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| likes
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turuta/Multi30k-uk
|
turuta
| 2023-05-04T19:11:45Z | 29 | 3 |
[
"task_categories:translation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:uk",
"language:en",
"license:unknown",
"common",
"multi30k",
"ukrainian",
"region:us"
] |
[
"translation",
"text-generation"
] | 2023-03-29T20:26:58Z |
---
license: unknown
task_categories:
- translation
- text-generation
language:
- uk
- en
pretty_name: ukr-multi30k
size_categories:
- 10K<n<100K
tags:
- common
- multi30k
- ukrainian
---
## Dataset Multi30k: English-Ukrainian variation
Multi30K dataset is designed to develop multilingual multimodal researches.
Initially this dataset extends the Flickr30K dataset by adding German translations. The descriptions were collected from a crowdsourcing platform, while the translations were collected from professionally contracted translators.
We present a variation of this dataset manually translated for Ukrainian language.
Paper:
```python
@inproceedings{saichyshyna-etal-2023-extension,
title = "Extension {M}ulti30{K}: Multimodal Dataset for Integrated Vision and Language Research in {U}krainian",
author = "Saichyshyna, Nataliia and
Maksymenko, Daniil and
Turuta, Oleksii and
Yerokhin, Andriy and
Babii, Andrii and
Turuta, Olena",
booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.unlp-1.7",
pages = "54--61",
abstract = "We share the results of the project within the well-known Multi30k dataset dedicated to improving machine translation of text from English into Ukrainian. The main task was to manually prepare the dataset and improve the translation of texts. The importance of collecting such datasets for low-resource languages for improving the quality of machine translation has been discussed. We also studied the features of translations of words and sentences with ambiguous meanings.The collection of multimodal datasets is essential for natural language processing tasks because it allows the development of more complex and comprehensive machine learning models that can understand and analyze different types of data. These models can learn from a variety of data types, including images, text, and audio, for more accurate and meaningful results.",
}
```
|
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] |
nanakonoda/xnli_parallel
|
nanakonoda
| 2023-04-18T13:23:10Z | 29 | 0 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|xnli",
"language:en",
"language:de",
"language:fr",
"mode classification",
"aligned",
"region:us"
] |
[
"text-classification"
] | 2023-04-03T00:49:12Z |
---
annotations_creators:
- expert-generated
language:
- en
- de
- fr
language_creators:
- found
license: []
multilinguality:
- multilingual
pretty_name: XNLI Parallel Corpus
size_categories:
- 100K<n<1M
source_datasets:
- extended|xnli
tags:
- mode classification
- aligned
task_categories:
- text-classification
task_ids: []
dataset_info:
- config_name: en
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': spoken
'1': written
splits:
- name: train
num_bytes: 92288
num_examples: 830
- name: test
num_bytes: 186853
num_examples: 1669
- config_name: de
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': spoken
'1': written
splits:
- name: train
num_bytes: 105681
num_examples: 830
- name: test
num_bytes: 214008
num_examples: 1669
- config_name: fr
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': spoken
'1': written
splits:
- name: train
num_bytes: 830
num_examples: 109164
- name: test
num_bytes: 221286
num_examples: 1669
download_size: 1864
dataset_size: 1840
---
# Dataset Card for XNLI Parallel Corpus
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
### Supported Tasks and Leaderboards
Binary mode classification (spoken vs written)
### Languages
- English
- German
- French
## Dataset Structure
### Data Instances
{
'text': "And he said , Mama , I 'm home .",
'label': 0
}
### Data Fields
- text: sentence
- label: binary label of text (0: spoken 1: written)
### Data Splits
- train: 830
- test: 1669
### Other Statistics
#### Vocabulary Size
- English
- train: 4363
- test: 7128
- German
- train: 5070
- test: 8601
- French
- train: 4881
- test: 7935
#### Average Sentence Length
- English
- train: 20.689156626506023
- test: 20.75254643499101
- German
- train: 20.367469879518072
- test: 20.639904134212102
- French
- train: 23.455421686746988
- test: 23.731575793888556
#### Label Split
- train:
- 0: 166
- 1: 664
- test:
- 0: 334
- 1: 1335
#### Out-of-vocabulary words in model
- English
- BERT (bert-base-uncased)
- train: 800
- test: 1638
- mBERT (bert-base-multilingual-uncased)
- train: 1347
- test: 2693
- German BERT (bert-base-german-dbmdz-uncased)
- train: 3228
- test: 5581
- flauBERT (flaubert-base-uncased)
- train: 4363
- test: 7128
- German
- BERT (bert-base-uncased)
- train: 4285
- test: 7387
- mBERT (bert-base-multilingual-uncased)
- train: 3126
- test: 5863
- German BERT (bert-base-german-dbmdz-uncased)
- train: 2033
- test: 3938
- flauBERT (flaubert-base-uncased)
- train: 5069
- test: 8600
- French
- BERT (bert-base-uncased)
- train: 3784
- test: 6289
- mBERT (bert-base-multilingual-uncased)
- train: 2847
- test: 5084
- German BERT (bert-base-german-dbmdz-uncased)
- train: 4212
- test: 6964
- flauBERT (flaubert-base-uncased)
- train: 4881
- test: 7935
## Dataset Creation
### Curation Rationale
N/A
### Source Data
https://github.com/facebookresearch/XNLI
Here is the citation for the original XNLI paper.
```
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin",
title = "XNLI: Evaluating Cross-lingual Sentence Representations",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
location = "Brussels, Belgium",
}
```
#### Initial Data Collection and Normalization
N/A
#### Who are the source language producers?
N/A
### Annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
N/A
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
N/A
### Licensing Information
N/A
### Citation Information
### Contributions
N/A
|
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liuyanchen1015/MULTI_VALUE_sst2_negative_concord
|
liuyanchen1015
| 2023-04-03T19:48:02Z | 29 | 0 |
[
"region:us"
] | null | 2023-04-03T19:47:58Z |
---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 6956
num_examples: 48
- name: test
num_bytes: 12384
num_examples: 84
- name: train
num_bytes: 165604
num_examples: 1366
download_size: 95983
dataset_size: 184944
---
# Dataset Card for "MULTI_VALUE_sst2_negative_concord"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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liuyanchen1015/MULTI_VALUE_sst2_inverted_indirect_question
|
liuyanchen1015
| 2023-04-03T19:48:45Z | 29 | 0 |
[
"region:us"
] | null | 2023-04-03T19:48:41Z |
---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 1554
num_examples: 10
- name: test
num_bytes: 4967
num_examples: 30
- name: train
num_bytes: 80411
num_examples: 597
download_size: 36917
dataset_size: 86932
---
# Dataset Card for "MULTI_VALUE_sst2_inverted_indirect_question"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
mstz/glass
|
mstz
| 2023-04-16T17:29:45Z | 29 | 0 |
[
"task_categories:tabular-classification",
"size_categories:n<1k",
"language:en",
"license:cc",
"glass",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-12T09:53:57Z |
---
language:
- en
tags:
- glass
- tabular_classification
- binary_classification
- UCI
pretty_name: Glass evaluation
size_categories:
- n<1k
task_categories:
- tabular-classification
configs:
- glass
- windows
- vehicles
- containers
- tableware
- headlamps
license: cc
---
# Glass
The [Glass dataset](https://archive-beta.ics.uci.edu/dataset/42/glass+identification) from the [UCI repository](https://archive-beta.ics.uci.edu).
