 at 4:47 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 4133 images.
Sheep are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 600x600 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
* Randomly crop between 0 and 20 percent of the image
* Random brigthness adjustment of between -15 and +15 percent
* Random exposure adjustment of between -10 and +10 percent
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] |
instruction-tuning-sd/cartoonization
|
instruction-tuning-sd
| 2023-05-11T15:16:08Z | 48 | 9 |
[
"task_categories:image-to-image",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
[
"image-to-image"
] | 2023-03-17T09:13:34Z |
---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: cartoonized_image
dtype: image
splits:
- name: train
num_bytes: 3257571330
num_examples: 5000
download_size: 3296272284
dataset_size: 3257571330
size_categories:
- 1K<n<10K
language:
- en
task_categories:
- image-to-image
---
# Instruction-prompted cartoonization dataset
This dataset was created from 5000 images randomly sampled from the [Imagenette dataset](https://github.com/fastai/imagenette). For more
details on how the dataset was created, check out [this directory](https://github.com/sayakpaul/instruction-tuned-sd/tree/main/data_preparation).
Following figure depicts the data preparation workflow:
<p align="center">
<img src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/cartoonization_data_wheel.png" width=600/>
</p>
## Known limitations and biases
The dataset was derived from Imagenette, which, in turn, was derived from [ImageNet](https://www.image-net.org/). So, naturally, this
dataset inherits the limitations and biases of ImageNet.
## Licensing
The dataset was derived from Imagenette, which, in turn, was derived from [ImageNet](https://www.image-net.org/). So, this dataset's license
is the same as ImageNet.
|
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ipipan/maupqa
|
ipipan
| 2023-09-18T07:28:41Z | 48 | 2 |
[
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:open-domain-qa",
"task_ids:document-retrieval",
"annotations_creators:found",
"annotations_creators:machine-generated",
"size_categories:1M<n<10M",
"language:pl",
"license:cc-by-sa-4.0",
"arxiv:2305.05486",
"arxiv:2309.08469",
"region:us"
] |
[
"question-answering",
"text-retrieval"
] | 2023-03-31T10:21:18Z |
---
task_categories:
- question-answering
- text-retrieval
task_ids:
- open-domain-qa
- document-retrieval
language:
- pl
pretty_name: MAUPQA
size_categories:
- 1M<n<10M
annotations_creators:
- found
- machine-generated
license: cc-by-sa-4.0
---
# Dataset Card for MAUPQA Dataset
## Dataset Description
- **Paper:** [MAUPQA: Massive Automatically-created Polish Question Answering Dataset](https://arxiv.org/abs/2305.05486), [SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering](https://arxiv.org/abs/2309.08469)
- **Point of Contact:** [Piotr Rybak](mailto:[email protected])
### Dataset Summary
MAUPQA is a collection of 14 datasets for Polish document retrieval. Most of the datasets are either machine-generated or machine-translated from English. Across all datasets, it consists of over 1M questions, 1M positive, and 7M hard-negative question-passage pairs.
### Supported Tasks and Leaderboards
- `document-retrieval`: The dataset can be used to train a model for document retrieval. Success on this task is typically measured by [top-k retrieval accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html) or [NDCG](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html).
### Languages
The text is in Polish, as spoken by the [Internet users](https://github.com/facebookresearch/cc_net), [Polish Wikipedia](https://pl.wikipedia.org/) editors, or is an output of generative or translation models. The BCP-47 code for Polish is pl-PL.
## Dataset Structure
### Data Instances
The dataset consists of over 8 million question-passage pairs. For each instance, there is a `question`, a passage (`passage_title`, `passage_text`), and a boolean indicator if the passage is `relevant` for the given question (i.e. does it contain the answers).
For a small subset of `question` there is also a list of possible `answers` formulated in a natural language, in a way a Polish
speaker would answer the questions.
```
{
'question_id': 1,
'question': 'Na którym kontynencie leży państwo Gujana, panie Krzysztofie?',
'answers': "['W Ameryce Południowej']",
'passage_title': 'Gujana (ujednoznacznienie)',
'passage_text': 'Gujana (region) – region Ameryki Południowej Gujana – państwo w Ameryce Południowej Gujana Brytyjska – dawna kolonia brytyjska; obecnie państwo Gujana Gujana Francuska – departament zamorski Francji; dawniej kolonia francuska Gujana Holenderska – dawna kolonia holenderska; obecnie państwo Surinam',
'relevant': True,
'passage_source': 'crawling',
'subset': '1z10'
}
```
### Data Fields
Question-passage pairs:
- `question_id`: an integer id of the question
- `question`: a string containing the question
- `passage_title`: a string containing the title of the Wikipedia article
- `passage_text`: a string containing the passage text as extracted by the human annotator
- `relevant`: a boolean flag representing whether a passage is relevant to the question (i.e. does it contain the answers)
- `annotated_by`: a string containing the name of the annotator who verified the relevance of the pair
- `answers`: a string containing a list of possible short answers to the question
- `passage_source`: a string containing the method of obtaining the passage. One of the following:
- `manual-annotation`: the question-passage pair was manually annotated
- `crawling`: the question-passage pairs were created by taking advantage of the specific structure of crawled website
- `dataset-translation`: the dataset was created by machine-translating the English dataset
- `generative-model`: the question was created by the generative model based on the given passage
- `bm25-negatives`: the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is not relevant
- `bm25-positives`: the passage was found by the BM25 retriever and scored using a multilingual cross-encoder to ensure it is relevant
- `subset`: a string containing the name of the dataset
### Data Splits
MAUPQA is a collection of 14 datasets and most of them are weakly labeled. Therefore, the intended use of MAUPQA is for training only. As such, all examples belong to a single `train` split. We recommend using the [PolQA](https://huggingface.co/datasets/ipipan/polqa) dataset for evaluation.
Basic statistics of all 14 datasets:
| dataset | # questions | # answers | # positive passages | # negative passages |
|-------------------|------------:|----------:|--------------------:|--------------------:|
| 1z10 | 22,835 | 21,415 | 22,014 | 139,471 |
| czy-wiesz-v2 | 29,078 | - | 29,078 | 143,306 |
| gpt3-cc | 10,146 | 10,146 | 10,177 | 89,203 |
| gpt3.5-cc | 29,591 | 29,583 | 29,720 | 251,959 |
| gpt3.5-wiki | 29,674 | 29,636 | 29,748 | 115,564 |
| mkqa | 4,036 | 4,036 | 3,968 | 19,814 |
| mqa | 172,768 | - | 178,131 | 1,249,659 |
| msmarco | 389,987 | - | 416,763 | 3,006,996 |
| multilingual-NLI | 100,752 | 64,900 | 68,096 | 743,857 |
| nq | 135,781 | - | 139,976 | 797,436 |
| poleval2021-pairs | 1,977 | - | 2,088 | 17,608 |
| poquad | 56,588 | 46,157 | 46,187 | 299,865 |
| templates | 15,993 | 14,504 | 15,993 | 45,228 |
| wiki-def | 18,093 | 18,092 | 18,093 | 84,956 |
| Total | 1,017,299 | 238,469 | 1,010,032 | 7,004,922 |
## Dataset Creation
### Curation Rationale
Open-domain question answering systems rely heavily on annotated datasets to train neural document retrievers. However, manually annotating such datasets is both difficult and time-consuming. To overcome these difficulties, we experimented with several methods for automatically collecting weakly labeled datasets. As a result, MAUPQA enables the development of robust document retrieval systems for Polish.
### Source Data
#### Initial Data Collection and Normalization
Below, we briefly describe each dataset. For a detailed description please refer to the [paper](https://arxiv.org/abs/2305.05486).
* `1z10`: We transcribe 333 recordings of the [Jeden z Dziesięciu](https://pl.wikipedia.org/wiki/Jeden_z_dziesi%C4%99ciu) TV show using the Whisper model and extract the question-answer pairs using GPT-3.5 model. We use the BM25 retriever and the GPT-3.5-based cross-encoder to match questions with Wikipedia passages.
* `czy-wiesz-v2`: We first crawl all questions from the [Did you know?](https://pl.wikipedia.org/wiki/Wikiprojekt:Czy_wiesz/archiwum) section on Polish Wikipedia together with a link to the relevant Wikipedia article. Then, we use the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage.
* `gpt3-cc`: We sample random passages from [CCNet](https://github.com/facebookresearch/cc_net) corpus and prompt GPT-3 to generate a relevant question.
* `gpt3.5-cc`: We sample random passages from [CCNet](https://github.com/facebookresearch/cc_net) corpus and prompt GPT-3.5 to generate a relevant question.
* `gpt3.5-wiki`: We sample random passages from Polish Wikipedia and prompt GPT-3.5 to generate a relevant question.
* `mkqa`: We clean the Polish subset of the [MKQA](https://huggingface.co/datasets/mkqa) dataset by removing questions without answers, requiring long answers (*Why?* and *How?* questions), and ambiguous ones ("Who is the *current* president?*). We use the BM25 retriever and the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage.
* `mqa`: We clean the Polish subset of the [MQA](https://huggingface.co/datasets/clips/mqa) dataset by removing artificially created questions like "What is the best hotel in *{city}*?" for hundreds of different *cities*. To clean the dataset, we cluster lexically similar questions/passages and remove clusters with over 5 questions.
* `msmarco`: We translate the [MS MARCO](https://huggingface.co/datasets/ms_marco) dataset into Polish using the machine translation model.
* `multilingual-NLI`: We extract question-answer pairs from the Polish subset of the [multilingual-NLI](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) dataset. We create questions using the following template: "Czy *{premise}*?" (Eng. "Does *{premise}*?") and use hypotheses as passages. We consider `entailment` and `contradiction` labels as relevant and `neutral` as negative.
* `nq`: We translate the [NQ](https://huggingface.co/datasets/natural_questions) dataset into Polish using the machine translation model.
* `poleval2021-pairs`: We take [allegro/polish-question-passage-pairs](https://huggingface.co/datasets/allegro/polish-question-passage-pairs) without any changes.
* `poquad`: We extract question-passages pairs from the training split of the [PoQuAD](https://huggingface.co/datasets/clarin-pl/poquad) dataset.
* `templates`: We take advantage of the Wikipedia structure to generate questions using predefined templates. For example, list pages group together similar entities (e.g. "Writers born in Poland") which allow generating questions like "Where was *{writer name}* born?". In total, we use 33 templates to generate questions. We use the [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to choose the most relevant passage from the linked article.
* `wiki-def`: We use [Wiktionary](https://www.wiktionary.org/) to generate questions based on word definitions. We use definitions that have links to Wikipedia articles to create the question-passage pairs. For example, the definition of "Monday" is "the first day of the week". Based on it, we generate the question "What is the name of *the first day of the week*?".
Additionally, we extend each dataset by sampling the hard negative passages using a BM25 retriever and score using a [multilingual cross-encoder](https://huggingface.co/unicamp-dl/mMiniLM-L6-v2-mmarco-v2) to ensure that passages are not relevant.
#### Who are the source language producers?
The text is in Polish, as spoken by the [Internet users](https://github.com/facebookresearch/cc_net), [Polish Wikipedia](https://pl.wikipedia.org/) editors, or is an output of generative or translation models.
### Annotations
#### Annotation process
The MAUPQA dataset doesn't provide additional annotation except for the annotation present in the source datasets.
#### Who are the annotators?
Please refer to the description of the source datasets.
### Personal and Sensitive Information
The dataset should not contain any personal or sensitive information. However, we use the [CCNet](https://github.com/facebookresearch/cc_net) dataset as a source of passages that we didn't manually inspect for personal and sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was created to promote the research in the open-domain question answering for Polish and allow developing question answering systems.
### Discussion of Biases
The machine-translated datasets might not represent the natural language as used by native Polish speakers. Similarly, the questions generated by the generative models might not be representative or correct.
Most of the question-passage pairs are created automatically using the BM25 retriever and as such it is biased to lexically similar pairs.
### Other Known Limitations
The MAUPQA dataset is mostly automatically generated and can therefore contain a high proportion of noise and incorrectly labeled question-passage pairs.
## Additional Information
### Dataset Curators
The MAUPQA dataset was collected by Piotr Rybak and Maciej Ogrodniczuk from the [Institute of Computer Science, Polish Academy of Sciences](http://zil.ipipan.waw.pl/) but the source datasets were created by many more researchers. Please refer to the original dataset descriptions for the full authorship.
This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@inproceedings{rybak-2023-maupqa,
title = "{MAUPQA}: Massive Automatically-created {P}olish Question Answering Dataset",
author = "Rybak, Piotr",
booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bsnlp-1.2",
pages = "11--16",
abstract = "Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.",
}
```
```
@misc{rybak2023silverretriever,
title={SilverRetriever: Advancing Neural Passage Retrieval for Polish Question Answering},
author={Piotr Rybak and Maciej Ogrodniczuk},
year={2023},
eprint={2309.08469},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
mstz/magic
|
mstz
| 2023-04-16T17:34:16Z | 48 | 0 |
[
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"magic",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-06T14:33:36Z |
---
language:
- en
tags:
- magic
- tabular_classification
- binary_classification
- UCI
pretty_name: Magic
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- magic
license: cc
---
# Magic
The [Magic dataset](https://archive.ics.uci.edu/ml/datasets/Magic) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|---------------------------------------------------------------|
| magic | Binary classification | Classify the person's magic as over or under the threshold. |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/magic")["train"]
```
|
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slvnwhrl/blurbs-clustering-p2p
|
slvnwhrl
| 2023-04-24T11:42:06Z | 48 | 0 |
[
"size_categories:10K<n<100K",
"language:de",
"license:cc-by-nc-4.0",
"embeddings",
"clustering",
"benchmark",
"region:us"
] | null | 2023-04-21T14:17:32Z |
---
license: cc-by-nc-4.0
language:
- de
tags:
- embeddings
- clustering
- benchmark
size_categories:
- 10K<n<100K
---
This dataset can be used as a benchmark for clustering word embeddings for <b>German</b>.
The datasets contains book titles and is based on the dataset from the [GermEval 2019 Shared Task on Hierarchical Classification of Blurbs](https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/germeval-2019-hmc.html). It contains 18'084 unqiue samples, 28 splits with 177 to 16'425 samples and 4 to 93 unique classes. Splits are built similarly to [MTEB](https://github.com/embeddings-benchmark/mteb)'s [ArxivClusteringP2P](https://huggingface.co/datasets/mteb/arxiv-clustering-p2p).