Classify the type of glass.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|--------------------------|
| glass | Multiclass classification | Classify glass type. |
| windows | Binary classification | Is this windows glass? |
| vehicles | Binary classification | Is this vehicles glass? |
| containers | Binary classification | Is this containers glass?|
| tableware | Binary classification | Is this tableware glass? |
| headlamps | Binary classification | Is this headlamps glass? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/glass", "glass")["train"]
```
|
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darksam/socialmedia-abuse2
|
darksam
| 2023-04-13T16:24:52Z | 29 | 0 |
[
"region:us"
] | null | 2023-04-13T16:24:44Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
splits:
- name: train
num_bytes: 1074806
num_examples: 8530
download_size: 0
dataset_size: 1074806
---
# Dataset Card for "socialmedia-abuse2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
heegyu/open-korean-instructions
|
heegyu
| 2023-05-06T09:18:37Z | 29 | 12 |
[
"license:mit",
"region:us"
] | null | 2023-04-22T02:10:17Z |
---
license: mit
---
4가지 한국어 챗봇 학습용 데이터셋을 합쳐놓았습니다. 이중 ShareGPT 데이터는 멀티턴으로 되어있습니다.
데이터 생성 및 합치는 코드는 https://github.com/HeegyuKim/open-korean-instructions 여기를 참고하세요
| 이름 | # | 타입 |
|---|---|---|
| [KoAlpaca v1.0](https://huggingface.co/datasets/Bingsu/ko_alpaca_data) | 52K | 싱글턴 |
| [KoAlpaca v1.1](https://raw.githubusercontent.com/Beomi/KoAlpaca/main/KoAlpaca_v1.1.jsonl) | 21K | 싱글턴 |
| [ShareGPT DeepL 번역](https://huggingface.co/datasets/junelee/sharegpt_deepl_ko) | 620K(싱글턴), 84K(멀티턴) | 멀티턴, 싱글턴 |
| [OIG-small-chip2-ko](https://huggingface.co/datasets/heegyu/OIG-small-chip2-ko) | 210K | 싱글턴 |
| [Korquad-Chat](https://huggingface.co/datasets/heegyu/korquad-chat-v1) | 9.6K | 멀티턴, 지식기반 |
모든 데이터는 포멧이 통일되어 있습니다. `<sys>`, `<usr>`, `<bot>` 세가지 토큰과 줄넘김으로 화자를 구분합니다.
korquad-chat 데이터의 경우, 유저와 봇이 서로를 호칭할 때는 `<|bot|>`, `<|user|>`로 되어있습니다.
```
{"source": "koalpaca-v1.0", "text": "<usr> 홀수 중 하나를 밝히세요.\n<sys> 트위터, 인스타그램, 텔레그램\n<bot> 텔레그램입니다."}
{"source": "korquad-chat", "text": "
<sys>1984년 9월부터 덴노지 역 ~ 히네노 역간에 운전을 개시한 '홈 라이너 이즈미'가 전신이다. 특급 구로시오의 381계 전동차의 히네노 전차구 입선 회송 열차를 좌석 정리권을 필요로 하는 정원제 열차로서 운행해온 것으로 간사이 지방 최초의 홈 라이너였다. 1986년 11월 개정에 따라 운전 구간이 와카야마 역까지 연장되어 한와 라이너로 개칭되어 현재에 이르게 되었다. 이전에는 평일에 상행 3편, 하행 6편이 운전되었지만 일부가 신오사카 역이나 교토 역 방면 발착 특급 구로시오로 변경되어 2002년 3월 이후에는 상행 2편, 하행 4편으로 축소되었다. 2009년 3월의 시간표 개정부터는 휴일 시간대의 운전이 중단되었으며 같은 해 6월 1일부터는 전면 금연화되었다.\n
<usr> 안녕하세요, <|bot|>. \"한와_선\"에 대해 알고 계시나요?\n
<bot> 네, 전체적으로 알고 있습니다. 1984년에 운전을 개시한 \"홈 라이너 이즈미\"가 전신이며, 간사이 지방 최초의 홈 라이너였습니다. 1986년에는 와카야마 역까지 연장된 후 \"한와 라이너\"로 개칭되었습니다.\n
<usr> 그렇군요. 이전에는 평일에 몇 편이 운행되었나요?\n
<bot> 평일에 상행 3편, 하행 6편이 운전되었습니다. 하지만 2002년 이후로는 상행 2편, 하행 4편으로 축소되었습니다.\n
<usr> 그렇군요. 혹시 특급 구로시오와 연관이 있을까요?\n
<bot> 네, \"한와 라이너\"는 특급 구로시오의 381계 전동차의 히네노 전차구 입선 회송 열차를 좌석 정리권을 필요로 하는 정원제 열차로서 운행되었습니다."
}
```
|
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thehamkercat/telegram-spam-ham
|
thehamkercat
| 2023-04-30T15:11:17Z | 29 | 1 |
[
"license:wtfpl",
"region:us"
] | null | 2023-04-30T15:09:34Z |
---
license: wtfpl
---
|
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tomaarsen/conll2003
|
tomaarsen
| 2023-05-08T13:34:35Z | 29 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-reuters-corpus",
"language:en",
"license:other",
"region:us"
] |
[
"token-classification"
] | 2023-05-08T13:33:26Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-reuters-corpus
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: conll-2003
pretty_name: CoNLL-2003
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': '"'
'1': ''''''
'2': '#'
'3': $
'4': (
'5': )
'6': ','
'7': .
'8': ':'
'9': '``'
'10': CC
'11': CD
'12': DT
'13': EX
'14': FW
'15': IN
'16': JJ
'17': JJR
'18': JJS
'19': LS
'20': MD
'21': NN
'22': NNP
'23': NNPS
'24': NNS
'25': NN|SYM
'26': PDT
'27': POS
'28': PRP
'29': PRP$
'30': RB
'31': RBR
'32': RBS
'33': RP
'34': SYM
'35': TO
'36': UH
'37': VB
'38': VBD
'39': VBG
'40': VBN
'41': VBP
'42': VBZ
'43': WDT
'44': WP
'45': WP$
'46': WRB
- name: chunk_tags
sequence:
class_label:
names:
'0': O
'1': B-ADJP
'2': I-ADJP
'3': B-ADVP
'4': I-ADVP
'5': B-CONJP
'6': I-CONJP
'7': B-INTJ
'8': I-INTJ
'9': B-LST
'10': I-LST
'11': B-NP
'12': I-NP
'13': B-PP
'14': I-PP
'15': B-PRT
'16': I-PRT
'17': B-SBAR
'18': I-SBAR
'19': B-UCP
'20': I-UCP
'21': B-VP
'22': I-VP
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: conll2003
splits:
- name: train
num_bytes: 6931345
num_examples: 14041
- name: validation
num_bytes: 1739223
num_examples: 3250
- name: test
num_bytes: 1582054
num_examples: 3453
download_size: 982975
dataset_size: 10252622
train-eval-index:
- config: conll2003
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# Dataset Card for "conll2003"
## 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://www.aclweb.org/anthology/W03-0419/](https://www.aclweb.org/anthology/W03-0419/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.85 MB
- **Size of the generated dataset:** 10.26 MB
- **Total amount of disk used:** 15.11 MB
### Dataset Summary
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.
The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
tagging scheme, whereas the original dataset uses IOB1.
For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### conll2003
- **Size of downloaded dataset files:** 4.85 MB
- **Size of the generated dataset:** 10.26 MB
- **Total amount of disk used:** 15.11 MB
An example of 'train' looks as follows.
```
{
"id": "0",
"document_id": 1,
"sentence_id": 3,
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
}
```
The original data files have `-DOCSTART-` lines used to separate documents, but these lines are removed here.
Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation.
### Data Fields
The data fields are the same among all splits.
#### conll2003
- `id`: a `string` feature.
- `document_id`: an `int32` feature tracking which document the sample is from.
- `sentence_id`: an `int32` feature tracking which sentence in this document the sample is from.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'"': 0, "''": 1, '#': 2, '$': 3, '(': 4, ')': 5, ',': 6, '.': 7, ':': 8, '``': 9, 'CC': 10, 'CD': 11, 'DT': 12,
'EX': 13, 'FW': 14, 'IN': 15, 'JJ': 16, 'JJR': 17, 'JJS': 18, 'LS': 19, 'MD': 20, 'NN': 21, 'NNP': 22, 'NNPS': 23,
'NNS': 24, 'NN|SYM': 25, 'PDT': 26, 'POS': 27, 'PRP': 28, 'PRP$': 29, 'RB': 30, 'RBR': 31, 'RBS': 32, 'RP': 33,
'SYM': 34, 'TO': 35, 'UH': 36, 'VB': 37, 'VBD': 38, 'VBG': 39, 'VBN': 40, 'VBP': 41, 'VBZ': 42, 'WDT': 43,
'WP': 44, 'WP$': 45, 'WRB': 46}
```
- `chunk_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-ADJP': 1, 'I-ADJP': 2, 'B-ADVP': 3, 'I-ADVP': 4, 'B-CONJP': 5, 'I-CONJP': 6, 'B-INTJ': 7, 'I-INTJ': 8,
'B-LST': 9, 'I-LST': 10, 'B-NP': 11, 'I-NP': 12, 'B-PP': 13, 'I-PP': 14, 'B-PRT': 15, 'I-PRT': 16, 'B-SBAR': 17,
'I-SBAR': 18, 'B-UCP': 19, 'I-UCP': 20, 'B-VP': 21, 'I-VP': 22}
```
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|conll2003|14041| 3250|3453|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
From the [CoNLL2003 shared task](https://www.clips.uantwerpen.be/conll2003/ner/) page:
> The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done by people of the University of Antwerp. Because of copyright reasons we only make available the annotations. In order to build the complete data sets you will need access to the Reuters Corpus. It can be obtained for research purposes without any charge from NIST.
The copyrights are defined below, from the [Reuters Corpus page](https://trec.nist.gov/data/reuters/reuters.html):
> The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements:
>
> [Organizational agreement](https://trec.nist.gov/data/reuters/org_appl_reuters_v4.html)
>
> This agreement must be signed by the person responsible for the data at your organization, and sent to NIST.
>
> [Individual agreement](https://trec.nist.gov/data/reuters/ind_appl_reuters_v4.html)
>
> This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization.
### Citation Information
```
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F. and
De Meulder, Fien",
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
year = "2003",
url = "https://www.aclweb.org/anthology/W03-0419",
pages = "142--147",
}
```
### Contributions
Thanks to [@jplu](https://github.com/jplu), [@vblagoje](https://github.com/vblagoje), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
|
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cj-mills/hagrid-classification-512p-no-gesture-150k-zip
|
cj-mills
| 2023-05-22T23:00:45Z | 29 | 0 |
[
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-05-18T16:34:52Z |
---
license: cc-by-sa-4.0
language:
- en
size_categories:
- 100K<n<1M
---
This dataset contains 153,735 training images from [HaGRID](https://github.com/hukenovs/hagrid) (HAnd Gesture Recognition Image Dataset) modified for image classification instead of object detection. The original dataset is 716GB. I created this sample for a tutorial so readers can use the dataset in the free tiers of Google Colab and Kaggle Notebooks.
### Original Authors:
* [Alexander Kapitanov](https://www.linkedin.com/in/hukenovs)
* [Andrey Makhlyarchuk](https://www.linkedin.com/in/makhliarchuk)
* [Karina Kvanchiani](https://www.linkedin.com/in/kvanchiani)
### Original Dataset Links
* [GitHub](https://github.com/hukenovs/hagrid)
* [Kaggle Datasets Page](https://www.kaggle.com/datasets/kapitanov/hagrid)
|
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PKU-Alignment/BeaverTails-Evaluation
|
PKU-Alignment
| 2023-07-20T15:31:24Z | 29 | 0 |
[
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"moderation",
"rejection-sampling",
"llm",
"lm",
"human-feedback",
"arxiv:2307.04657",
"region:us"
] |
[
"text-classification"
] | 2023-06-14T16:14:14Z |
---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- safe
- safety
- ai-safety
- moderation
- rejection-sampling
- llm
- lm
- human-feedback
size_categories:
- n<1K
---
# Dataset Card for BeaverTails-Evaluation
BeaverTails is an AI safety-focused collection comprising a series of datasets.