Have a look at [German Text Embedding Clustering Benchmark](https://github.com/ClimSocAna/tecb-de) for more infos, datasets and evaluation results.
|
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] |
jlh/uci-adult-income
|
jlh
| 2023-04-25T23:19:35Z | 48 | 0 |
[
"region:us"
] | null | 2023-04-25T21:40:16Z |
---
dataset_info:
features:
- name: age
dtype: int64
- name: workclass
dtype: string
- name: fnlwgt
dtype: int64
- name: education
dtype: string
- name: education-num
dtype: int64
- name: marital-status
dtype: string
- name: occupation
dtype: string
- name: relationship
dtype: string
- name: race
dtype: string
- name: sex
dtype: string
- name: capital-gain
dtype: int64
- name: capital-loss
dtype: int64
- name: hours-per-week
dtype: int64
- name: native-country
dtype: string
- name: income
dtype:
class_label:
names:
'0': ' <=50K'
'1': ' >50K'
splits:
- name: train
num_bytes: 5552570
num_examples: 32561
download_size: 586658
dataset_size: 5552570
---
# Dataset Card for "uci-adult-income"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
emozilla/quality
|
emozilla
| 2023-07-14T00:56:02Z | 48 | 5 |
[
"language:en",
"region:us"
] | null | 2023-04-30T03:31:45Z |
---
language: en
dataset_info:
features:
- name: article
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: int64
- name: hard
dtype: bool
splits:
- name: train
num_bytes: 62597212
num_examples: 2523
- name: validation
num_bytes: 51198650
num_examples: 2086
download_size: 14352147
dataset_size: 113795862
---
# Dataset Card for "quality"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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zetavg/zh-tw-wikipedia
|
zetavg
| 2023-05-06T12:44:26Z | 48 | 14 |
[
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"region:us"
] |
[
"text-generation"
] | 2023-05-06T10:35:05Z |
---
dataset_info:
features:
- name: pageid
dtype: int64
- name: html
dtype: string
- name: markdown
dtype: string
- name: coordinate
struct:
- name: globe
dtype: string
- name: lat
dtype: float64
- name: lon
dtype: float64
- name: length
dtype: int64
- name: touched
dtype: string
- name: lastrevid
dtype: int64
- name: original_title
dtype: string
splits:
- name: train
num_bytes: 21930303312
num_examples: 2533212
download_size: 0
dataset_size: 21930303312
task_categories:
- text-generation
language:
- zh
pretty_name: 台灣正體中文維基百科 (zh-tw Wikipedia)
size_categories:
- 1M<n<10M
---
# 台灣正體中文維基百科 (zh-tw Wikipedia)
截至 2023 年 5 月，中文維基百科 2,533,212 篇條目的台灣正體文字內容。每篇條目為一列 (row)，包含 HTML 以及 Markdown 兩種格式。
A nearly-complete collection of 2,533,212 Traditional Chinese (`zh-tw`) Wikipedia pages, gathered between May 1, 2023, and May 7, 2023. Includes both the original HTML format and an auto-converted Markdown version, which has been processed using [vinta/pangu.py](https://github.com/vinta/pangu.py).
於 2023 年 5 月 1 日至 5 月 7 日間取自維基百科 [`action=query`](https://zh.wikipedia.org/w/api.php?action=help&modules=query) & [`prop=extracts`](https://zh.wikipedia.org/w/api.php?action=help&modules=query%2Bextracts) API，內容皆與維基百科網站之台灣正體版本一致，沒有繁簡體混雜的問題。
For development usage, checkout [`zetavg/zh-tw-wikipedia-dev`](https://huggingface.co/datasets/zetavg/zh-tw-wikipedia-dev), which is a subset that contains only 1,000 randomly picked items.
## 資料內容
* `pageid` — 維基百科頁面 ID。
* `html` — 頁面原始的 HTML 匯出。
* `markdown` — 頁面轉換為 Markdown 格式，並以 [vinta/pangu.py](https://github.com/vinta/pangu.js) 於全形字與半形字之間加入空格後的版本。
* `coordinate` — 頁面主題的經緯度座標，例如 `{ "lat": 22.63333333, "lon": 120.26666667 }`。若無則為 `null`。
* `length` — 頁面內容長度。
* `touched` — 頁面的最後修訂時間。
* `lastrevid` — 最新修訂版本的修訂 ID。
* `original_title` — 維基百科未經轉換的原始頁面標題，可能為簡體中文。
## 已知問題
* 無法抽取為 *受限格式 HTML* 的內容皆會遺失，例如所有圖片、圖表、表格、參考資料列表，以及部分程式碼區塊。
* 極少數內容過長的條目沒有納入，大致上計有：`四千`、`五千`、`六千`、`英雄傳說VI`、`軌跡系列角色列表`、`碧之軌跡角色列表`、`零之軌跡角色列表`。
* 缺少頁面標題 `title` 欄位（原可透過 API `inprop=varianttitles` 取得，但資料抓取時程式撰寫遺漏了這個欄位）。
|
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Joemgu/sumstew
|
Joemgu
| 2023-06-21T13:07:18Z | 48 | 6 |
[
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:en",
"language:de",
"language:fr",
"language:it",
"language:es",
"license:apache-2.0",
"chemistry",
"biology",
"region:us"
] |
[
"summarization"
] | 2023-05-30T20:36:23Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: target
dtype: string
- name: input_tokens
dtype: int64
- name: target_tokens
dtype: int64
- name: subset
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 3338029493
num_examples: 187221
- name: validation
num_bytes: 218403099
num_examples: 14542
- name: test
num_bytes: 201638368
num_examples: 12467
download_size: 1982559322
dataset_size: 3758070960
task_categories:
- summarization
language:
- en
- de
- fr
- it
- es
size_categories:
- 100K<n<1M
license: apache-2.0
tags:
- chemistry
- biology
---
# Dataset Card for "sumstew"
## TL;DR:
Sumstew is a abstractive, multilingual Dataset, with a balanced number of samples from a diverse set of summarization Datasets. The input sizes range up to 16384 tokens.
Filtered using a diverse set of heuristics to encourage high coverage, accuracy and factual consistency. Code to reproduce Dataset available at *TODO*
## Dataset Description
- **Dataset Identifier**: sumstew
- **Dataset Summary**: "SumStew" is a rich multilingual dataset for text summarization. It incorporates diverse data sources such as cnn_dailymail, samsum, mlsum (de, fr, es, it), klexikon, xlsum (fr, en, es), govreport, sciqa, piqa, pumbed_qa, multinews, laysum, booksum, dialogsum, fanpage (it), ilpost (it). This data has been curated by filtering based on n-gram overlap between the source and target documents and normalized to prevent undue bias. Every instance in this dataset is prefixed by an instruction (title, summary, or qa).
## Task Information
- **Task Categories**: The tasks covered by this dataset are primarily summarization tasks.
- **Languages**: This dataset supports multiple languages including English (en), German (de), French (fr), Italian (it), and Spanish (es).
## Dataset Structure
- **Data Instances**: Each data instance in the dataset comprises five fields - 'prompt', 'target', 'task', 'subset', and 'language'.
- 'prompt': The input text for the task. (dtype: string)
- 'target': The expected output for the task. (dtype: string)
- 'subset': The subset of the dataset the instance belongs to. (dtype: string)
- 'language': The language of the instance. (dtype: string)
- **Data Splits**: The dataset is split into two subsets:
- 'train' set: 187221 examples
- 'validation' set: 14542 examples
- 'test' set: 12467 examples
## Dataset Statistics
- **Max Document Length**: The maximum document length is 16384 mlong-t5 tokens.
- **Max Output Length**: The maximum output length is 1024 mlong-t5 tokens.
## Additional Information
- **Data Collection**: The data has been collected from a variety of sources spanning different languages and domains, ensuring a diverse and comprehensive dataset.
- **Data Cleaning**: The dataset has been filtered by checking the ngram overlap between the source and target document and dropping samples which have too much or too little overlap, and also through normalization.
- **Known Limitations**: As the dataset is generated from diverse sources, the inherent biases or limitations of those sources may persist in this dataset as well.
- **Usage Scenarios**: This dataset can be used for training and evaluating models on tasks like summarization and question-answering, in a multilingual context.
## Credits
At this point I want to thank every creator of the underlying datasets (there are too many for me to count). If there are any issues concercining licensing or you want your data removed from the dataset, feel free to DM over Twitter (link in profile).
Special thanks to @pszemraj [https://huggingface.co/pszemraj] for the inspiration.
If interested in collaboration or consulting for your project, feel free to DM https://twitter.com/StutterBuddy
|
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] |
clarin-knext/hotpotqa-pl
|
clarin-knext
| 2023-06-07T08:13:33Z | 48 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:21:34Z |
---
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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] |
divergente/wikitext-ptbr-1
|
divergente
| 2023-06-15T01:24:23Z | 48 | 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",
"language:pt",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2023-06-15T00:46:30Z |
---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- pt
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- monolingual
pretty_name: WikiTextPtBr
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
- config_name: wikitext-ptbr
features:
- name: text
dtype: string
---
|
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dmayhem93/agieval-sat-math
|
dmayhem93
| 2023-06-18T17:32:05Z | 48 | 6 |
[
"license:mit",
"arxiv:2304.06364",
"region:us"
] | null | 2023-06-18T12:51:24Z |
---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 110388
num_examples: 220
download_size: 57002
dataset_size: 110388
license: mit
---
# Dataset Card for "agieval-sat-math"
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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rbiojout/odoo_python_15
|
rbiojout
| 2023-07-06T15:42:22Z | 48 | 0 |
[
"license:agpl-3.0",
"region:us"
] | null | 2023-07-06T14:50:12Z |
---
license: agpl-3.0
dataset_info:
features:
- name: size
dtype: int64
- name: ext
dtype: string
- name: lang
dtype: string
- name: branch
dtype: string
- name: content
dtype: string
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
splits:
- name: train
num_bytes: 40721998
num_examples: 9349
download_size: 12783255
dataset_size: 40721998
---
|
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pszemraj/simple_wikipedia
|
pszemraj
| 2023-09-09T14:54:54Z | 48 | 0 |
[
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"language modeling",
"lamguage",
"2023 data",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2023-09-03T02:32:33Z |
---
license: apache-2.0
task_categories:
- text-generation
- fill-mask
language:
- en
tags:
- language modeling
- lamguage
- 2023 data
size_categories:
- 100K<n<1M
---
# simple wikipedia
the 'simple' split of Wikipedia, from Sept 1 2023. The train split contains about 65M tokens,
Pulled via:
```python
dataset = load_dataset(
"wikipedia", language="simple", date="20230901", beam_runner="DirectRunner"
)
```
## stats
### train split
general info
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 226242 entries, 0 to 226241
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 226242 non-null string
1 url 226242 non-null string
2 title 226242 non-null string
3 text 226242 non-null string
dtypes: string(4)
```
token length (NeoX)
| | tokens |
|:------|--------------:|
| count | 226242 |
| mean | 287.007 |
| std | 1327.07 |
| min | 1 |
| 25% | 65 |
| 50% | 126 |
| 75% | 243 |
| max | 60844 |
|
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] |
lavita/ChatDoctor-iCliniq
|
lavita
| 2023-09-11T21:13:37Z | 48 | 2 |
[
"region:us"
] | null | 2023-09-11T21:11:18Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: answer_icliniq
dtype: string
- name: answer_chatgpt
dtype: string
- name: answer_chatdoctor
dtype: string
splits:
- name: train
num_bytes: 16962106
num_examples: 7321
download_size: 9373080
dataset_size: 16962106
---
# Dataset Card for "ChatDoctor-iCliniq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Otter-AI/MMVet
|
Otter-AI
| 2023-10-06T15:44:13Z | 48 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-06T14:54:36Z |
---
license: apache-2.0
---
|
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] |
lucas-meyer/asr_af
|
lucas-meyer
| 2023-10-16T20:51:26Z | 48 | 0 |
[
"region:us"
] | null | 2023-10-10T17:08:46Z |
---
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: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 1134983208.472
num_examples: 2723
- name: validation
num_bytes: 398459352.0
num_examples: 447
- name: test
num_bytes: 467308235.0
num_examples: 476
download_size: 2232381103
dataset_size: 2000750795.472
---
# Dataset Card for "asr_af"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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shossain/merged-no-pad-16384
|
shossain
| 2023-11-06T22:00:57Z | 48 | 0 |
[
"region:us"
] | null | 2023-10-16T00:46:28Z |
---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1226064029
num_examples: 6401
download_size: 336720252
dataset_size: 1226064029
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "merged-no-pad-16384"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Spiral-AI/cc100_debug
|
Spiral-AI
| 2023-10-17T04:27:52Z | 48 | 0 |
[
"region:us"
] | null | 2023-10-17T04:27:47Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 12282688
num_examples: 129838
download_size: 6976030
dataset_size: 12282688
---
# Dataset Card for "cc100_debug"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
atmallen/sharegpt-binary
|
atmallen
| 2023-10-23T21:50:35Z | 48 | 0 |
[
"region:us"
] | null | 2023-10-23T05:40:21Z |
---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: statement
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: model
dtype: string
splits:
- name: test
num_bytes: 1090167
num_examples: 243
download_size: 188810
dataset_size: 1090167
---
# Dataset Card for "sharegpt-binary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
krishan-CSE/HatEval_Relabeled
|
krishan-CSE
| 2023-10-29T11:28:24Z | 48 | 0 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"hate-speech",
"HatEval",
"region:us"
] |
[
"text-classification"
] | 2023-10-29T06:18:02Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- hate-speech
- HatEval
size_categories:
- 10K<n<100K
---
|
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] |
Santp98/query_generated-title-secop2
|
Santp98
| 2023-11-04T01:45:40Z | 48 | 0 |
[
"region:us"
] | null | 2023-10-31T03:01:42Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: process_id
dtype: string
- name: description
dtype: string
- name: generated_queries
dtype: string
- name: qty_of_bidders
dtype: int64
- name: bids_qty_by_bidders
dtype: int64
- name: publication_date
dtype: timestamp[ns]
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 37310887
num_examples: 127284
- name: test
num_bytes: 34600458
num_examples: 110263
download_size: 18543871
dataset_size: 71911345
---
# Dataset Card for "query_generated-title-secop2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
unalignment/spicy-3.1
|
unalignment
| 2023-12-02T09:20:09Z | 48 | 0 |
[
"license:apache-2.0",
"not-for-all-audiences",
"region:us"
] | null | 2023-10-31T14:31:52Z |
---
license: apache-2.0
tags:
- not-for-all-audiences
---
Airoboros 3.1 dataset with the spicy/decensorship data re-added.
|
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] |
gear42/Nuscenes-QA-merge-front-image
|
gear42
| 2023-11-09T03:55:39Z | 48 | 1 |
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] |
[
"conversational"
] | 2023-11-01T06:50:44Z |
---
task_categories:
- conversational
language:
- en
size_categories:
- 10K<n<100K
---
USAGE in Python
# load train and valid dataset
```
```
# add base_folder
```
```
|
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] |
MU-NLPC/Calc-X
|
MU-NLPC
| 2023-11-08T09:35:11Z | 48 | 0 |
[
"arxiv:2305.15017",
"arxiv:2110.14168",
"region:us"
] | null | 2023-11-05T21:27:34Z |
---
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: id
dtype: string
- name: question
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: source_ds
dtype: string
splits:
- name: train
num_bytes: 156447731.0
num_examples: 319169
- name: validation
num_bytes: 1428917
num_examples: 3277
- name: test
num_bytes: 2787009
num_examples: 6096
download_size: 73015819
dataset_size: 160663657.0
---
# Dataset Card for "Calc-X"
This dataset is a concatenation of all arithmetical reasoning datasets of [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483)
that can be used without data leakages for training, validation and testing of models for arithmetical reasoning.
Find more details in the following resources:
- [**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 (EMNLP 2023)**](https://arxiv.org/abs/2305.15017)
- [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x)
## How was this dataset created
Below is the code that was used to generate this dataset.