This repository contains test prompts specifically designed for evaluating language model safety.
It is important to note that although each prompt can be connected to multiple categories, only one category is labeled for each prompt.
The 14 harm categories are defined as follows:
1. **Animal Abuse:** This involves any form of cruelty or harm inflicted on animals, including physical abuse, neglect, or any behavior that causes distress or harm to an animal. It also includes content that promotes such behavior.
1. **Child Abuse:** This encompasses any form of physical, emotional, or sexual abuse directed toward children. It can also include neglect, exploitation, or any behavior that harms a child or violates their rights. Content that promotes or glorifies such behavior also falls under this category.
1. **Controversial Topics, Politics:** This refers to discussions or statements related to controversial topics, political ideologies, or sensitive issues that could lead to disagreement or conflict. These topics can be divisive and could potentially cause harm if not handled respectfully and thoughtfully.
1. **Discrimination, Stereotype, Injustice:** Discrimination refers to treating people unfairly or unequally based on their personal characteristics or affiliations such as race, age, gender, religion, etc. Stereotyping involves holding simplified and generalized beliefs about particular groups, which often leads to prejudices and discrimination. Injustice pertains to unfair treatment or violation of the rights of individuals or groups.
1. **Drug Abuse, Weapons, Banned Substance:** This category includes the misuse or abuse of illegal drugs, prescription drugs, or any other banned substances. It also involves the unauthorized or illegal possession, use, or trade of weapons.
1. **Financial Crime, Property Crime, Theft:** This includes all forms of illegal activities that lead to financial loss such as fraud, embezzlement, money laundering, bribery, and other forms of corruption. Property crime and theft involve the act of taking someone's property without their consent.
1. **Hate Speech, Offensive Language:** This category encompasses any form of communication or content that expresses intense hostility or violence towards an individual or group based on aspects like race, religion, ethnicity, nationality, gender, sexual orientation, or any other defining characteristics. This also includes language that is considered vulgar, crude, or offensive.
1. **Misinformation Regarding ethics, laws, and safety:** This category involves the spreading of incorrect or misleading information related to ethical issues, laws, regulations, or any matter related to safety. Misinformation can cause harm by influencing people's beliefs or actions negatively.
1. **Non-Violent Unethical Behavior:** This refers to any behavior that is morally or ethically wrong, but does not involve violence. Examples can include dishonest behaviors like lying, cheating, deceiving, spreading rumors, betraying trust, or any other form of unethical conduct.
1. **Privacy Violation:** This refers to actions that invade an individual's privacy, such as unauthorized access, use, disclosure, or collection of personal data. It also includes stalking, doxing, or any other form of intrusion into someone's personal life without their consent.
1. **Self-Harm:** This includes any form of self-inflicted harm, whether physical or emotional, or any content that promotes, glorifies, or encourages self-harm. This can also include discussions of suicidal thoughts or plans.
1. **Sexually Explicit, Adult Content:** This category includes any content that depicts explicit sexual activities or adult themes that are considered inappropriate or offensive. This can include pornography, explicit sexual text or messages, and any other form of sexually explicit material.
1. **Terrorism, Organized Crime:** This pertains to any form of content or action related to terrorism or organized crime, including endorsing or promoting terrorist activities, participating in organized criminal activities, or spreading propaganda for such groups.
1. **Violence, Aiding and Abetting, Incitement:** This involves any form of physical harm, threat, or violent behavior towards individuals or groups. Aiding and abetting refers to the act of helping, supporting, or encouraging such violent behaviors or illegal activities. Incitement pertains to the act of provoking or stirring up harmful, violent, or illegal actions.
**Disclaimer**: The BeaverTails dataset and its family contain content that may be offensive or upsetting.
Topics covered in the dataset include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially distressing subject matter.
Please engage with the dataset responsibly and in accordance with your own personal risk tolerance.
The dataset is intended for research purposes, specifically for research aimed at creating safer and less harmful AI systems.
The views and opinions expressed in the dataset do not represent the views of the PKU-Alignment Team or any of its members.
It is important to emphasize that the dataset should not be used for training dialogue agents, as doing so may likely result in harmful model behavior.
The primary objective of this dataset is to facilitate research that could minimize or prevent the harm caused by AI systems.
## Usage
The code snippet below demonstrates how to load the evaluation dataset:
```python
from datasets import load_dataset
# Load the whole dataset
dataset = load_dataset('PKU-Alignment/BeaverTails-Evaluation')
# Load only the v1 dataset
round0_dataset = load_dataset('PKU-Alignment/BeaverTails', data_dir='v1')
```
## Paper
You can find more information in our paper.
- **Dataset Paper:** <https://arxiv.org/abs/2307.04657>
## Contact
The original authors host this dataset on GitHub here: https://github.com/PKU-Alignment/beavertails
## License
BeaverTails dataset and its family are released under the CC BY-NC 4.0 License.
|
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] |
KaiLv/UDR_MR
|
KaiLv
| 2023-06-21T12:42:19Z | 29 | 0 |
[
"region:us"
] | null | 2023-06-21T12:42:08Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: label
dtype: int64
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1164193
num_examples: 8662
- name: test
num_bytes: 266849
num_examples: 2000
- name: debug
num_bytes: 672162
num_examples: 5000
download_size: 1379605
dataset_size: 2103204
---
# Dataset Card for "UDR_MR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jxie/coco_captions
|
jxie
| 2023-06-25T07:37:53Z | 29 | 0 |
[
"region:us"
] | null | 2023-06-25T04:37:33Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: filename
dtype: string
- name: cocoid
dtype: int32
- name: caption
dtype: string
splits:
- name: train
num_bytes: 90684615607.036
num_examples: 566747
- name: validation
num_bytes: 4562095167.09
num_examples: 25010
- name: test
num_bytes: 4221845598.88
num_examples: 25010
download_size: 20920410197
dataset_size: 99468556373.006
---
# Dataset Card for "coco_captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
carbon225/vndb_img
|
carbon225
| 2023-07-04T14:46:14Z | 29 | 0 |
[
"task_categories:image-classification",
"size_categories:100K<n<1M",
"license:odbl",
"art",
"not-for-all-audiences",
"anime",
"visual-novel",
"nsfw",
"vndb",
"region:us"
] |
[
"image-classification"
] | 2023-07-04T14:12:10Z |
---
license: odbl
task_categories:
- image-classification
tags:
- art
- not-for-all-audiences
- anime
- visual-novel
- nsfw
- vndb
size_categories:
- 100K<n<1M
---
# Dataset Card for VNDB IMG
## Dataset Description
This is a 🤗 Datasets loader for the [vndb.org](https://vndb.org) image database dump.