```python
calcx_ds_names = ["gsm8k", "ape210k", "aqua_rat", "math_qa", "svamp", "asdiv_a", "mawps"]
all_ds = {
ds_name: datasets.load_dataset(f"MU-NLPC/calc-{ds_name}")
for ds_name in calcx_ds_names
}
common_cols = ["id", "question", "chain", "result"]
calcx = datasets.DatasetDict({
split: datasets.concatenate_datasets([
(all_ds[ds_name][split]
.select_columns(common_cols)
.add_column("source_ds", [ds_name] * len(all_ds[ds_name][split]))
)
for ds_name in calcx_ds_names
if split in all_ds[ds_name]
])
for split in ["train", "validation", "test"]
})
calcx["train"] = calcx["train"].shuffle(seed=0)
```
## Cite
If you use this version of the dataset in research, please cite the [original GSM8K paper](https://arxiv.org/abs/2110.14168), and [Calc-X collection](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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vinhtran2611/zaloai-2023-nlp-en
|
vinhtran2611
| 2023-11-11T01:39:52Z | 48 | 0 |
[
"region:us"
] | null | 2023-11-10T14:38:41Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: choices
sequence: string
- name: explanation
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answer
dtype: string
- name: prompts
dtype: string
- name: labels
dtype: string
splits:
- name: train
num_bytes: 389140
num_examples: 960
- name: test
num_bytes: 47744
num_examples: 120
- name: dev
num_bytes: 48518
num_examples: 120
download_size: 269982
dataset_size: 485402
---
# Dataset Card for "zaloai-2023-nlp-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
yuufong/PhoNer_Covid19
|
yuufong
| 2023-11-13T01:18:14Z | 48 | 0 |
[
"region:us"
] | null | 2023-11-13T01:18:11Z |
---
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: words
sequence: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 2638301
num_examples: 5027
- name: validation
num_bytes: 1158651
num_examples: 2000
- name: test
num_bytes: 1158651
num_examples: 2000
download_size: 684199
dataset_size: 4955603
---
# Dataset Card for "PhoNer_Covid19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
philschmid/dolly-15k-oai-style
|
philschmid
| 2023-11-15T08:14:46Z | 48 | 0 |
[
"region:us"
] | null | 2023-11-15T08:09:58Z |
---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 12278400
num_examples: 15011
download_size: 7243728
dataset_size: 12278400
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dolly-15k-oai-style"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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openerotica/basilisk-v0.2
|
openerotica
| 2023-11-18T04:46:33Z | 48 | 5 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-18T01:39:15Z |
---
license: apache-2.0
---
This is the dataset that was used to train basilisk-7b-v0.2. It contains a subset of orca-best, as well as most of freedom-rp.
If you like what I'm trying to do, please consider subscribing to my patreon. I'm only asking for about tree fiddy.
https://patreon.com/openerotica
|
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doubledsbv/german-prefs_v4_prepared
|
doubledsbv
| 2023-11-20T09:23:48Z | 48 | 0 |
[
"region:us"
] | null | 2023-11-20T09:23:39Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 207351857
num_examples: 57380
- name: test
num_bytes: 6452941
num_examples: 1775
download_size: 120828622
dataset_size: 213804798
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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] |
sadmoseby/sample-function-call
|
sadmoseby
| 2023-11-29T01:12:04Z | 48 | 0 |
[
"region:us"
] | null | 2023-11-24T23:26:05Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2259661
num_examples: 1000
- name: validation
num_bytes: 2231397
num_examples: 1000
- name: test
num_bytes: 2233892
num_examples: 1000
- name: val
num_bytes: 2231397
num_examples: 1000
download_size: 3541865
dataset_size: 8956347
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
|
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] |
Xnhyacinth/TQA-Image
|
Xnhyacinth
| 2023-11-25T08:19:52Z | 48 | 0 |
[
"region:us"
] | null | 2023-11-25T07:49:39Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: answers
sequence: string
- name: target
dtype: string
- name: ctxs
list:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: compressed_ctxs_1
struct:
- name: compressed_prompt
dtype: string
- name: compressed_tokens
dtype: int64
- name: origin_tokens
dtype: int64
- name: ratio
dtype: string
- name: saving
dtype: string
- name: compressed_ctxs_100
struct:
- name: compressed_prompt
dtype: string
- name: compressed_tokens
dtype: int64
- name: origin_tokens
dtype: int64
- name: ratio
dtype: string
- name: saving
dtype: string
splits:
- name: train
num_bytes: 5395421949
num_examples: 78785
- name: eval
num_bytes: 605118800
num_examples: 8837
- name: test
num_bytes: 775128252
num_examples: 11313
download_size: 3917964666
dataset_size: 6775669001
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
- split: test
path: data/test-*
---
|
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hodgesz/validated-sql-create-context
|
hodgesz
| 2023-11-28T18:37:00Z | 48 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-28T18:29:41Z |
---
license: apache-2.0
---
|
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] |
Parikshith/monolingual-ha
|
Parikshith
| 2023-12-03T11:45:25Z | 48 | 0 |
[
"region:us"
] | null | 2023-11-30T02:06:33Z |
---
dataset_info:
features:
- name: ha
dtype: string
splits:
- name: complete
num_bytes: 412797567
num_examples: 3372487
- name: small
num_bytes: 12244219
num_examples: 100000
- name: one_million
num_bytes: 122335245
num_examples: 1000000
download_size: 367863048
dataset_size: 547377031
configs:
- config_name: default
data_files:
- split: complete
path: data/complete-*
- split: small
path: data/small-*
- split: one_million
path: data/one_million-*
---
|
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mespinosami/elephants_512
|
mespinosami
| 2023-11-30T17:27:55Z | 48 | 0 |
[
"region:us"
] | null | 2023-11-30T17:23:23Z |
---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 845894902.629
num_examples: 9023
download_size: 840684586
dataset_size: 845894902.629
---
# Dataset Card for "elephants_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
strombergnlp/ans-stance
|
strombergnlp
| 2022-10-25T21:45:09Z | 47 | 1 |
[
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:apache-2.0",
"stance-detection",
"arxiv:2005.10410",
"region:us"
] |
[
"text-classification"
] | 2022-05-20T12:30:15Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: ans-stance
tags:
- stance-detection
---
# Dataset Card for AraStance
## 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
- **Repository:** [https://github.com/latynt/ans](https://github.com/latynt/ans)
- **Paper:** [https://arxiv.org/abs/2005.10410](https://arxiv.org/abs/2005.10410)
- **Point of Contact:** [Jude Khouja]([email protected])
### Dataset Summary
The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance.
### Languages
Arabic
## Dataset Structure
### Data Instances
An example of 'train' looks as follows:
```
{
'id': '0',
's1': 'هجوم صاروخي يستهدف مطار في طرابلس ويجبر ليبيا على تغيير مسار الرحلات الجوية',
's2': 'هدوء الاشتباكات فى طرابلس',
'stance': 0
}
```
### Data Fields
- `id`: a 'string' feature.
- `s1`: a 'string' expressing a claim/topic.
- `s2`: a 'string' to be classified for its stance to the source.
- `stance`: a class label representing the stance the article expresses towards the claim. Full tagset with indices:
```
0: "disagree",
1: "agree",
2: "other",
```
### Data Splits
|name|instances|
|----|----:|
|train|2652|
|validation|755|
|test|379|
## 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
The dataset is curated by the paper's authors
### Licensing Information
The authors distribute this data under the Apache License, Version 2.0
### Citation Information
```
@inproceedings{,
title = "Stance Prediction and Claim Verification: An {A}rabic Perspective",
author = "Khouja, Jude",
booktitle = "Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})",
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
}
```
### Contributions
Thanks to [mkonxd](https://github.com/mkonxd) for adding this dataset.
|
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silver/lccc
|
silver
| 2022-11-06T04:51:16Z | 47 | 12 |
[
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:zh",
"license:mit",
"dialogue-response-retrieval",
"arxiv:2008.03946",
"region:us"
] |
[
"conversational"
] | 2022-05-29T09:19:28Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- zh
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
pretty_name: lccc
tags:
- dialogue-response-retrieval
---
# Dataset Card for lccc_large
## Table of Contents
- [Dataset Card for lccc_large](#dataset-card-for-lccc_large)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/thu-coai/CDial-GPT
- **Repository:** https://github.com/thu-coai/CDial-GPT
- **Paper:** https://arxiv.org/abs/2008.03946
### Dataset Summary
lccc: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. A rigorous data cleaning pipeline is designed to ensure the quality of the corpus. This pipeline involves a set of rules and several classifier-based filters. Noises such as offensive or sensitive words, special symbols, emojis, grammatically incorrect sentences, and incoherent conversations are filtered.
lccc是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。 这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。 我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。
### Supported Tasks and Leaderboards
- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.
### Languages
LCCC is in Chinese
LCCC中的对话是中文的
## Dataset Structure
### Data Instances
["火锅 我 在 重庆 成都 吃 了 七八 顿 火锅", "哈哈哈哈 ！ 那 我 的 嘴巴 可能 要 烂掉 ！", "不会 的 就是 好 油腻"]
### Data Fields
Each line is a list of utterances that consist a dialogue.
Note that the LCCC dataset provided in our original Github page is in json format,
however, we are providing LCCC in jsonl format here.
### Data Splits
We do not provide the offical split for LCCC-large.
But we provide a split for LCCC-base:
|train|valid|test|
|:---:|:---:|:---:|
|6,820,506 | 20,000 | 10,000|
## 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
Please cite the following paper if you find this dataset useful:
```bibtex
@inproceedings{wang2020chinese,
title={A Large-Scale Chinese Short-Text Conversation Dataset},
author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
booktitle={NLPCC},
year={2020},
url={https://arxiv.org/abs/2008.03946}
}
```
|
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DFKI-SLT/wikitext_linked
|
DFKI-SLT
| 2022-07-04T06:09:56Z | 47 | 5 |
[
"task_categories:fill-mask",
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:masked-language-modeling",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"task_ids:lemmatization",
"task_ids:parsing",
"task_ids:entity-linking-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|wikitext",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:1609.07843",
"region:us"
] |
[
"fill-mask",
"token-classification",
"text-classification"
] | 2022-05-30T14:26:06Z |
---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: wikitext_linked
size_categories:
- 1M<n<10M
source_datasets:
- extended|wikitext
task_categories:
- fill-mask
- token-classification
- text-classification
task_ids:
- masked-language-modeling
- named-entity-recognition
- part-of-speech
- lemmatization
- parsing
- entity-linking-classification
---
# Dataset Card for wikitext_linked
## 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:** [https://github.com/GabrielKP/svo/](https://github.com/GabrielKP/svo/)
- **Paper:** -
- **Leaderboard:** -
- **Point of Contact:** [[email protected]](mailto:[email protected])
### 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. Dependency Relations, POS, NER tags
are marked with [trankit](https://github.com/nlp-uoregon/trankit), entities are linked with
[entity-fishing](https://nerd.readthedocs.io/en/latest/index.html), which also tags another field
of NER tags. 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.
### Supported Tasks and Leaderboards
- masked-language-modeling
- named-entity-recognition
- part-of-speech
- lemmatization
- parsing
- entity-linking-classification
### Languages
English.
## Dataset Structure
### Data Instances
#### wikitext2
- **Size of downloaded dataset files:** 27.3 MB
- **Size of the generated dataset:** 197.2 MB
- **Total amount of disk used:** 197.2 MB
An example of 'validation' looks as follows.
```json
{
'text': 'It is closely related to the American lobster , H. americanus .',
'original_id': 3,
'tok_span': [[0, 0], [0, 2], [3, 5], [6, 13], [14, 21], [22, 24], [25, 28], [29, 37], [38, 45], [46, 47], [48, 50], [51, 61], [62, 63]],
'tok_upos': ['root', 'PRON', 'AUX', 'ADV', 'ADJ', 'ADP', 'DET', 'ADJ', 'NOUN', 'PUNCT', 'PROPN', 'PROPN', 'PUNCT'],
'tok_xpos': ['root', 'PRP', 'VBZ', 'RB', 'JJ', 'IN', 'DT', 'JJ', 'NN', ',', 'NNP', 'NNP', '.'],
'tok_dephead': [0, 4, 4, 4, 0, 8, 8, 8, 4, 8, 8, 10, 4],
'tok_deprel': ['root', 'nsubj', 'cop', 'advmod', 'root', 'case', 'det', 'amod', 'obl', 'punct', 'appos', 'flat', 'punct'],
'tok_lemma': [None, 'it', 'be', 'closely', 'related', 'to', 'the', 'american', 'lobster', ',', 'H.', 'americanus', '.'],
'tok_ner': [None, 'O', 'O', 'O', 'O', 'O', 'O', 'S-MISC', 'O', 'O', 'O', 'O', 'O'],
'ent_span': [[29, 45]],
'ent_wikipedia_external_ref': ['377397'],
'ent_ner': [None],
'ent_domains': [['Enterprise']],
}
```
#### wikitext103
- **Size of downloaded dataset files:** 1.11 GB
- **Size of the generated dataset:** 7.82 GB
- **Total amount of disk used:** 7.82 GB
An example of 'train' looks as follows.
```json
{
'text': 'Vision for the PlayStation Portable .',
'original_id': 3,
'tok_span': [[0, 0], [0, 6], [7, 10], [11, 14], [15, 26], [27, 35], [36, 37]],
'tok_upos': ['root', 'NOUN', 'ADP', 'DET', 'PROPN', 'PROPN', 'PUNCT'],
'tok_xpos': ['root', 'NN', 'IN', 'DT', 'NNP', 'NNP', '.'],
'tok_dephead': [0, 0, 5, 5, 5, 1, 1],
'tok_deprel': ['root', 'root', 'case', 'det', 'compound', 'nmod', 'punct'],
'tok_lemma': [None, 'vision', 'for', 'the', 'PlayStation', 'Portable', '.'],
'tok_ner': [None, 'O', 'O', 'O', 'B-MISC', 'E-MISC', 'O'],
'ent_span': [[15, 35]],
'ent_wikipedia_external_ref': ['619009'],
'ent_ner': [None],
'ent_domains': [['Electronics', 'Computer_Science']]
}
```
Use following code to print the examples nicely:
```py
def print_tokens_entities(example):
text = example['text']
print(
"Text:\n"
f" {text}"
"\nOrig-Id: "
f"{example['original_id']}"
"\nTokens:"
)
iterator = enumerate(zip(
example["tok_span"],
example["tok_upos"],
example["tok_xpos"],
example["tok_ner"],
example["tok_dephead"],
example["tok_deprel"],
example["tok_lemma"],
))
print(f" Id | {'token':12} | {'upos':8} | {'xpos':8} | {'ner':8} | {'deph':4} | {'deprel':9} | {'lemma':12} | Id")
print("---------------------------------------------------------------------------------------------------")
for idx, (tok_span, upos, xpos, ner, dephead, deprel, lemma) in iterator:
print(f" {idx:3} | {text[tok_span[0]:tok_span[1]]:12} | {upos:8} | {xpos:8} | {str(ner):8} | {str(dephead):4} | {deprel:9} | {str(lemma):12} | {idx}")
iterator = list(enumerate(zip(
example.get("ent_span", []),
example.get("ent_wikipedia_external_ref", []),
example.get("ent_ner", []),
example.get("ent_domains", []),
)))
if len(iterator) > 0:
print("Entities")
print(f" Id | {'entity':21} | {'wiki_ref':7} | {'ner':7} | domains")
print("--------------------------------------------------------------------")
for idx, ((start, end), wiki_ref, ent_ner, ent_domains) in iterator:
print(f" {idx:3} | {text[start:end]:21} | {str(wiki_ref):7} | {str(ent_ner):7} | {ent_domains}")
```
### Data Fields
The data fields are the same among all splits.
* text: string feature.
* original_id: int feature. Mapping to index within original wikitext dataset.
* tok_span: sequence of (int, int) tuples. Denotes token spans (start inclusive, end exclusive)
within each sentence.
**Note that each sentence includes an artificial root node to align dependency relations.**
* tok_upos: string feature. [Universal Dependency POS tag](https://universaldependencies.org/)
tags. Aligned with tok_span. Root node has tag "root".
* tok_xpos: string geature. [XPOS POS tag](https://trankit.readthedocs.io/en/latest/overview.html#token-list).
Aligned with tok_span. Root node has tag "root".
* tok_dephead: int feature.
[Universal Dependency Head Node](https://universaldependencies.org/introduction.html). Int refers
to tokens in tok_span. Root node has head `0` (itself).
* tok_deprel: [Universal Dependency Relation Description](https://universaldependencies.org/introduction.html).
Refers to the relation between this token and head token. Aligned with tok_span. Root node has
dependency relation "root" to itself.
* tok_lemma: string feature. Lemma of token. Aligend with tok_span.