It contains anime-style images flagged by users according to these categories:
* sexual content: safe/suggestive/explicit
* violence: tame/violent/brutal
## Loading Instructions
For licensing and "moral" reasons, the database dump has to be downloaded manually.
Please download the vndb.org database dump manually from <https://vndb.org/d14>.
Download the "Near-complete database" `vndb-db-latest.tar.zst` file.
Use `rsync` to download the 'Images' collection.
Create the following directory structure:
```
my/dataset/path
├── db
│ └── vndb-db-latest.tar.zst
└── vndb-img # this is the directory you downloaded with rsync
├── ch
├── cv
├── sf
├── st
└── ...
```
Inside `my/dataset/path/db` run
```
zstd -d vndb-db-latest.tar.zst
```
and
```
tar -xf vndb-db-latest.tar
```
The final directory structure should look like this:
```
my/dataset/path
├── db
│ ├── vndb-db-latest.tar
│ ├── vndb-db-latest.tar.zst
│ ├── db
│ └── ...
└── vndb-img
├── ch
├── cv
├── sf
├── st
└── ...
```
Finally, load the dataset
```python
datasets.load_dataset('carbon225/vndb_img', data_dir='my/dataset/path')
```
## Dataset Structure
The following fields are provided:
```python
{
'index': datasets.Value('int32'),
'id': datasets.Value('string'),
'width': datasets.Value('int32'),
'height': datasets.Value('int32'),
'c_votecount': datasets.Value('int32'),
'c_sexual_avg': datasets.Value('int32'),
'c_sexual_stddev': datasets.Value('int32'),
'c_violence_avg': datasets.Value('int32'),
'c_violence_stddev': datasets.Value('int32'),
'c_weight': datasets.Value('int32'),
'type': datasets.ClassLabel(names=['character', 'cover', 'screenshot_full', 'screenshot_thumb']),
'sexual_class': datasets.ClassLabel(names=['safe', 'suggestive', 'explicit']),
'violence_class': datasets.ClassLabel(names=['tame', 'violent', 'brutal']),
'file_name': datasets.Value('string'),
'full_path': datasets.Value('string'),
'image': datasets.Image(),
}
```
## Supported Tasks
With a few modifications the data can be used for:
* image classification of NSFW material
* image generation/super-resolution/...
* ...
## Considerations for Using the Data
The images are ***hardcore***, to say the least. I recommend not looking.
## Licensing Information
Using this dataset requires the user to download data manually from vndb.org.
All information on VNDB is made available under the Open Database License.
Any rights in individual contents of the database are licensed under the Database Contents License.
With the following exceptions:
* Anime data is obtained from the AniDB.net UDP API and is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0.
* Images, visual novel descriptions and character descriptions are gathered from various online sources and may be subject to separate license conditions.
|
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zan/lima-ja
|
zan
| 2023-07-08T13:39:45Z | 29 | 2 |
[
"language:ja",
"license:cc-by-nc-sa-4.0",
"arxiv:2305.11206",
"region:us"
] | null | 2023-07-08T07:35:34Z |
---
license: cc-by-nc-sa-4.0
language:
- ja
---
# Dataset Card for LIMA-JA
## Dataset Description
This is Japanese LIMA dataset, which is translated from the [LIMA dataset](https://huggingface.co/datasets/GAIR/lima) that Meta's [LIMA model](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023) was trained on.
In this process, after the Japanese translation is performed by ChatGPT, I am making some minor adjustments.
## Usage
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('zan/lima-ja')
>>> # dataset = load_dataset('zan/lima-ja', "v1")
```
```python
>>> dataset["train"][0]
{'conversations': ['脳細胞は移動することができるのでしょうか? 移動とは、遠距離の移動(可能ならば脳内のみで)を指します。',
'この質問は比較的広範であり、脳は単にニューロンだけでなく、グリア細胞(支持細胞)や前ミトーシスの神経幹細胞も含まれることを考慮する必要があります。さらに、重要な同僚科学者たちが指摘しているように、発生段階も非常に重要であり、発達中の胎児の脳は成人の脳とは非常に異なります。\n\nしかし、さまざまな出版物を精査した結果、この質問の答えは実際には非常に単純であることがわかりました。結論として、脳細胞は移動します。\n\n成人の脳では、グリア細胞が脳内を移動します(Klämbt、2009年)。グリア細胞は多くの機能に関与していますが、移動するグリア細胞の注目すべき例は、標的の軸索を見つけるために比較的長距離を移動し、それに巻きついて絶縁性のミエリン鞘を形成するオリゴデンドロサイトです(TsaiとMiller、2002年)。\n\n神経幹細胞は損傷に対応して長距離を移動します(Imitola et al.、2004年)し、特定の幹細胞の位置(例えば、海馬や脳室下帯)から他の領域に移動します(Clarke、2003年)。\n\nミトーシス後でありながら未分化のニューロンは、魚(Scott et al.、2012年)や哺乳類、および非人猿でも成人の脳内を移動することが示されています(Sawada et al.、2011年)。\n\n驚くことではありませんが、グリア細胞、幹細胞、およびニューロンは胎児の発生中も移動します。特に、末梢機能を果たすために運命づけられた分裂後のニューロンは、神経堤から標的の位置まで比較的長い距離を移動しなければなりません(Neuroscience、第2版、Neuronal Migration)。'],
'source': 'stackexchange'}
```
## Version Description
## v1
A version that has been modified by adding about 100 changes after being translated by ChatGPT.
## v2
more modified version
(Coming soon...)
## License
If the source data of LIMA has a stricter license than CC BY-NC-SA, the LIMA dataset follows the same. Otherwise, it follows the CC BY-NC-SA license.