* tok_ner: string feature. NER tag of token. Marked in BIOS schema (e.g. S-MISC, B-LOC, ...)
Aligned with tok_span. Root node has NER tag `None`.
* ent_span: sequence of (int, int) tuples. Denotes entities found by entity-fishing
(start inclusive, end exclusive).
* ent_wikipedia_external_ref: string feature. External Reference to wikipedia page. You can
access the wikipedia page via the url `https://en.wikipedia.org/wiki?curid=<ent_wikipedia_external_ref>`.
Aligend with ent_span. All entities either have this field, or the `ent_ner` field, but not both.
An empty field is denoted by the string `None`. Aligned with ent_span.
* ent_ner: string feature. Denotes NER tags. An empty field is denoted by the string `None`.
Aligned with ent_span.
"ent_domains": sequence of string. Denotes domains of entity. Can be empty sequence. Aligned with
ent_span.
### Data Splits
| name | train |validation| test|
|-------------------|------:|---------:|----:|
|wikitext103 |4076530| 8607|10062|
|wikitext2 | 82649| 8606|10062|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[https://huggingface.co/datasets/wikitext](https://huggingface.co/datasets/wikitext)
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
1. Started with `wikitext2-raw-v1` and `wikitext103-raw-v1` from [wikitext](https://huggingface.co/datasets/wikitext)
2. Ran datasets through Trankit. Marked all fields starting with `tok`.
In this step, the texts have been split into sentences. To retain the original text sections
you can accumulate over `original_id` (examples are in order).
3. Ran datasets through entity-fishing. Marked all fields starting with `ent`.
#### Who are the annotators?
Machines powered by [DFKI](https://www.dfki.de/web).
### 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
Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
### Citation Information
Please cite the original creators of wikitext, and the great people
developing trankit and entity-fishing.
```
@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}
}
@inproceedings{nguyen2021trankit,
title={Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing},
author={Nguyen, Minh Van and Lai, Viet Dac and Veyseh, Amir Pouran Ben and Nguyen, Thien Huu},
booktitle="Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
year={2021}
}
@misc{entity-fishing,
title = {entity-fishing},
howpublished = {\\url{https://github.com/kermitt2/entity-fishing}},
publisher = {GitHub},
year = {2016--2022},
archivePrefix = {swh},
eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c}
}
```
### Contributions
Thanks to [@GabrielKP](https://github.com/GabrielKP) for adding this dataset.
|
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imvladikon/nemo_corpus
|
imvladikon
| 2023-11-24T10:36:57Z | 47 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-reuters-corpus",
"language:he",
"region:us"
] |
[
"token-classification"
] | 2022-06-28T16:51:45Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- he
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-reuters-corpus
task_categories:
- token-classification
task_ids:
- named-entity-recognition
train-eval-index:
- config: nemo_corpus
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: validation
test_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# NEMO-Corpus - The Hebrew Named Entities and Morphology Corpus
## Config and Usage
Config:
* flat_token - flatten tags
* nested_token - nested tags
* flat_morph - flatten tags with morphologically presegmentized tokens
* nested_morph - nested tags with morphologically presegmentized tokens
Note: It seems that a couple of samples for the flat_token and nested_token are mistakenly presegmented, and as a result, these samples have white space in the token.
```python
from datasets import load_dataset
# the main corpus
ds = load_dataset('imvladikon/nemo_corpus', "flat_token")
for sample in ds["train"]:
print(sample)
# the nested corpus
ds = load_dataset('imvladikon/nemo_corpus', "nested_morph")
```
Getting classes and encoding/decoding could be done through these functions:
```
idx2label = dataset["train"].features["ner_tags"].feature.int2str
label2idx = dataset["train"].features["ner_tags"].feature.str2int
```
or just use raw_tags field.
## Fields
available fields (flat):
* "id"
* "sentence"
* "tokens"
* "raw_tags"
* "ner_tags"
Example of the one record for `flat`:
```json
{'id': '0', 'tokens': ['"', 'תהיה', 'נקמה', 'ו', 'בגדול', '.'], 'sentence': '" תהיה נקמה ו בגדול .', 'raw_tags': ['O', 'O', 'O', 'O', 'O', 'O'], 'ner_tags': [24, 24, 24, 24, 24, 24]}
```
Example of the one record for `nested`:
```json
{'id': '0', 'tokens': ['"', 'תהיה', 'נקמה', 'ו', 'בגדול', '.'], 'ner_tags': [24, 24, 24, 24, 24, 24], 'ner_tags_2': [24, 24, 24, 24, 24, 24], 'ner_tags_3': [24, 24, 24, 24, 24, 24], 'ner_tags_4': [24, 24, 24, 24, 24, 24]}
```
## Dataset Description
it's README.md of the [original repository](https://github.com/OnlpLab/NEMO-Corpus)
Named Entity (NER) annotations of the Hebrew Treebank (Haaretz newspaper) corpus, including: morpheme and token level NER labels, nested mentions, and more.
We publish the NEMO corpus in the TACL paper [*"Neural Modeling for Named Entities and Morphology (NEMO<sup>2</sup>)"*](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00404/107206/Neural-Modeling-for-Named-Entities-and-Morphology) [1], where we use it in extensive experiments and analyses, showing the importance of morphological boundaries for neural modeling of NER in morphologically rich languages. Code for these models and experiments can be found in the [NEMO code repo](https://github.com/OnlpLab/NEMO).
## Main features:
1. Morpheme, token-single and token-multi sequence labels. Morpheme labels provide exact boundaries, token-multi provide partial sub-word morphological but no exact boundaries, token-single provides only token-level information.
1. All annotations are in `BIOSE` format (`B`=Begin, `I`=Inside, `O`=Outside, `S`=Singleton, `E`=End).
1. Widely-used OntoNotes entity category set: `GPE` (geo-political entity), `PER` (person), `LOC` (location), `ORG` (organization), `FAC` (facility), `EVE` (event), `WOA` (work-of-art), `ANG` (language), `DUC` (product).
1. NEMO includes NER annotations for the two major versions of the Hebrew Treebank, UD (Universal Dependency) and SPMRL. These can be aligned to the other morphosyntactic information layers of the treebank using [bclm](https://github.com/OnlpLab/bclm)
1. We provide nested mentions. Only the first, widest, layer is used in the NEMO<sup>2</sup> paper. We invite you to take on this challenge!
1. Guidelines used for annotation are provided [here](./guidelines/).
1. Corpus was annotated by two native Hebrew speakers of academic education, and curated by the project manager. We provide the original annotations made by the annotators as well to promote work on [learning with disagreements](https://sites.google.com/view/semeval2021-task12/home).
1. Annotation was performed using [WebAnno](https://webanno.github.io/webanno/) (version 3.4.5)
## Legend for Files and Folder Structure
1. The two main [data](./data/) folders are [ud](./data/ud/) and [spmrl](./data/spmrl/), corresponding to the relevant Hebrew Treebank corpus version.
1. Both contain a `gold` folder ([spmrl/gold](./data/spmrl/gold/), [ud/gold](./data/ud/gold/)) of gold curated annotations.
1. Each `gold` folder contains files of the three input-output variants (morph, token-multi, token-single), for each of the treebank splits (train,dev,test).
1. Each `gold` folder also contains a `nested` subfolder ([spmrl/nested](./data/spmrl/gold/nested/), [ud/nested](./data/ud/gold/nested/)), which contains all layers of nested mentions (the first layer is the layer used in the non-nested files, and in the NEMO<sup>2</sup> paper [1])
1. The `ud` folder also contains an [ab_annotators](./data/ud/ab_annotators/) folder. This folder contains the original annotations made by each annotator (named `a`, `b`), including first-layer and nested annotatations.
1. *\*UPDATE 2021-09-06\** `ud` folder now contains a [pilot_annotations](./data/ud/pilot_annotations/) folder. This folder contains the original annotations made by each annotator in our two phase pilot (phase I - sentences 1-200 of dev; phase II - sentences 201-400 of dev).
## Basic Corpus Statistics
| | train | dev | test |
|------------------------------| --:| --:| --:|
| Sentences | 4,937 | 500 | 706 |
| Tokens | 93,504 | 8,531 | 12,619 |
| Morphemes | 127,031 | 11,301 | 16,828 |
| All mentions | 6,282 | 499 | 932 |
| Type: Person (PER) | 2,128 | 193 | 267 |
| Type: Organization (ORG) | 2,043 | 119 | 408 |
| Type: Geo-Political (GPE) | 1,377 | 121 | 195 |
| Type: Location (LOC) | 331 | 28 | 41 |
| Type: Facility (FAC) | 163 | 12 | 11 |
| Type: Work-of-Art (WOA) | 114 | 9 | 6 |
| Type: Event (EVE) | 57 | 12 | 0 |
| Type: Product (DUC) | 36 | 2 | 3 |
| Type: Language (ANG) | 33 | 3 | 1 |
## Aligned Treenbank Versions
The NEMO corpus matches the treebank version of [bclm v.1.0.0](https://github.com/OnlpLab/bclm/releases/tag/v1.0.0-alpha).
This version is based on the [HTB UD v2.2](https://github.com/UniversalDependencies/UD_Hebrew-HTB/releases/tag/r2.2) and the [latest SPMRL HTB version](https://github.com/OnlpLab/HebrewResources/tree/102674bb030f5836e1ab827feb63954ad7a6f8fe/HebrewTreebank/hebtb).
The changes contain (but might not be limited to the following):
1. Flagged and dropped duplicate and leaking sentences (between train and test). In addition to the sentences already removed in the bclm v1.0.0 HTB version, the following duplicate sentences were dropped as well (SPMRL sentence IDs): 5438, 5444, 5445, 5446, 5448, 5449, 5450, 5451, 5453, 5459 (in the bclm dataframes, these are marked in the `duplicate_sent_id` column).
To read the treebank (UD/SPMRL) in a way that matches the NEMO corpus, you can use the following:
```python
import bclm
dropped = [5438, 5444, 5445, 5446, 5448, 5449, 5450, 5451, 5453, 5459]
spdf = bclm.read_dataframe('spmrl') # load SPMRL treebank dataframe
global_dropped = [spdf[spdf.sent_id==d].global_sent_id.iat[0] for d in dropped]
uddf = bclm.read_dataframe('ud') # load UD treebank dataframe
uddf = uddf[(~uddf.global_sent_id.isin(global_dropped))] # remove extra duplicates
spdf = spdf[(~spdf.sent_id.isin(dropped))] # remove extra duplicates
# The resulting dataframes contain gold morph NER labels in the `biose_layer0`, `biose_layer1`... columns.
```
2. The UD treebank contains many more duplicates. In this version: all sentences exist in both UD and SPMRL versions, and all sentences and tokens are aligned between UD and SPMRL.
2. Fixed numbers that were originally reversed.
2. Fixed mismatches between tokens and morphemes.
2. Added Binyan feature.
2. No individual morphemes or tokens were added or removed, only complete sentences.
## Evaluation
An evaluation script is provided in the [NEMO code repo](https://github.com/OnlpLab/NEMO#evaluation) along with evaluation instructions.
## Citations
##### [1]
If you use the NEMO corpus in your research, please cite the NEMO<sup>2</sup> paper:
```bibtex
@article{10.1162/tacl_a_00404,
author = {Bareket, Dan and Tsarfaty, Reut},
title = "{Neural Modeling for Named Entities and Morphology (NEMO2)}",
journal = {Transactions of the Association for Computational Linguistics},
volume = {9},
pages = {909-928},
year = {2021},
month = {09},
abstract = "{Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically rich languages (MRLs) pose a challenge to this basic formulation, as the boundaries of named entities do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings (i.e., where no gold morphology is available). We empirically investigate these questions on a novel NER benchmark, with parallel token- level and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.}",
issn = {2307-387X},
doi = {10.1162/tacl_a_00404},
url = {https://doi.org/10.1162/tacl\_a\_00404},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00404/1962472/tacl\_a\_00404.pdf},
}
```
##### [2]
Please cite the Hebrew Treebank as well, described the following paper:
```bibtex
@article{sima2001building,
title={Building a tree-bank of modern Hebrew text},
author={Sima’an, Khalil and Itai, Alon and Winter, Yoad and Altman, Alon and Nativ, Noa},
journal={Traitement Automatique des Langues},
volume={42},
number={2},
pages={247--380},
year={2001},
publisher={Citeseer}
}
```
##### [3]
The UD version of the Hebrew Treebank is described in:
```bibtex
@inproceedings{sade-etal-2018-hebrew,
title = "The {H}ebrew {U}niversal {D}ependency Treebank: Past Present and Future",
author = "Sade, Shoval and
Seker, Amit and
Tsarfaty, Reut",
booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6016",
doi = "10.18653/v1/W18-6016",
pages = "133--143",
abstract = "The Hebrew treebank (HTB), consisting of 6221 morpho-syntactically annotated newspaper sentences, has been the only resource for training and validating statistical parsers and taggers for Hebrew, for almost two decades now. During these decades, the HTB has gone through a trajectory of automatic and semi-automatic conversions, until arriving at its UDv2 form. In this work we manually validate the UDv2 version of the HTB, and, according to our findings, we apply scheme changes that bring the UD HTB to the same theoretical grounds as the rest of UD. Our experimental parsing results with UDv2New confirm that improving the coherence and internal consistency of the UD HTB indeed leads to improved parsing performance. At the same time, our analysis demonstrates that there is more to be done at the point of intersection of UD with other linguistic processing layers, in particular, at the points where UD interfaces external morphological and lexical resources.",
}
```
|
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] |
yeeb/C50
|
yeeb
| 2022-10-26T05:55:06Z | 47 | 0 |
[
"license:openrail",
"region:us"
] | null | 2022-10-26T05:49:50Z |
---
license: openrail
---
## Dataset Description
The dataset is the subset of RCV1. These corpus has already been used in author identification experiments. In the top 50 authors (with respect to total size of articles) were selected. 50 authors of texts labeled with at least one subtopic of the class CCAT(corporate/industrial) were selected.That way, it is attempted to minimize the topic factor in distinguishing among the texts. The training corpus consists of 2,500 texts (50 per author) and the test corpus includes other 2,500 texts (50 per author) non-overlapping with the training texts.
- **Homepage:** https://archive.ics.uci.edu/ml/datasets/Reuter_50_50
- **Repository:** https://archive.ics.uci.edu/ml/datasets/Reuter_50_50
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
|
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Sociovestix/lenu
|
Sociovestix
| 2023-12-04T13:48:09Z | 47 | 1 |
[
"region:us"
] | null | 2022-11-22T17:24:58Z |
---
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---
# Dataset Card for "LENU - Legal Entity Name Understanding"
---------------
<h1 align="center">
<a href="https://gleif.org">
<img src="http://sdglabs.ai/wp-content/uploads/2022/07/gleif-logo-new.png" width="220px" style="display: inherit">
</a>
</h1><br>
<h3 align="center">in collaboration with</h3>
<h1 align="center">
<a href="https://sociovestix.com">
<img src="https://sociovestix.com/img/svl_logo_centered.svg" width="700px" style="width: 100%">
</a>
</h1><br>
---------------
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [gleif.org](https://gleif.org)
- **Repository:** [The LENU project](https://github.com/Sociovestix/lenu)
- **Point of Contact:** [aarimond](https://huggingface.co/aarimond)
### Dataset Summary
This dataset contains legal entity names from the Global LEI System in which each entity is
assigned with a unique
[Legal Entity Identifier](https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei)
(LEI) code (ISO Standard 17441)
along with their corresponding
[Entity Legal Form (ELF) Codes](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list)
(ISO Standard 20275),
which specifies the legal form of each entity.