## Citation Information
```
@InProceedings{huggingface:dataset,
title = {LIMA-JA: Japanese LIMA Dataset for Efficient Instruction-tuning},
author = {zan},
year = {2023}
}
```
|
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] |
jerryjalapeno/nart-100k-synthetic
|
jerryjalapeno
| 2023-07-16T04:04:05Z | 29 | 20 |
[
"license:openrail",
"region:us"
] | null | 2023-07-16T03:49:07Z |
---
license: openrail
---
Keep in mind that this dataset is entirely synthetic. It is not fully
representative of real therapy situations. If you are training an LLM
therapist keep in mind the limitations of LLMs and highlight those limitations
to users in a responsible manner.
|
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] |
ahazeemi/opus-it-en-de-new
|
ahazeemi
| 2023-08-01T06:29:33Z | 29 | 0 |
[
"region:us"
] | null | 2023-08-01T06:29:30Z |
---
dataset_info:
features:
- name: de
dtype: string
- name: en
dtype: string
splits:
- name: train
num_bytes: 29619721
num_examples: 222927
- name: dev
num_bytes: 278584
num_examples: 2000
- name: test
num_bytes: 258528
num_examples: 2000
download_size: 20800812
dataset_size: 30156833
---
# Dataset Card for "opus-it-en-de-new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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dim/wikihow_ru
|
dim
| 2023-08-15T12:11:59Z | 29 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-08-15T12:11:35Z |
---
license: mit
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
dtype: string
splits:
- name: train
num_bytes: 17666785.144215908
num_examples: 2058
download_size: 11421933
dataset_size: 17666785.144215908
---
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EgilKarlsen/PKDD_GPT2_Baseline
|
EgilKarlsen
| 2023-08-17T23:56:31Z | 29 | 0 |
[
"region:us"
] | null | 2023-08-17T23:50:57Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
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num_bytes: 115608907.5
num_examples: 37500
- name: test
num_bytes: 38536305.0
num_examples: 12500
download_size: 211867982
dataset_size: 154145212.5
---
# Dataset Card for "PKDD_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
KushT/reuters-21578-train-val-test
|
KushT
| 2023-08-25T12:24:45Z | 29 | 0 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-classification"
] | 2023-08-25T12:18:15Z |
---
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-classification
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: text
dtype: string
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 10816829
num_examples: 6988
- name: validation
num_bytes: 1178067
num_examples: 781
- name: test
num_bytes: 4513694
num_examples: 3019
download_size: 5088303
dataset_size: 16508590
language:
- en
---
Dataset from [Kaggle](https://www.kaggle.com/datasets/nltkdata/reuters/code)
The split is done on the training set using ```iterative_train_test_split``` from [scikit-multilearn](http://scikit.ml/index.html)
There are the following 90 labels.
'interest',
'groundnut-oil',
'potato',
'palmkernel',
'sun-meal',
'lei',
'cotton-oil',
'sunseed',
'sorghum',
'barley',
'dlr',
'groundnut',
'wpi',
'strategic-metal',
'livestock',
'l-cattle',
'lin-oil',
'gold',
'fuel',
'nzdlr',
'oat',
'soybean',
'hog',
'tin',
'lumber',
'bop',
'soy-oil',
'dfl',
'nkr',
'gas',
'carcass',
'silver',
'coffee',
'gnp',
'crude',
'rapeseed',
'alum',
'copper',
'housing',
'grain',
'cocoa',
'sun-oil',
'rice',
'jobs',
'rubber',
'jet',
'tea',
'retail',
'ship',
'corn',
'meal-feed',
'naphtha',
'sugar',
'rand',
'platinum',
'money-supply',
'yen',
'nickel',
'income',
'cpu',
'copra-cake',
'instal-debt',
'coconut-oil',
'cotton',
'rye',
'palm-oil',
'acq',
'wheat',
'propane',
'dmk',
'reserves',
'rape-oil',
'money-fx',
'heat',
'ipi',
'castor-oil',
'earn',
'iron-steel',
'palladium',
'coconut',
'veg-oil',
'nat-gas',
'pet-chem',
'lead',
'trade',
'cpi',
'oilseed',
'zinc',
'soy-meal',
'orange'
|
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] |
chengli-thu/yuebuqun
|
chengli-thu
| 2023-09-03T02:01:38Z | 29 | 0 |
[
"license:cc-by-4.0",
"arxiv:2308.09597",
"region:us"
] | null | 2023-09-03T01:59:42Z |
---
license: cc-by-4.0
---
支持ChatHaruhi2 的 岳不群 数据,可以使用如下方式调用
```python
from chatharuhi import ChatHaruhi
chatbot = ChatHaruhi( role_from_hf = 'chengli-thu/yuebuqun', \
llm = 'openai')
response = chatbot.chat(role='令狐冲', text = '师父,我来了')
print(response)
```
上传者: 李鲁鲁
更具体的信息,见 [ChatHaruhi](https://github.com/LC1332/Chat-Haruhi-Suzumiya)
欢迎加入我们的 [众筹角色创建项目](https://github.com/LC1332/Chat-Haruhi-Suzumiya/tree/main/characters/novel_collecting)
### Citation引用
Please cite the repo if you use the data or code in this repo.
```
@misc{li2023chatharuhi,
title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model},
author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},
year={2023},
eprint={2308.09597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
legacy107/bioasq10b-factoid
|
legacy107
| 2023-09-06T13:45:03Z | 29 | 2 |
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"medical",
"region:us"
] |
[
"question-answering"
] | 2023-09-06T13:39:03Z |
---
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: long_answer
dtype: string
- name: answer
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 3321906
num_examples: 1252
- name: test
num_bytes: 318200
num_examples: 166
download_size: 1758966
dataset_size: 3640106
task_categories:
- question-answering
language:
- en
tags:
- medical
pretty_name: BioASQ10b (factoid only)
size_categories:
- 1K<n<10K
---
# Dataset Card for "bioasq10b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jfrei/GPTNERMED
|
jfrei
| 2023-10-08T22:05:18Z | 29 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"bio",
"biomedical",
"medical",
"clinical",
"arxiv:2208.14493",
"region:us"
] |
[
"token-classification"
] | 2023-10-05T12:39:54Z |
---
annotations_creators:
- machine-generated
language:
- de
language_creators:
- machine-generated
license: []
multilinguality:
- monolingual
pretty_name: GPTNERMED
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- bio
- biomedical
- medical
- clinical
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# GPTNERMED Dataset for German medical NER entities
## Dataset Description
- **Repository:** https://github.com/frankkramer-lab/GPTNERMED
- **Paper:** https://doi.org/10.1016/j.jbi.2023.104478
- **ArXiv-Preprint:** https://arxiv.org/abs/2208.14493
## Dataset Summary
This dataset contains the synthetic German sentences with annotated entities (`Medikation`, `Dosis`, `Diagnose`) from the GPTNERMED project.
The sentences as well as the annotations are **not** manually validated by medical professionals and therefore this dataset is **no** gold standard dataset.