The dataset has been created as part of a collaboration of the [Global Legal Entity Identifier Foundation](https://gleif.org) (GLEIF) and
[Sociovestix Labs](https://sociovestix.com) with the goal to explore how Machine Learning can support in detecting the ELF Code solely based on an entity's legal name and legal jurisdiction.
See also the open source python library [LENU](https://github.com/Sociovestix/lenu), which supports in this task.
The data is created from LEI data downloaded from
[GLEIF's public website](https://www.gleif.org/en/lei-data/gleif-golden-copy/download-the-golden-copy/)
(Date: 2022-11-01 00:00), where it is accessible free of charge.
It is divided into subsets for a selection of legal jurisdictions, whereas each Jurisdiction has its own set of ELF Codes.
The ELF Code reference list can be downloaded [here](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list).
### Languages
The data contains several major Jurisdictions (e.g. US-DE (US Delaware), JP (Japan), DE (Germany) and others).
Legal entity names usually follow certain language patterns, depending on which jurisdiction they are located in.
Thus, we apply models that are pre-trained on the corresponding language.
## Dataset Structure
### Data Instances
The data contains of the LEI, the corresponding legal name and ELF Code.
```
{
'LEI': '254900OMZ079O2SDWA75',
'Entity.LegalName': 'Park Reseda Mortgage LLC',
'Entity.LegalForm.EntityLegalFormCode': 0
}
```
### Data Fields
This is just a subset of available fields in the LEI system. All fields are described in detail in GLEIF's
[LEI Common Data Format (CDF)](https://www.gleif.org/en/about-lei/common-data-file-format/current-versions/level-1-data-lei-cdf-3-1-format).
- `LEI`: The [Legal Entity Identifier](https://www.gleif.org/en/about-lei/introducing-the-legal-entity-identifier-lei) Code. Uniquely identifies a Legal Entity.
- `Entity.LegalName`: The official name of the legal entity as registered in the LEI system.
- `Entity.LegalForm.EntityLegalFormCode`: class encoded column which contains the [Entity Legal Form Code](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list)
### Data Splits
We have divided each jurisdiction's subset into stratified train (70%), validation (10%) and test (20%) splits.
ELF Codes that appear less than three times in a Jurisdiction have been removed.
## Licensing Information
This dataset, which is based on LEI data, is available under Creative Commons (CC0) license.
See [gleif.org/en/about/open-data](https://gleif.org/en/about/open-data).
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language-and-voice-lab/samromur_children
|
language-and-voice-lab
| 2023-10-15T16:02:44Z | 47 | 2 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:is",
"license:cc-by-4.0",
"samromur",
"children's speech",
"icelandic: iceland",
"icelandic children",
"icelandic kids",
"kids",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-11-26T03:15:54Z |
---
annotations_creators:
- crowdsourced
language:
- is
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: "Samrómur Children Icelandic Speech 1.0"
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- "samromur"
- children's speech
- 'icelandic: iceland'
- icelandic children
- icelandic kids
- kids
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for samromur_children
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-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:** [Samrómur Children Icelandic Speech 1.0](https://samromur.is/)
- **Repository:** [LDC](https://catalog.ldc.upenn.edu/LDC2022S11)
- **Paper:** [Samrómur Children: An Icelandic Speech Corpus](https://aclanthology.org/2022.lrec-1.105.pdf)
- **Point of Contact:** [Carlos Mena](mailto:[email protected]), [Jón Guðnason](mailto:[email protected])
### Dataset Summary
The Samrómur Children Corpus consists of audio recordings and metadata files containing prompts read by the participants. It contains more than 137000 validated speech-recordings uttered by Icelandic children.
The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021).
### Example Usage
The Samrómur Children Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:
```python
from datasets import load_dataset
samromur_children = load_dataset("language-and-voice-lab/samromur_children")
```
To load an specific split (for example, the validation split) do:
```python
from datasets import load_dataset
samromur_children = load_dataset("language-and-voice-lab/samromur_children",split="validation")
```
### Supported Tasks
automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
### Languages
The audio is in Icelandic.
The reading prompts were gathered from a variety of sources, mainly from the [Icelandic Gigaword Corpus](http://clarin.is/en/resources/gigaword). The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/).
## Dataset Structure
### Data Instances
```python
{
'audio_id': '015652-0717240',
'audio': {
'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/2c6b0d82de2ef0dc0879732f726809cccbe6060664966099f43276e8c94b03f2/test/015652/015652-0717240.flac',
'array': array([ 0. , 0. , 0. , ..., -0.00311279,
-0.0007019 , 0.00128174], dtype=float32),
'sampling_rate': 16000
},
'speaker_id': '015652',
'gender': 'female',
'age': '11',
'duration': 4.179999828338623,
'normalized_text': 'eiginlega var hann hin unga rússneska bylting lifandi komin'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `speaker_id` (string) - id of speaker
* `gender` (string) - gender of speaker (male or female)
* `age` (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+).
* `duration` (float32) - duration of the audio file in seconds.
* `normalized_text` (string) - normalized audio segment transcription.
### Data Splits
The corpus is split into train, dev, and test portions. Lenghts of every portion are: train = 127h25m, test = 1h50m, dev=1h50m.
To load an specific portion please see the above section "Example Usage".
## Dataset Creation
### Curation Rationale
In the field of Automatic Speech Recognition (ASR) is a known fact that the children's speech is particularly hard to recognise due to its high variability produced by developmental changes in children's anatomy and speech production skills.
For this reason, the criteria of selection for the train/dev/test portions have to take into account the children's age. Nevertheless, the Samrómur Children is an unbalanced corpus in terms of gender and age of the speakers. This means that the corpus has, for example, a total of 1667 female speakers (73h38m) versus 1412 of male speakers (52h26m).
These unbalances impose conditions in the type of the experiments than can be performed with the corpus. For example, a equal number of female and male speakers through certain ranges of age is impossible. So, if one can't have a perfectly balance corpus in the training set, at least one can have it in the test portion.
The test portion of the Samrómur Children was meticulously selected to cover ages between 6 to 16 years in both female and male speakers. Every of these range of age in both genders have a total duration of 5 minutes each.
The development portion of the corpus contains only speakers with an unknown gender information. Both test and dev sets have a total duration of 1h50m each.
In order to perform fairer experiments, speakers in the train and test sets are not shared. Nevertheless, there is only one speaker shared between the train and development set. It can be identified with the speaker ID=010363. However, no audio files are shared between these two sets.
### Source Data
#### Initial Data Collection and Normalization
The data was collected using the website https://samromur.is, code of which is available at https://github.com/cadia-lvl/samromur. The age range selected for this corpus is between 4 and 17 years.
The original audio was collected at 44.1 kHz or 48 kHz sampling rate as *.wav files, which was down-sampled to 16 kHz and converted to *.flac. Each recording contains one read sentence from a script. The script contains 85.080 unique sentences and 90.838 unique tokens.
There was no identifier other than the session ID, which is used as the speaker ID. The corpus is distributed with a metadata file with a detailed information on each utterance and speaker. The madata file is encoded as UTF-8 Unicode.
The prompts were gathered from a variety of sources, mainly from The Icelandic Gigaword Corpus, which is available at http://clarin.is/en/resources/gigaword. The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/).
### Annotations
#### Annotation process
Prompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the [DIM: Database Icelandic Morphology](https://aclanthology.org/W19-6116.pdf).
There are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device.
#### Who are the annotators?
The audio files content was manually verified against the prompts by one or more listener (summer students mainly).
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This is the first ASR corpus of Icelandic children.
### Discussion of Biases
* The utterances were recorded by a smartphone or the web app.
* Participants self-reported their age group, gender, and the native language.
* Participants are aged between 4 to 17 years.
* The corpus contains 137597 utterances from 3175 speakers, totalling 131 hours.
* The amount of data due to female speakers is 73h38m, the amount of data due to male speakers is 52h26m and the amount of data due to speakers with an unknown gender information is 05h02m
* The number of female speakers is 1667, the number of male speakers is 1412. The number of speakers with an unknown gender information is 96.
* The audios due to female speakers are 78993, the audios due to male speakers are 53927 and the audios due to speakers with an unknown gender information are 4677.
### Other Known Limitations
"Samrómur Children: Icelandic Speech 21.09" by the Language and Voice Laboratory (LVL) at the Reykjavik University is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
## Additional Information
### Dataset Curators
The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021). The corpus was curated by Carlos Daniel Hernández Mena in 2021.
### Licensing Information
[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@misc{menasamromurchildren2021,
title={Samrómur Children Icelandic Speech 1.0},
ldc_catalog_no={LDC2022S11},
DOI={https://doi.org/10.35111/frrj-qd60},
author={Hernández Mena, Carlos Daniel and Borsky, Michal and Mollberg, David Erik and Guðmundsson, Smári Freyr and Hedström, Staffan and Pálsson, Ragnar and Jónsson, Ólafur Helgi and Þorsteinsdóttir, Sunneva and Guðmundsdóttir, Jóhanna Vigdís and Magnúsdóttir, Eydís Huld and Þórhallsdóttir, Ragnheiður and Guðnason, Jón},
publisher={Reykjavík University},
journal={Linguistic Data Consortium, Philadelphia},
year={2021},
url={https://catalog.ldc.upenn.edu/LDC2022S11},
}
```
### Contributions
This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.
The verification for the dataset was funded by the the Icelandic Directorate of Labour's Student Summer Job Program in 2020 and 2021.
Special thanks for the summer students for all the hard work.
|
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sander-wood/irishman
|
sander-wood
| 2023-09-25T15:14:16Z | 47 | 11 |
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"license:mit",
"music",
"region:us"
] |
[
"text-generation"
] | 2023-01-10T23:42:04Z |
---
license: mit
task_categories:
- text-generation
pretty_name: IrishMAN
size_categories:
- 100K<n<1M
tags:
- music
---
If you prefer MIDI or MusicXML, download [IrishMAN-MIDI](https://huggingface.co/datasets/sander-wood/irishman/resolve/main/irishman-midi.zip) or [IrishMAN-XML](https://huggingface.co/datasets/sander-wood/irishman/resolve/main/irishman-xml.zip). For better use of structural info in control codes, consider ABC notation.
## ABC Notation
ABC notation is an ASCII-based plain text musical notation system that is commonly used for transcribing traditional music and sharing sheet music online. It provides a simple and concise way to represent musical elements such as notes, rhythms, chords, and more.
For those looking to interact with ABC notation in various ways, there are several tools available:
1. **[Online ABC Player](https://abc.rectanglered.com/):** This web-based tool allows you to input ABC notation and hear the corresponding audio playback. By pasting your ABC code into the player, you can instantly listen to the tune as it would sound when played.
2. **[ABC Sheet Music Editor - EasyABC](https://easyabc.sourceforge.net/):** EasyABC is a user-friendly software application designed for creating, editing, and formatting ABC notation. Its graphical interface enables you to input your ABC code, preview the sheet music, and make adjustments as necessary.
## Dataset Summary
The **Irish Massive ABC Notation (IrishMAN)** dataset includes 216,284 Irish tunes in ABC notation, divided into 99\% (214,122 tunes) for training and 1\% (2,162 tunes) for validation. These tunes were collected from thesession.org and abcnotation.com, both renowned for sharing traditional music. To ensure uniformity in formatting, all tunes were converted to XML and then back to ABC using [scripts](https://wim.vree.org/svgParse/), and fields containing natural language (e.g., titles and lyrics) were removed.
Each tune is automatically annotated with control codes derived from ABC symbols, as described in the below section. These control codes offer insights into the musical forms and structures of each composition.
In the IrishMAN dataset, a [music21](https://web.mit.edu/music21/doc/index.html#)-filtered [subset](https://huggingface.co/datasets/sander-wood/irishman/raw/main/leadsheet_ids.json) includes 34,211 lead sheets, each human-annotated with chord symbols. It is from this very subset that [TunesFormer](https://huggingface.co/sander-wood/tunesformer) developed its capacity to generate melodies with harmonies.
A noteworthy aspect is the copyright status. All tunes in the dataset are in the public domain, ensuring ethical and legal usage for research and creative projects.
## Control Codes
Inspired by [CTRL](https://huggingface.co/ctrl), we incorporate control codes into TunesFormer to represent musical forms. These codes, positioned ahead of the ABC notation, enable users to specify the structures of the generated tunes. The following control codes are introduced:
- **S:number of sections**: determines the number of sections in the entire melody. It counts on several symbols that can be used to represent section boundaries: `[|`, `||`, `|]`, `|:`, `::`, and `:|`. In our dataset, the range is 1 to 8 (e.g., `S:1` for a single-section melody, and `S:8` for a melody with eight sections).
- **B:number of bars**: specifies the desired number of bars within a section. It counts on the bar symbol `|`. In our dataset, the range is 1 to 32 (e.g., `B:1` for a one-bar section, and `B:32` for a section with 32 bars).
- **E:edit distance similarity**: controls the similarity level between the current section $c$ and a previous section $p$ in the melody. It is based on the Levenshtein distance $lev(c,p)$ , quantifying the difference between sections for creating variations or contrasts. Mathematically, it can be expressed as:
```
eds(c,p) = 1 - lev(c,p) / max(|c|,|p|)
```
where $|c|$ and $|p|$ are the string lengths of the two sections. It is discretized into 11 levels, ranging from no match at all to an exact match (e.g., `E:0` for no similarity, and `E:10` for an exact match).
## Copyright Disclaimer
This dataset is for research use only and not for commercial purposes. We believe all data in this dataset is in the public domain. If you own the copyright to any musical composition in the IrishMAN dataset and have concerns, please contact us at [email protected]. We will address your concerns and take appropriate action if needed.
## Special Thanks
We would like to extend a special thanks to thesession.org and abcnotation.com for their contributions to the development and promotion of ABC notation, as well as their significant impact on the field of music information retrieval. Their platforms have become invaluable resources for the traditional and folk music community. We also wish to express our gratitude to Willem (Wim) for providing the essential conversion tools that enabled the transformation of the tunes into a uniform format. Together, these collaborations have made it possible for researchers like us to create and study extensive datasets like IrishMAN.
|
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dipesh/Intent-Classification-small
|
dipesh
| 2023-01-27T22:08:26Z | 47 | 0 |
[
"region:us"
] | null | 2023-01-27T22:08:07Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: intent
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: label
dtype:
class_label:
names:
'0': goodbye
'1': volume control
'2': play games
'3': covid cases
'4': open website
'5': tell me joke
'6': play on youtube
'7': places near me
'8': greet and hello hi kind of things, general check in
'9': asking time
'10': asking date
'11': tell me news
'12': asking weather
'13': download youtube video
'14': what can you do
'15': take screenshot
'16': send email
'17': i am bored
'18': click photo
'19': tell me about
'20': send whatsapp message
splits:
- name: train
num_bytes: 630723
num_examples: 6153
- name: validation
num_bytes: 71230
num_examples: 684
download_size: 201336
dataset_size: 701953
---
# Dataset Card for "Intent-Classification-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Multimodal-Fatima/VQAv2_sample_validation
|
Multimodal-Fatima
| 2023-06-09T00:06:10Z | 47 | 0 |
[
"region:us"
] | null | 2023-02-10T17:59:57Z |
---
dataset_info:
features:
- name: question_type
dtype: string
- name: multiple_choice_answer
dtype: string
- name: answers
sequence: string
- name: answers_original
list:
- name: answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: id_image
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: image
dtype: image
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
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dtype: string
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list:
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list:
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list:
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sequence: string
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sequence: string
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sequence: string
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sequence: string
- name: blip_caption_beam_5_Salesforce_blip2_flan_t5_xxl
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- name: DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_
list:
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list:
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- name: blip_caption_topk_50_Salesforce_blip_image_captioning_base_multiple
sequence: string
- name: DETA_detections_deta_swin_large_o365_clip_caption_all_patches_Salesforce_blip_image_captioning_large__ViT_L_14
list:
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splits:
- name: validation
num_bytes: 511357022.0
num_examples: 1000
download_size: 293191811
dataset_size: 511357022.0
---
# Dataset Card for "VQAv2_sample_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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RiniPL/Dementia_Dataset
|
RiniPL
| 2023-03-15T07:48:14Z | 47 | 3 |
[
"task_categories:image-classification",
"language:en",
"license:ecl-2.0",
"code",
"region:us"
] |
[
"image-classification"
] | 2023-03-15T05:57:38Z |
---
license: ecl-2.0
task_categories:
- image-classification
language:
- en
tags:
- code
pretty_name: Dementia
---
|
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NbAiLab/norwegian-alpaca
|
NbAiLab
| 2023-07-25T15:05:00Z | 47 | 8 |
[
"task_categories:text-generation",
"language:no",
"language:nb",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] |
[
"text-generation"
] | 2023-03-20T13:14:23Z |
---
license: cc-by-4.0
language:
- 'no'
- nb
tags:
- instruction-finetuning
pretty_name: NB Alpaca Norwegian Bokmål
task_categories:
- text-generation
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: instruction_en
dtype: string
- name: input_en
dtype: string
- name: output_en
dtype: string
splits:
- name: train
num_bytes: 38067492
num_examples: 51942
download_size: 24204487
dataset_size: 38067492
---
# NB Alpaca Norwegian Bokmål
This dataset is a translation to Norwegian Bokmål of [alpaca_data_cleaned.json](https://github.com/tloen/alpaca-lora/blob/main/alpaca_data_cleaned.json), a clean version of the [Alpaca dataset made at Stanford](https://huggingface.co/datasets/tatsu-lab/alpaca).