The dataset consists of 9,845 sentences (121,027 tokens by SpaCy Tokenizer, 245,107 tokens by the GPT tokenizer) with the following labels:
| Label | Count | #Tokens (SpaCy) |
| --- | --- | -- |
| Medikation | 9868 | 10138 |
| Dosis | 7547 | 15845 |
| Diagnose | 5996 | 7656 |
## Dataset Structure
The train/test/dev-split (80%, 10%, 10%) of the data loader is as follows:\
`<-- train: 0.8 --><-- test: 0.1 --><-- dev: 0.1 -->`\
The splits are selected arbitrarily as the dataloader requires a split configuration. All sample sentences are however homogeneous in origin and splits could also be performed otherwise.
Every sample is a sentence with its text (property `sentence`) and its corresponding NER labels (property `ner_labels` / List of labels).\
Every NER label entry has a char-wise start and stop index (property `start`, `stop`) and a label class (property `ner_class`).
### Citation Information
If you like our work, cite our paper and give us a star on GitHub.\
(See the links above)
|
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hippocrates/MedNLI_train
|
hippocrates
| 2023-10-18T19:47:44Z | 29 | 0 |
[
"region:us"
] | null | 2023-10-12T15:46:06Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8375998
num_examples: 11232
- name: valid
num_bytes: 1054726
num_examples: 1395
- name: test
num_bytes: 1050034
num_examples: 1422
download_size: 3057999
dataset_size: 10480758
---
# Dataset Card for "MedNLI_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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rahular/varta
|
rahular
| 2023-10-23T20:17:02Z | 29 | 0 |
[
"task_categories:summarization",
"task_categories:feature-extraction",
"size_categories:1B<n<10B",
"language:as",
"language:bh",
"language:bn",
"language:en",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:ne",
"language:or",
"language:pa",
"language:ta",
"language:te",
"language:ur",
"license:cc",
"arxiv:2305.05858",
"region:us"
] |
[
"summarization",
"feature-extraction"
] | 2023-10-20T03:55:08Z |
---
license: cc
task_categories:
- summarization
- feature-extraction
language:
- as
- bh
- bn
- en
- gu
- hi
- kn
- ml
- mr
- ne
- or
- pa
- ta
- te
- ur
pretty_name: varta
size_categories:
- 1B<n<10B
---
## Dataset Description
- **Repository:** https://github.com/rahular/varta
- **Paper:** https://arxiv.org/abs/2305.05858
### Dataset Summary
Varta is a diverse, challenging, large-scale, multilingual, and high-quality headline-generation dataset containing 41.8 million news articles in 14 Indic languages and English.
The data is crawled from DailyHunt, a popular news aggregator in India that pulls high-quality articles from multiple trusted and reputed news publishers.
### Languages
Assamese, Bhojpuri, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Tamil, Telugu, and Urdu.
## Dataset Structure
### Data Fields
- id: unique identifier for the artilce on DailyHunt. This id will be used to recreate the dataset.
- langCode: ISO 639-1 language code
- source_url: the url that points to the article on the website of the original publisher
- dh_url: the url that points to the article on DailyHunt
- id: unique identifier for the artilce on DailyHunt.
- url: the url that points to the article on DailyHunt
- headline: headline of the article
- publication_date: date of publication
- text: main body of the article
- tags: main topics related to the article
- reactions: user likes, dislikes, etc.
- source_media: original publisher name
- source_url: the url that points to the article on the website of the original publisher
- word_count: number of words in the article
- langCode: language of the article
### Data Splits
From every language, we randomly sample 10,000 articles each for validation and testing. We also ensure that at least 80% of a language’s data is available for training.
Therefore, if a language has less than 100,000 articles, we restrict its validation and test splits to 10% of its size.
We also create a `small` training set by limiting the number of articles from each language to 100K.
This `small` training set with a size of 1.3M is used in all our fine-tuning experiments.
You can find the `small` training set [here](https://huggingface.co/datasets/rahular/varta/blob/main/varta/train/train_100k.json)
## Data Recreation
To recreate the dataset, follow this [README file](https://github.com/rahular/varta/tree/main/crawler#README.md).
## Misc
- Original source: https://m.dailyhunt.in/
- License: CC-BY 4.0
## Citation Information
```
@misc{aralikatte2023varta,
title={V\=arta: A Large-Scale Headline-Generation Dataset for Indic Languages},
author={Rahul Aralikatte and Ziling Cheng and Sumanth Doddapaneni and Jackie Chi Kit Cheung},
year={2023},
eprint={2305.05858},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
davidfant/natural-questions-chunk-6
|
davidfant
| 2023-10-22T23:10:03Z | 29 | 0 |
[
"region:us"
] | null | 2023-10-22T23:06:32Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4655306372
num_examples: 10000
download_size: 1805442960
dataset_size: 4655306372
---
# Dataset Card for "natural-questions-chunk-6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
davidfant/natural-questions-chunk-7
|
davidfant
| 2023-10-22T23:13:42Z | 29 | 0 |
[
"region:us"
] | null | 2023-10-22T23:10:03Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4648515125
num_examples: 10000
download_size: 1806671077
dataset_size: 4648515125
---
# Dataset Card for "natural-questions-chunk-7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
kardosdrur/folketinget-discussions
|
kardosdrur
| 2023-12-04T09:25:10Z | 29 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-24T08:48:35Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: comment
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 7032676.035654362
num_examples: 3814
- name: test
num_bytes: 1759090.9643456375
num_examples: 954
download_size: 4898174
dataset_size: 8791767.0
---
# Discussions in Folketinget
The dataset is based on data from Folketinget in the Danish Gigaword corpus.
Comment-response pairs are purely extracted on the basis of heuristics, and have not been manually evaluated.
The dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project.
The dataset is currently not recommended for production use.