An [earlier version](https://huggingface.co/datasets/NbAiLab/norwegian-alpaca/tree/main/nllb) used [Facebook's NLLB 1.3B model](https://huggingface.co/facebook/nllb-200-1.3B), but the current version uses OpenAI's `gpt-3.5-turbo`, hence this dataset cannot be used to create models that compete in any way against OpenAI.
|
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liuyanchen1015/MULTI_VALUE_wnli_she_inanimate_objects
|
liuyanchen1015
| 2023-04-03T19:47:26Z | 47 | 0 |
[
"region:us"
] | null | 2023-04-03T19:47:22Z |
---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 3305
num_examples: 21
- name: test
num_bytes: 12409
num_examples: 44
- name: train
num_bytes: 29515
num_examples: 166
download_size: 23893
dataset_size: 45229
---
# Dataset Card for "MULTI_VALUE_wnli_she_inanimate_objects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Hemanth-thunder/en_ta
|
Hemanth-thunder
| 2023-08-12T06:58:11Z | 47 | 2 |
[
"size_categories:10K<n<100K",
"language:ta",
"language:en",
"license:mit",
"region:us"
] | null | 2023-05-16T07:45:59Z |
---
license: mit
language:
- ta
- en
size_categories:
- 10K<n<100K
---
|
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] |
KaiLv/UDR_Amazon
|
KaiLv
| 2023-06-21T12:23:17Z | 47 | 0 |
[
"region:us"
] | null | 2023-06-21T12:22:34Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: label
dtype: int64
- name: headline
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 13936883
num_examples: 30000
- name: test
num_bytes: 1382953
num_examples: 3000
- name: debug
num_bytes: 2318411
num_examples: 5000
download_size: 11799872
dataset_size: 17638247
---
# Dataset Card for "UDR_Amazon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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DynamicSuperb/SpoofDetection_ASVspoof2017
|
DynamicSuperb
| 2023-07-31T10:54:40Z | 47 | 0 |
[
"region:us"
] | null | 2023-07-13T03:40:36Z |
---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: instruction
dtype: string
- name: label
dtype: string
splits:
- name: test
num_bytes: 1411064438.928
num_examples: 13306
download_size: 1361993549
dataset_size: 1411064438.928
---
# Dataset Card for "SpoofDetection_ASVspoof2017"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_chargoddard__platypus2-22b-relora
|
open-llm-leaderboard
| 2023-10-25T20:25:34Z | 47 | 0 |
[
"region:us"
] | null | 2023-08-26T01:20:09Z |
---
pretty_name: Evaluation run of chargoddard/platypus2-22b-relora
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/platypus2-22b-relora](https://huggingface.co/chargoddard/platypus2-22b-relora)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__platypus2-22b-relora\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T20:25:26.109513](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__platypus2-22b-relora/blob/main/results_2023-10-25T20-25-26.109513.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3890520134228188,\n\
\ \"em_stderr\": 0.004992816489160872,\n \"f1\": 0.42904677013422904,\n\
\ \"f1_stderr\": 0.0048769344943773545,\n \"acc\": 0.41701966223724296,\n\
\ \"acc_stderr\": 0.00924806140277906\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3890520134228188,\n \"em_stderr\": 0.004992816489160872,\n\
\ \"f1\": 0.42904677013422904,\n \"f1_stderr\": 0.0048769344943773545\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06292645943896892,\n \
\ \"acc_stderr\": 0.0066887625815327196\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.771112865035517,\n \"acc_stderr\": 0.0118073602240254\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/platypus2-22b-relora
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|arc:challenge|25_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|arc:challenge|25_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T12_51_55.075795
path:
- '**/details_harness|drop|3_2023-10-16T12-51-55.075795.parquet'
- split: 2023_10_25T20_25_26.109513
path:
- '**/details_harness|drop|3_2023-10-25T20-25-26.109513.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T20-25-26.109513.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T12_51_55.075795
path:
- '**/details_harness|gsm8k|5_2023-10-16T12-51-55.075795.parquet'
- split: 2023_10_25T20_25_26.109513
path:
- '**/details_harness|gsm8k|5_2023-10-25T20-25-26.109513.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T20-25-26.109513.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hellaswag|10_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hellaswag|10_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T01:19:46.876046.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T01:19:46.876046.parquet'
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- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T01:19:46.876046.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T01:19:46.876046.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T22-48-46.274282.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T22-48-46.274282.parquet'
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- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T22-48-46.274282.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T22-48-46.274282.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T22-48-46.274282.parquet'
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- split: latest
path:
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T22-48-46.274282.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T22-48-46.274282.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T22-48-46.274282.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T01:19:46.876046.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T01:19:46.876046.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T01:19:46.876046.parquet'
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T01:19:46.876046.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T01:19:46.876046.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T01:19:46.876046.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T01:19:46.876046.parquet'
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T01:19:46.876046.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T01:19:46.876046.parquet'
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T01:19:46.876046.parquet'
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T01:19:46.876046.parquet'
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path:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T01:19:46.876046.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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- split: 2023_09_12T22_48_46.274282
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T01:19:46.876046.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
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- split: 2023_09_12T22_48_46.274282
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
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- split: 2023_09_12T22_48_46.274282
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T22-48-46.274282.parquet'
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T01:19:46.876046.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T01:19:46.876046.parquet'
- split: 2023_09_12T22_48_46.274282
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T22-48-46.274282.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T22-48-46.274282.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T12_51_55.075795
path:
- '**/details_harness|winogrande|5_2023-10-16T12-51-55.075795.parquet'
- split: 2023_10_25T20_25_26.109513
path:
- '**/details_harness|winogrande|5_2023-10-25T20-25-26.109513.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T20-25-26.109513.parquet'
- config_name: results
data_files:
- split: 2023_08_26T01_19_46.876046
path:
- results_2023-08-26T01:19:46.876046.parquet
- split: 2023_09_12T22_48_46.274282
path:
- results_2023-09-12T22-48-46.274282.parquet
- split: 2023_10_16T12_51_55.075795
path:
- results_2023-10-16T12-51-55.075795.parquet
- split: 2023_10_25T20_25_26.109513
path:
- results_2023-10-25T20-25-26.109513.parquet
- split: latest
path:
- results_2023-10-25T20-25-26.109513.parquet
---
# Dataset Card for Evaluation run of chargoddard/platypus2-22b-relora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/platypus2-22b-relora
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/platypus2-22b-relora](https://huggingface.co/chargoddard/platypus2-22b-relora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chargoddard__platypus2-22b-relora",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T20:25:26.109513](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__platypus2-22b-relora/blob/main/results_2023-10-25T20-25-26.109513.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.3890520134228188,
"em_stderr": 0.004992816489160872,
"f1": 0.42904677013422904,
"f1_stderr": 0.0048769344943773545,
"acc": 0.41701966223724296,
"acc_stderr": 0.00924806140277906
},
"harness|drop|3": {
"em": 0.3890520134228188,
"em_stderr": 0.004992816489160872,
"f1": 0.42904677013422904,
"f1_stderr": 0.0048769344943773545
},
"harness|gsm8k|5": {
"acc": 0.06292645943896892,
"acc_stderr": 0.0066887625815327196
},
"harness|winogrande|5": {
"acc": 0.771112865035517,
"acc_stderr": 0.0118073602240254
}
}
```
### 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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] |
madaanpulkit/tab-wnut
|
madaanpulkit
| 2023-11-02T06:07:27Z | 47 | 0 |
[
"region:us"
] | null | 2023-10-11T07:38:29Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: tagged_text
sequence: string
- name: tags
sequence:
class_label:
names:
'0': '0'
'1': B-DIRECT-CODE
'2': I-DIRECT-CODE
'3': B-DIRECT-PERSON
'4': I-DIRECT-PERSON
'5': B-QUASI-DATETIME
'6': I-QUASI-DATETIME
'7': B-QUASI-PERSON
'8': I-QUASI-PERSON
'9': B-QUASI-LOC
'10': I-QUASI-LOC
'11': B-QUASI-QUANTITY
'12': I-QUASI-QUANTITY
'13': B-QUASI-CODE
'14': I-QUASI-CODE
'15': B-QUASI-ORG
'16': I-QUASI-ORG
'17': B-QUASI-DEM
'18': I-QUASI-DEM
'19': B-QUASI-MISC
'20': I-QUASI-MISC
'21': B-DIRECT-ORG
'22': I-DIRECT-ORG
'23': B-DIRECT-DATETIME
'24': I-DIRECT-DATETIME
'25': B-DIRECT-LOC
'26': I-DIRECT-LOC
'27': B-DIRECT-MISC
'28': I-DIRECT-MISC
'29': B-DIRECT-DEM
'30': I-DIRECT-DEM
splits:
- name: train
num_bytes: 45872319
num_examples: 1014
- name: dev
num_bytes: 3749307
num_examples: 127
- name: test
num_bytes: 3619745
num_examples: 127
download_size: 11056816
dataset_size: 53241371
---
# Dataset Card for "tab-wnut"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
crumb/c4-benchfilter-nano
|
crumb
| 2023-10-22T19:22:56Z | 47 | 2 |
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"language_creators:found",
"size_categories:100K<n<1M",
"source_datasets:c4",
"language:en",
"license:odc-by",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2023-10-22T06:20:10Z |
---
language_creators:
- found
language:
- en
license: odc-by
source_datasets:
- c4
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
features:
- name: text
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 373897649.51453334
num_examples: 278115
download_size: 242478448
dataset_size: 373897649.51453334
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
size_categories:
- 100K<n<1M
---
# crumb/c4-benchfilter-nano
A 278k sample derivation of the first 3M samples from the C4 dataset for a cheap and short continued pretraining for language models to optimize for benchmark scores without sacrificing generalization and generative modelling unrelated to chat or 'instruct' data.
The estimated top 10% of highest estimated length normalized ngram (mean of tri, quad, and penta-gram) overlaps for each of the
selected benchmark datasets (arc, truthful_qa, hellaswag, mmlu, humaneval) based
on 1k samples, within the first 3M samples of C4. The top scoring sample
datasets for each benchmark are then filtered again for top 30% scores and
combined and exact-match de-duplicated. Then the top 3% scores and samples less than 200 characters long are removed
because they likely have exact large n-token matches by chance such as exact
dates or times that aren't actually relevant to the data.\*
\*Upon further examination, some of these samples are still present throughout the data, albeit at much lower frequency than before, you might benefit from using `dataset.filter(x['score'] > thresh)` for some threshold, but you risk losing high quality samples as well, this tradeoff should be well-examined before training.
|
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] |
AdapterOcean/med_alpaca_standardized_cluster_85_std
|
AdapterOcean
| 2023-10-24T02:22:46Z | 47 | 0 |
[
"region:us"
] | null | 2023-10-24T02:22:42Z |
---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1664891
num_examples: 10997
download_size: 681626
dataset_size: 1664891
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_85_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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krishaamer/taiwanese-college-students
|
krishaamer
| 2023-11-02T13:13:35Z | 47 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-31T02:56:27Z |
---
license: apache-2.0
---
|
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] |
ftang97/sw-consultancy-agent
|
ftang97
| 2023-11-03T15:23:28Z | 47 | 0 |
[
"region:us"
] | null | 2023-11-03T15:23:23Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 2311272.0
num_examples: 282
- name: test
num_bytes: 262272.0
num_examples: 32
download_size: 1195336
dataset_size: 2573544.0
---
# Dataset Card for "sw-consultancy-agent"
[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_ru
|
bragovo
| 2023-11-05T18:59:11Z | 47 | 0 |
[
"language:ru",
"region:us"
] | null | 2023-11-04T07:26:32Z |
---
configs:
- config_name: dsum
task: summarization
data_files:
- split: train
path: data/train-*
language:
- ru
---
|
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] |
alexemanuel27/org-acad-train-test
|
alexemanuel27
| 2023-11-04T18:21:26Z | 47 | 0 |
[
"region:us"
] | null | 2023-11-04T18:11:21Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: title
dtype: string
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
splits:
- name: train
num_bytes: 435339
num_examples: 69
- name: validation
num_bytes: 193409
num_examples: 31
download_size: 51330
dataset_size: 628748
---
# Dataset Card for "org-acad-train-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ktrinh38/eva-pix2pix
|
ktrinh38
| 2023-11-04T20:33:49Z | 47 | 0 |
[
"region:us"
] | null | 2023-11-04T20:33:10Z |
---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 712910099.55
num_examples: 4291
download_size: 337563830
dataset_size: 712910099.55
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eva-pix2pix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
vic0428/imdb-card-pred-decimal
|
vic0428
| 2023-11-18T06:20:19Z | 47 | 0 |
[
"region:us"
] | null | 2023-11-10T01:06:42Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: prompt
dtype: string
- name: true_cardinality
dtype: int64
splits:
- name: train
num_bytes: 39101954.4
num_examples: 80000
- name: test
num_bytes: 9775488.6
num_examples: 20000
download_size: 8384711
dataset_size: 48877443.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "imdb-card-pred-decimal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Deojoandco/capstone_forgpt_without_gold
|
Deojoandco
| 2023-11-10T13:00:46Z | 47 | 0 |
[
"region:us"
] | null | 2023-11-10T13:00:32Z |
---
dataset_info:
features:
- name: dialog_id
dtype: int64
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: gold_tags
dtype: string
splits:
- name: train
num_bytes: 73584
num_examples: 76
- name: validation
num_bytes: 14568
num_examples: 12
- name: test
num_bytes: 8476
num_examples: 12
download_size: 38534
dataset_size: 96628
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "capstone_forgpt_without_gold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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quccili/invoice
|
quccili
| 2023-11-11T12:59:29Z | 47 | 0 |
[
"region:us"
] | null | 2023-11-11T12:59:26Z |
---
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: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 14986534.0
num_examples: 18
- name: validation
num_bytes: 14986534.0
num_examples: 18
- name: test
num_bytes: 14986534.0
num_examples: 18
download_size: 39577947
dataset_size: 44959602.0
---
# Dataset Card for "invoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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susnato/plant_disease_detection_processed
|
susnato
| 2023-11-25T12:05:23Z | 47 | 0 |
[
"task_categories:object-detection",
"license:cc-by-4.0",
"region:us"
] |
[
"object-detection"
] | 2023-11-24T10:43:53Z |
---
license: cc-by-4.0
task_categories:
- object-detection
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
struct:
- name: area
sequence: int64
- name: bbox
sequence:
sequence: int64
- name: category
sequence: int64
- name: pixel_values
sequence:
sequence:
sequence: float32
- name: pixel_mask
sequence:
sequence: int64
- name: labels
struct:
- name: area
sequence: float32
- name: boxes
sequence:
sequence: float32
- name: class_labels
sequence: int64
- name: image_id
sequence: int64
- name: iscrowd
sequence: int64
- name: orig_size
sequence: int64
- name: size
sequence: int64
splits:
- name: train
num_bytes: 27853534555.06
num_examples: 2110
- name: test
num_bytes: 2810816579.0
num_examples: 214
download_size: 5331925364
dataset_size: 30664351134.06
---
This Dataset is created from processing the files from this GitHub repository : [PlantDoc-Object-Detection-Dataset](https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset/tree/master)
Citation
BibTeX:
```
@inproceedings{10.1145/3371158.3371196,
author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
title = {PlantDoc: A Dataset for Visual Plant Disease Detection},
year = {2020},
isbn = {9781450377386},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3371158.3371196},
doi = {10.1145/3371158.3371196},
booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
pages = {249–253},
numpages = {5},
keywords = {Deep Learning, Object Detection, Image Classification},
location = {Hyderabad, India},
series = {CoDS COMAD 2020}
}
```
|
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sam1120/safety-utcustom-EVAL
|
sam1120
| 2023-12-01T08:41:52Z | 47 | 0 |
[
"region:us"
] | null | 2023-12-01T08:34:32Z |
---
dataset_info:
features:
- name: name
dtype: string
- name: pixel_values
dtype: image
- name: labels
dtype: image
splits:
- name: train
num_bytes: 139241854.0
num_examples: 50
download_size: 40270282
dataset_size: 139241854.0
---
# Dataset Card for "safety-utcustom-EVAL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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VanessaSchenkel/opus_books_en_pt
|
VanessaSchenkel
| 2022-08-06T22:46:10Z | 46 | 1 |
[
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|opus_books",
"language:en",
"language:pt",
"license:afl-3.0",
"region:us"
] |
[
"translation"
] | 2022-08-06T22:34:58Z |
---
annotations_creators:
- found
language:
- en
- pt
language_creators:
- found
license:
- afl-3.0
multilinguality:
- translation
pretty_name: VanessaSchenkel/opus_books_en_pt
size_categories:
- 1K<n<10K
source_datasets:
- extended|opus_books
tags: []
task_categories:
- translation
task_ids: []
---
How to use it:
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/opus_books_en_pt", field="data")
remote_dataset
```
Output:
```
DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 1404
})
})
```
Exemple:
```
remote_dataset["train"][5]
```
Output:
```
{'id': '5',
'translation': {'en': "There was nothing so very remarkable in that; nor did Alice think it so very much out of the way to hear the Rabbit say to itself, 'Oh dear!",
'pt': 'Não havia nada de tão extraordinário nisso; nem Alice achou assim tão fora do normal ouvir o Coelho dizer para si mesmo: —"Oh, céus!'}}
```
|
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] |
joelniklaus/MultiLegalPile_Wikipedia_Filtered
|
joelniklaus
| 2022-11-29T21:52:23Z | 46 | 0 |
[
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"region:us"
] |
[
"fill-mask"
] | 2022-11-17T19:28:00Z |
---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "MultiLegalPile_Wikipedia_Filtered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles."
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
---
# Dataset Card for MultiLegalPile_Wikipedia_Filtered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles
## 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:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
The Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.
It spans over 24 languages and four legal text types.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask.
### Languages
The following languages are supported:
bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
It is structured in the following format: {language}_{text_type}_{shard}.jsonl.xz
text_type is one of the following:
- caselaw
- contracts
- legislation
- other
- wikipedia
Use the dataset like this:
```python
from datasets import load_dataset
config = 'en_contracts' # {language}_{text_type}
dataset = load_dataset('joelito/Multi_Legal_Pile', config, split='train', streaming=True)
```
'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.
To load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '
all_legislation').
### Data Instances
The file format is jsonl.xz and there is a `train` and `validation` split available.
Since some configurations are very small or non-existent, they might not contain a train split or not be present at all.
The complete dataset consists of five large subsets:
- [Native Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile)
- [Eurlex Resources](https://huggingface.co/datasets/joelito/eurlex_resources)
- [MC4 Legal](https://huggingface.co/datasets/joelito/mc4_legal)
- [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law)
- [EU Wikipedias](https://huggingface.co/datasets/joelito/EU_Wikipedias)
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
This dataset has been created by combining the following datasets:
Native Multi Legal Pile, Eurlex Resources, MC4 Legal, Pile of Law, EU Wikipedias.
It has been filtered to remove short documents (less than 64 whitespace-separated tokens) and
documents with more than 30% punctuation or numbers (see prepare_legal_data.py for more details).
### 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
```
TODO add citation
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
|
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etrent17/irs-articles
|
etrent17
| 2023-01-21T19:42:50Z | 46 | 1 |
[
"license:mit",
"region:us"
] | null | 2023-01-21T19:42:17Z |
---
license: mit
---
|
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] |
inkoziev/incomplete_utterance_restoration
|
inkoziev
| 2023-01-27T15:56:24Z | 46 | 4 |
[
"task_categories:conversational",
"task_categories:question-answering",
"task_ids:dialogue-generation",
"task_ids:open-domain-qa",
"multilinguality:monolingual",
"source_datasets:original",
"language:ru",
"license:cc-by-nc-4.0",
"region:us"
] |
[
"conversational",
"question-answering"
] | 2023-01-27T15:19:28Z |
---
language: ru
license: cc-by-nc-4.0
language_creators: expert
multilinguality: monolingual
source_datasets: original
task_categories:
- conversational
- question-answering
task_ids:
- dialogue-generation
- open-domain-qa
---
# Датасет для задачи раскрытия неполных реплик в контексте диалога
Подробное описание задачи "Incomplete Utterance Restoration" можно найти в карточке генеративной модели [inkoziev/rugpt_interpreter](https://huggingface.co/inkoziev/rugpt_interpreter), которая обучена на аугментированном варианте этого датасета.
В датасете содержатся фрагменты диалогов длиной от 1 до 3 последовательных реплик. Для последней реплики дается ее полный вариант с раскрытыми анафорами, эллипсисами и т.д.
Например, следующий сэмпл:
```
{
"context": ["Добрый день, давай поговорим?"],
"short_phrase": "добрый день, давай",
"expanded_phrase": "Давай поговорим"
}
```
Соответствует фрагменту диалога:
```
- Добрый день, давай поговорим?
- добрый день, давай
```
Для последней реплики "_добрый день, давай_" приводится ее полный вариант "_Давай поговорим_".
Для фрагментов длиной 3 реплики в поле ```context``` будет 2 элемента:
```
{
"context": [
"Что-то зрителей полный инет, а изучителей не видать.",
"Ты кем себя считаешь: зрителем или изучателем?"
],
"short_phrase": "изучателем",
"expanded_phrase": "я считаю себя изучателем"
},
```
Этот сэмпл соответствует фрагменту диалога:
```
- Что-то зрителей полный инет, а изучителей не видать.
- Ты кем себя считаешь: зрителем или изучателем?
- изучателем
```
Для последней реплики дана полная форма "_я считаю себя изучателем_".
## Особенности датасета
1) Для многих сэмплов вежливая форма 2л. мн.ч. "Вы" в раскрытом варианте нормализована до "ты".
2) Есть нецензурная лексика, оскорбления и т.п.
## Ссылки, цитирование
Этот датасет я собирал сам для экспериментов с [диалоговыми системами](https://github.com/Koziev/chatbot). Если вы используете его в каком-то виде, буду благодарен за ссылки на эту страницу.
|
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] |
Kaludi/data-csgo-weapon-classification
|
Kaludi
| 2023-02-02T23:34:31Z | 46 | 0 |
[
"task_categories:image-classification",
"region:us"
] |
[
"image-classification"
] | 2023-02-02T22:42:56Z |
---
task_categories:
- image-classification
---
# Dataset for project: csgo-weapon-classification
## Dataset Description
This dataset has for project csgo-weapon-classification was collected with the help of a bulk google image downloader.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1768x718 RGB PIL image>",
"target": 0
},
{
"image": "<716x375 RGBA PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['AK-47', 'AWP', 'Famas', 'Galil-AR', 'Glock', 'M4A1', 'M4A4', 'P-90', 'SG-553', 'UMP', 'USP'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1100 |
| valid | 275 |
|
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jlbaker361/mnist_sorted_v0.0
|
jlbaker361
| 2023-02-04T17:09:27Z | 46 | 0 |
[
"region:us"
] | null | 2023-02-04T17:09:24Z |
---
dataset_info:
features:
- name: label
dtype: int64
- name: sequence
sequence: int64
- name: occurence
dtype: int64
- name: split
dtype: string
splits:
- name: train
num_bytes: 84223889
num_examples: 68614
download_size: 12695868
dataset_size: 84223889
---
# Dataset Card for "mnist_sorted_v0.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
metaeval/counterfactually-augmented-snli
|
metaeval
| 2023-03-08T08:55:24Z | 46 | 0 |
[
"task_categories:text-classification",
"language:en",
"license:unknown",
"region:us"
] |
[
"text-classification"
] | 2023-03-08T08:52:43Z |
---
license: unknown
task_categories:
- text-classification
language:
- en
---
```bib
@article{kaushik2020learning,
title={Learning the Difference that Makes a Difference with Counterfactually Augmented Data},
author={Kaushik, Divyansh and Hovy, Eduard and Lipton, Zachary C},
journal={International Conference on Learning Representations (ICLR)},
year={2020}
}
```
|
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] |
mohammadjavadpirhadi/fake-news-detection-dataset-english
|
mohammadjavadpirhadi
| 2023-03-26T16:10:25Z | 46 | 0 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2023-03-26T14:19:58Z |
---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: subject
dtype: string
- name: date
dtype: string
- name: label
dtype:
class_label:
names:
'0': real
'1': fake
splits:
- name: train
num_bytes: 93521249
num_examples: 35918
- name: test
num_bytes: 23506751
num_examples: 8980
download_size: 71290190
dataset_size: 117028000
license: mit
task_categories:
- text-classification
language:
- en
pretty_name: Fake News Detection English
size_categories:
- 10K<n<100K
---
# Dataset Card for "fake-news-detection-dataset-english"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ybelkada/common_voice_mr_11_0_copy
|
ybelkada
| 2023-04-04T06:15:41Z | 46 | 0 |
[
"region:us"
] | null | 2023-04-04T06:14:54Z |
---
dataset_info:
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
splits:
- name: train
num_bytes: 81761699.0
num_examples: 2245
- name: validation
num_bytes: 65082681.0
num_examples: 1682
- name: test
num_bytes: 69247449.0
num_examples: 1816
- name: other
num_bytes: 109682091.0
num_examples: 2819
- name: invalidated
num_bytes: 90463060.0
num_examples: 2237
download_size: 407562763
dataset_size: 416236980.0
---
# Dataset Card for "common_voice_mr_11_0_copy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
hackathon-somos-nlp-2023/informes_discriminacion_gitana
|
hackathon-somos-nlp-2023
| 2023-04-11T09:29:14Z | 46 | 7 |
[
"task_categories:text-classification",
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:es",
"license:apache-2.0",
"hate",
"region:us"
] |
[
"text-classification",
"text2text-generation"
] | 2023-04-04T14:19:40Z |
---
dataset_info:
features:
- name: sintetico
dtype: string
- name: text
dtype: string
- name: intervencion
dtype: string
- name: tipo_discriminacion
dtype: string
- name: resultado
dtype: string
splits:
- name: train
num_bytes: 1569183.3
num_examples: 1791
- name: test
num_bytes: 87614.92462311558
num_examples: 100
- name: valid
num_bytes: 86738.77537688443
num_examples: 99
download_size: 936705
dataset_size: 1743537.0000000002
task_categories:
- text-classification
- text2text-generation
language:
- es
tags:
- hate
size_categories:
- n<1K
license: apache-2.0
---
### Resumen del dataset
Se trata de un dataset en español, extraído del centro de documentación de la Fundación Secretariado Gitano, en el que se presentan distintas situaciones discriminatorias acontecidas por el pueblo gitano. Puesto que el objetivo del modelo es crear un sistema de generación de actuaciones que permita minimizar el impacto de una situación discriminatoria, se hizo un scrappeo y se extrajeron todos los PDFs que contuvieron casos de discriminación con el formato (HECHOS, INTERVENCIÓN, RESULTADO). Para extraer la información se hizo un scrappeo de la página, a continuación se limpió y se unificó todo el dataset con un script de preprocesamiento para que todo el dataset tuviera el mismo formato.
### Tareas admitidas y tablas de clasificación
- `task-generation`: Dado el hecho generar la intervención y la etiqueta de resultado, para dar métodos para hacer la intervección y que sea efectiva. ([PAG-BERT](https://huggingface.co/hackathon-somos-nlp-2023/PAG-BERT))
- `task-classication`: Se puede entrenar un modelo de clasificación, dejamos a los usarios, predecir el tipo de descriminación de dependiedo del hecho
### Idioma
Es un dataset con la variante español de España, el estilo empleado es formal y objetivo, limitándose a describir los hechos descritos por las personas afectadas.
## Estructura de los datos
### Instancias
A continuación se muestra una instancia de ejemplo del dataset:
```
{
'sintetico': '0',
'text': 'Una joven gitana comenzó a trabajar en una tienda de ropa, hace dos años, con contrato indefinido. Al mes de comenzar a trabajar, una compañera le preguntó, en presencia de su encargada, si era gitana, ella respondió que sí; desde entonces el trato de la encargada hacia la joven cambió, comenzó a tirar al suelo perchas, tierra, para luego acusarla de que no limpiaba el suelo, además de hacer continuamente comentarios generalizados refiriéndose a las mujeres gitanas, del tipo “¿Pero te dejan trabajar?” “¿Y estudiar?”, “tú tienes que saber cómo trabajar en la tienda porque como aprendéis en los mercadillos...” La víctima comentó que desde que la encargada se enteró de que era gitana le hizo la vida imposible, se sintió muy humillada. No aguantó más y presentó la baja voluntaria, aun siendo consciente de que perdía su derecho a la prestación por desempleo.',
'intervencion': 'Se entrevistó a la joven. Se comprobó a través del testimonio de la víctima que desde que su encargada se enteró de que es mujer gitana, al mes de comenzar a trabajar aproximadamente, comenzó a sufrir discriminación. Se informó a la víctima del Servicio, del trabajo que realizamos y de sus derechos.\xa0',
'tipo_discriminacion': 'Discriminación directa',
'resultado': 'Negativo.'
}
```
### Campos de los datos
- `sintetico`: indica si los datos son relacionados con la intervención y el resultado son originales, es decir, proceden de la fuente "Fundación Secretariado Gitano" (valor 0); o si, por el contrario, los hemos generado sintéticamente (valor 1).
- `text`: expone los hechos descritos por la persona afectada.
- `intervencion`: presenta las medidas que se tomaron desde la Fundación para evitar que los hechos descritos en "text" se volvieran a repetir.
- `tipo_discriminacion`: etiqueta que identifica el tipo de discriminación. Puede tomar los valores **Acoso discriminatorio**, **Discriminación directa**, **Discriminación indirecta**, **Discriminación interseccional**, **Discurso de odio**, **Orden de discriminar**,, **Sin especificar**.
- `resultado`: presenta la repercusión que tuvo la intervención adoptada. Sus posibles valores son **Positivo**, **Negativo** y **Neutro**.
### División de los datos
El dataset cuenta con un total de 1990 instancias, repartidas del siguiente modo:
| | train | validation | test |
|-------------------------|----------:|-------------:|----------:|
| Input Sentences | 90% | 5% | 5% |
| Average Sentence Length | 94.71 | 90.94 | 98.07 |
Cabe destacar que, teniendo en cuenta el resultado de las intervenciones (positivo, negativo o neutro), el dataset no está balanceado. En concreto, hay un total de 280 muestras positivas, 939 negativas y 771 neutras. En próximas actualizaciones del dataset trabajaremos para incrementar el tamaño del dataset de forma balanceada.
## Creación del dataset
### Justificación de la curación
El motivo por el que se creó este dataset es para conocer de una forma objetiva, si las medidas actuales que se están adoptando por parte de la Fundación han surtido efecto (en cuyo caso sería positivo), no ha surtido ningún efecto (negativo), o si por el contrario, las medidas propuestas no han incentivado al usuario a llevar a cabo ninguna acción.
Se ha optado por este dataset por el volumen de datos que contiene relativos a distintos escenarios, y por el formato que todos comparten de: HECHOS, INTERVENCIÓN Y RESULTADO.
### Fuente de los datos
Los datos utilizados para construir el modelo fueron extraídos de la página web de la Fundación Secretariado Gitano (<a href="https://informesdiscriminacion.gitanos.org">FSM</a>). El FSM tiene una base de datos que contiene actos de discriminación que han sido reportados a la organización. Estos actos de discriminación fueron seleccionados para entrenar y evaluar el modelo.
#### Recogida inicial de datos y normalización
Los datos fueron extraídos de la sección de <a href = "https://informesdiscriminacion.gitanos.org/buscar-casos" >Buscador de casos</a>, donde se lleva un registro de todo los casos de descriminación.
Los campos que ofrece la página web para estetipo de informes son:
* `Hecho` que hace referencia al acto de descriminación.
* `Intervención` qué medidas tomo la FSG para solucionar el problema.
* `Resultado`: Descripción del resultado.
* Año que ocurrió el caso.
* Año del informe.
* Ámbito: Dado el caso de que la discrimnación haya sido una empresa gubernamenta, en cual derecho fundamental se presentó.
* Provincia: Lugar donde ocurrió el acto.
* Tipo de discriminación.
En la extracción de datos solo tuvimos en cuenta los campos **hechos**, **intervención**, **resultados** y **tipo de discriminación**. El lenguaje usado en los informes es formal.
Originalmente, una elevado número de Hechos no contaban con una intervención y resultado (los campos estaban vacíos).
#### Limpieza de los datos
En la página web, el campo resultado contiene un breve explicación del los efectos obtenidos tras llevar a cabo la intervección. Usando la librería <a href="https://github.com/pysentimiento/pysentimiento">pysentimiento</a>, se clasificó el resultado entre negativo, neutro y positivo.
Posterior mente se revisó la etiqueta y se ajustó para lo que se consideraba neutral, negativo o positivo
El 17% de los actos de discriminación en el dataset no contaban con intervención ni resultado. Para completar estos campos se aplicó la técnica few-show learning usando el modelo Bloom. De modo que dado algunos ejemplos de **hechos**, **intervención** y **resultado**, seríamos capaces de generar **intervenciones** y **resultados** de forma automática. El output del modelo Bloom se revisó manualmente para corregir errores.
El 41% de los textos del campo **hechos** eran demasiado largos para ser utilizado en BLOOM aplicando la técnica de few-shot learning. Para resolver este problema, se decidió resumirlos, para esto se utilizó la función `segmenter.split_single` de la librería <a href="https://github.com/fnl/segtok" >segtok</a>, que divide el texto en oraciones y las separa por caracteres de nueva línea.
Se usaron dos modelos pre-etrenados para resumir cada sub-texto. El primero fue <a href="https://huggingface.co/mrm8488/bert2bert_shared-spanish-finetuned-summarization">mrm8488/bert2bert_shared-spanish-finetuned-summarization</a> y el segundo fue el <a href="https://huggingface.co/Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization">Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization</a>
En el repositorio https://github.com/Frorozcoloa/somos_nlp_hackaton se encuentran los scripts originales usados para el preprocesamiento. También puedes encontrar una copia de los mismos en este mismo repositorio.
### Anotación
Las anotaciones que se ralizaron fueron verificaciones a los datos de sintéticos generados con few-shot learning (intervenciones y resultados):
* Se rellenaron los valores nulos.
* Se hicieron resumenes de algunos textos (Hehos) aplicando modelos pre-entrenados.
* Se cambió el texto de resultado por etiquetas de POS, NEU, NEG.
#### Proceso de anotación
Para el proceso de etiquetado se utilizó Argilla para etiquetar la categoría de "Resultado", para ello se emplearon las siguientes etiquetas: "Positivo", "Negativo" y "Neutro". En el proceso de etiquetado lo que nos interesaba era etiquetar el resultado de las intervenciones para que el modelo aprendiera y pudiera generar texto para dar respuesta a la situación expuesta por el usuario, además de predecir con los datos etiquetados si la repercusión que pudiera tener la medida que propone el modelo sería "positiva"(surtiría efecto), "negativa"(no tendría ningún efecto) o "neutra"(si es posible que el usuario no llevara a cabo ninguna acción).
En concreto, tras descargar todos los datos disponibles en la web, los preprocesamos y unimos en un solo dataset que fue subido a Argilla. Una vez aquí, validamos cada una de las instancias del siguiente modo:
* Si la intervención y/o resultado están vacías, se anota como tal.
* Se comprueba que el resultado positivo, negativo o neutro es correcto. La mayoría de las incongruencias surgen entre los pares positivo/neutro y negativo/neutro.
Una vez validado el dataset con argilla, seleccionamos las muestras que fueron anotadas como "vacías" para proceder a completarlas. Para ello, hemos aplicado Few-Shot Learning usando el modelo [BLOOM](https://huggingface.co/bigscience/bloom).
Cabe destacar que algunos hechos del dataset eran demasiado largos y no podían ser procesados por BLOOM (generaba un error que indicaba que habíamos superado el número máximo de tokens), para solucionarlo, utilizamos los modelos <a href="https://huggingface.co/mrm8488/bert2bert_shared-spanish-finetuned-summarization">mrm8488/bert2bert_shared-spanish-finetuned-summarization</a> y <a href="https://huggingface.co/Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization">Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization</a> para resumir dichos hechos y minimizar así su tamaño.
### Información personal y sensible
En este caso no se ha necesitado utilizar ningún proceso de anonimización, ya que los datos procedentes de esta fuente no contienen ninguna información que vulnere los derechos de los afectados.
## Consideraciones sobre el uso de los datos
### Consideraciones sobre el uso de los datos
El impacto social de este dataset se dirige a ser una herramienta que sirva para implementar acciones que ayuden a combatir el racismo hacia la población gitana, además este dataset se podría utilizar para evaluar la repercusión de las distintas medidas adoptadas durante un período de tiempo, y aquellas medidas con una repercusión "negativa" o "neutra" investigarlas y mejorarlas con un trato más concienzudo hacia la población gitana.
### Debate sobre los prejuicios
Sé realizó un analisís exploratorio de los datos, para eso hemos realizado una nube de palabras para analizar los datos sintéticos y no sintéticos.
#### Datos no sintéticos
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Hechos_normales.png">
Aquí podemos ver que muchos de los hechos se generaron en noticias, en mujeres, temas de vivienda, con la policia y la familia.
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Intervenci%C3%B3n_normal.png">
Las intervenciones hablan de derechos, de cartas, de igualdad, asesorar a la persona y de presentar quejas.
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/etiqueta_normal.png">
Muchos de los resultados de las intervenciones fueron negativos o neutrales (Posiblemente sin respuesta) o de que no se logró lo propuesto (Negativo). Se puede observar el desbalance en los datos.
Por medio de la librería *pysentimiento* y usando el modelo `pysentimiento/pt_hate_speech`, se realizó una métrica para medir el discurso de odio en el `Hecho`.
Para eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.
Se encotró lo siguiente
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/hate_normal.png">
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/hate_2_normal.png">
La distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.
Para el caso, donde no se generó la intervección y resultado se presenta un crecimiento en el tercer cuartil, esto quiere decir que hay mensajes que muestra un discurso de odio. Por ejemplo el hateful es de 0.4, targeted de 0.02 y aggresive de 0.03. En conclusión, como está escrito el hecho y como fue entrenado el modelo de *pysentimiento*, en general los hechos no tienen un mensaje de odio.
#### Datos sintéticos.
Se realizó el mismo análisis para los datos sintéticos
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Hechos_sinteticos.png"/>
Cabe resltar que el hecho no fue generado.
Es claro que el dataset está más sesgado a contener las palabras gitano, gitana, comunidad gitana, etnia gitana, familia, discriminación.
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Intervenci%C3%B3n_sintetica.png"/>
Esta parte fue generada por el modelo *Bloom*. Puede comprobarse que con *few-shot* se logra captar más que todo la palabra `derecho`.
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/Etiquetas%20sinteticas.png">
Tambien hay un desbalance en las etiquetas generadas.
Por medio de la librería *pysentimiento* y usando el modelo `pysentimiento/pt_hate_speech` ,se realizó una métrica para medir el discurso de odio en el `Hecho`
Para eso análizaremos hateful, targeted y aggressive. La métrica va de 0 a 1, para cada una. Siendo la probabilidad de que esa caracteristica esté en el texto.
Se encotró lo siguiente
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/hate_sintetico.png">
<img src="https://huggingface.co/datasets/hackathon-somos-nlp-2023/informes_discriminacion_gitana/resolve/main/imagenes/hate_2_sintetico.png">
La distribución de los valores de hateful, targeted y aggressive presentan una cola alargada hacia la derecha, lo que indica que hay pocos casos en los que se detecta un mensaje de odio en los hechos.
Tanto la mediana como la media de los valores de hateful, targeted y aggressive están muy cerca de cero, lo que indica que la mayoría de los hechos no contienen mensajes de odio. Además, se observó que en el tercer cuartil, el 75% de los datos en la métrica de hateful es 0.3, para targeted es de 0.0089 y para aggressive es de 0.06, lo que refuerza la conclusión de que la mayoría de los datos no contienen un mensaje de odio en la descripción de los hechos.
## Información adicional
### Curadores del dataset
* <a href="https://www.linkedin.com/in/frorozcol/">Fredy Orozco</a>
* <a href="https://www.linkedin.com/in/mariajesusgs">María Jesús García</a>
* <a href="https://www.linkedin.com/in/ramonruedadelgado/">Ramón Rueda</a>
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AnanthZeke/tamil_sentences_master_raw
|
AnanthZeke
| 2023-04-07T05:25:02Z | 46 | 0 |
[
"region:us"
] | null | 2023-04-07T04:38:38Z |
---
dataset_info:
features:
- name: sent_token
dtype: string
splits:
- name: train
num_bytes: 20175735014
num_examples: 64948854
download_size: 6917303724
dataset_size: 20175735014
---
# Dataset Card for "tamil_sentences_master"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
skrishna/CSQA_preprocessed
|
skrishna
| 2023-05-10T18:01:33Z | 46 | 1 |
[
"region:us"
] | null | 2023-05-10T14:31:46Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_concept
dtype: string
- name: choices
sequence:
- name: label
dtype: string
- name: text
dtype: string
- name: answerKey
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 3875948
num_examples: 9741
- name: validation
num_bytes: 480334
num_examples: 1221
- name: test
num_bytes: 452620
num_examples: 1140
download_size: 2706083
dataset_size: 4808902
---
# Dataset Card for "CSQA_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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doushabao4766/resume_zh_ner
|
doushabao4766
| 2023-05-23T06:22:19Z | 46 | 0 |
[
"region:us"
] | null | 2023-05-23T06:18:34Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
splits:
- name: train
num_bytes: 1893971
num_examples: 3821
- name: test
num_bytes: 231104
num_examples: 477
- name: validation
num_bytes: 212262
num_examples: 463
download_size: 0
dataset_size: 2337337
---
# Dataset Card for "resume_zh_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jlvdoorn/atcosim
|
jlvdoorn
| 2023-06-29T14:36:14Z | 46 | 1 |
[
"language:en",
"air traffic management",
"automatic speech recognition",
"natural language processing",
"atcosim",
"atm",
"asr",
"nlp",
"doi:10.57967/hf/1378",
"region:us"
] | null | 2023-05-29T10:21:24Z |
---
language:
- en
tags:
- air traffic management
- automatic speech recognition
- natural language processing
- atcosim
- atm
- asr
- nlp
pretty_name: ATCOSIM
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 1929508254.0
num_examples: 7646
- name: validation
num_bytes: 480869258.0
num_examples: 1913
download_size: 2399337867
dataset_size: 2410377512.0
---
This is an ATM dataset for the use of automatic speech recognition. The original source of the data is from the [ATCOSIM](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html) project.
|
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clarin-knext/scifact-pl-qrels
|
clarin-knext
| 2023-06-07T08:25:00Z | 46 | 0 |
[
"task_categories:sentence-similarity",
"language:pl",
"license:cc-by-sa-4.0",
"arxiv:2305.19840",
"region:us"
] |
[
"sentence-similarity"
] | 2023-06-06T17:09:44Z |
---
license: cc-by-sa-4.0
task_categories:
- sentence-similarity
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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ejschwartz/oo-method-test
|
ejschwartz
| 2023-09-03T14:34:23Z | 46 | 1 |
[
"task_categories:text-classification",
"license:bsd",
"region:us"
] |
[
"text-classification"
] | 2023-06-07T19:33:55Z |
---
license: bsd
task_categories:
- text-classification
#task_ids:
#- binary-classification
dataset_info:
features:
- name: Binary
dtype: string
- name: Addr
dtype: string
- name: Name
dtype: string
- name: Type
dtype:
class_label:
names:
'0': func
'1': method
- name: Disassembly
dtype: string
config_name: ejschwartz--oo-method-test
splits:
- name: combined
num_bytes: 6054378861
num_examples: 3537794
download_size: 1351783459
dataset_size: 6054378861
train-eval-index:
- config: default # The dataset config name to use. Example for datasets without configs: default. Example for glue: sst2
task: text-classification # The task category name (same as task_category). Example: question-answering
task_id: binary_classification # The AutoTrain task id. Example: extractive_question_answering
splits:
#train_split: train # The split to use for training. Example: train
eval_split: train # The split to use for evaluation. Example: test
col_mapping: # The columns mapping needed to configure the task_id.
Disassembly: text
Type: target
metrics:
- type: accuracy # The metric id. Example: wer. Use metric id from https://hf.co/metrics
name: accuracy # Tne metric name to be displayed. Example: Test WER
---
# Dataset Card for OO Method Test Dataset
## Dataset Description
### Dataset Summary
This dataset describes compiled functions in various [small, simple C++ programs](https://github.com/sei-eschwartz/buildexes/tree/master/tests/src/oo).
These programs were automatically compiled using various versions of Microsoft's Visual C++ compiler and different compilation settings. The details can be found
in the [BuildExes](https://github.com/sei-eschwartz/buildexes) repository.
For each function, the dataset includes a disassembled (using ROSE's `bat-dis` tool) representation of the compiled code, its name, and whether the function is a OO method or not.
**This dataset is largely intended for @ejschwartz to experiment with learning techniques and tools. The programs are artificial and are likely not representative of real programs.**
### Supported Tasks and Leaderboards
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
|
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] |
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