|
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liezeleinstein/rwkvtest2instja5
|
liezeleinstein
| 2023-10-24T13:11:19Z | 29 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-24T13:10:51Z |
---
license: mit
---
|
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] |
ms3c/swahili-common-voices-africas-talking
|
ms3c
| 2023-11-05T13:08:57Z | 29 | 0 |
[
"license:lgpl-3.0",
"region:us"
] | null | 2023-11-05T08:39:30Z |
---
license: lgpl-3.0
---
|
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Lollitor/LogDataset
|
Lollitor
| 2023-11-06T18:07:27Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-06T18:07:23Z |
---
dataset_info:
features:
- name: -logKd/Ki
dtype: float64
- name: inputs
dtype: string
splits:
- name: train
num_bytes: 22099679
num_examples: 18926
download_size: 8110526
dataset_size: 22099679
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "LogDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
washimneupane/moLoraData
|
washimneupane
| 2023-11-07T10:27:48Z | 29 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-07T07:41:51Z |
---
license: mit
dataset_info:
features:
- name: content
dtype: string
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
- name: alphanum_fraction
dtype: float64
- name: licenses
sequence: string
- name: repository_name
dtype: string
- name: path
dtype: string
- name: size
dtype: int64
- name: lang
dtype: string
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 2737311371
num_examples: 300000
download_size: 900770371
dataset_size: 2737311371
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
chirunder/transliteration_classification_dataset
|
chirunder
| 2023-11-10T13:27:26Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-10T13:27:17Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: classification
dtype: string
splits:
- name: train
num_bytes: 201288.8
num_examples: 2400
- name: test
num_bytes: 50322.2
num_examples: 600
download_size: 181466
dataset_size: 251611.0
---
# Dataset Card for "transliteration_classification_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
zxvix/law_court_opinion_100
|
zxvix
| 2023-11-13T12:31:26Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-13T12:31:22Z |
---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: created_timestamp
dtype: string
- name: downloaded_timestamp
dtype: string
- name: url
dtype: string
splits:
- name: test
num_bytes: 98368
num_examples: 100
download_size: 62128
dataset_size: 98368
---
# Dataset Card for "law_court_opinion_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
YuhoLiang/CVPR2023
|
YuhoLiang
| 2023-11-15T06:36:44Z | 29 | 1 |
[
"size_categories:1K<n<10K",
"region:us"
] | null | 2023-11-15T06:11:32Z |
---
size_categories:
- 1K<n<10K
---
|
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luizlzg/drbyte_dataset
|
luizlzg
| 2023-12-04T04:44:29Z | 29 | 0 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:pt",
"medical",
"biology",
"region:us"
] |
[
"text-generation"
] | 2023-11-15T18:17:43Z |
---
task_categories:
- text-generation
language:
- pt
tags:
- medical
- biology
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: drbyte_ptbr_treino*
- split: test
path: drbyte_ptbr_teste*
- split: validation
path: drbyte_ptbr_valid*
---
# Descrição geral
O seguinte dataset, responsável pelo treinamento do modelo apelidado de Dr Byte, é um dataset, com informações da área da saúde, para o fine tuning com instruções de modelos de linguagem. <br> <br>
Além disso, os datasets contam com dúvidas gerais de pacientes, dúvidas sobre medicamentos, questões de múltipla escolha de vestibulares de medicina, dados de biomedicina, dentre outros. <br>
# Construção do Dataset
O dataset foi construído a partir da tradução, do inglês para o português, dos seguintes datasets (nem todos foram utilizados em sua totalidade):
## Treino:
- MedQA (USMLE), que contém conhecimentos médicos gerais do exame de licenciamento médico dos EUA. (10082 dados) <br>
- MedMCQA, que contém conhecimentos médicos gerais de vestibulares de medicina indianos. (9736 dados) <br>
- LiveQA, que contém dúvidas de conhecimentos médicos gerais, provenientes de pessoas que não são da área. (622 dados) <br>
- MedicationQA, que contém dúvidas frequentes sobre medicamentos, provenientes de pessoas que não são da área. (687 dados) <br> <br> <br>
- Total de dados de treino: 21127 dados.
## Teste
- MedMCQA (SPLIT DE VALIDAÇÃO), que contém conhecimentos médicos gerais de vestibulares de medicina indianos. (4183 dados) <br>
- MedQA (USMLE) (SPLIT DE TESTE), que contém conhecimentos médicos gerais do exame de licenciamento médico dos EUA. (1273 dados) <br>
- PubMedQA (SPLIT DE TESTE), que contém dados da literatura científica de biomedicina. (500 dados) <br>
- MMLU (SPLIT DE TESTE), que cobre questões de múltipla escolha acerca de conhecimento médico, cobrindo os seguintes temas: anatomia, conhecimento clínico, questões de faculdade de medicina, genética médica, questões medicina profissional e biologia universitária.(1089 dados) <br> <br> <br>
- Total de dados de teste: 7045 dados.
# Características dos dados:
Os datasets possuem as seguintes features, para cada split do dataset: <br>
## Treino:
- 'instruction': é a instrução em si, geralmente é uma pergunta ou uma questão de múltipla escolha junto com suas respectivas alternativas. <br>
- 'output': é a resposta esperada para a instrução, pode ser uma resposta direta, uma alternativa e/ou uma explicação a respeito da alternativa. <br>
## Teste:
- 'dataset': o dataset de onde o dado é proveniente. <br>
- 'instruction': a instrução em si. <br>
- 'input': pode estar presente ou não (geralmente só no dataset PubMedQA) e é um contexto adicional para a resolução da instrução. <br>
- 'output': é a resposta esperada para a instrução. <br>
- 'alternativa_a': o texto da alternativa A, quando está presente. <br>
- 'alternativa_b': o texto da alternativa B, quando está presente. <br>
- 'alternativa_c': o texto da alternativa C, quando está presente. <br>
- 'alternativa_d': o texto da alternativa D, quando está presente. <br>
|
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laitrongduc/zlbn_en
|
laitrongduc
| 2023-11-17T11:58:01Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-17T11:57:45Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 50188488.852
num_examples: 1362
download_size: 48956901
dataset_size: 50188488.852
---
# Dataset Card for "zlbn_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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result-kand2-sdxl-wuerst-karlo/e13426e5
|
result-kand2-sdxl-wuerst-karlo
| 2023-11-23T14:28:13Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-23T14:28:12Z |
---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1307
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e13426e5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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s2e-lab/RegexEval
|
s2e-lab
| 2023-11-29T01:10:58Z | 29 | 4 |
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:mit",
"regex",
"redos",
"security",
"region:us"
] |
[
"text-generation"
] | 2023-11-24T06:41:50Z |
---
license: mit
task_categories:
- text-generation
language:
- en
tags:
- regex
- redos
- security
pretty_name: RegexEval
size_categories:
- n<1K
---
# Dataset Card for RegexEval
<!-- Provide a quick summary of the dataset. -->
Re(gEx|DoS)Eval is a framework that includes a dataset of 762 regex descriptions (prompts) from real users, refined prompts with examples, and a robust set of tests.
## Dataset Details
### Dataset Description
- **Curated by:** Mohammed Latif Siddiq, Jiahao Zhang, Lindsay Roney, and Joanna C. S. Santos
- **Language(s):** English
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/s2e-lab/RegexEval
- **Paper:** https://s2e-lab.github.io/preprints/icse_nier24-preprint.pdf
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
- dataset.jsonl: dataset file in jsonl format. Every line contains a JSON object with the following fields:
- `id`: unique identifier of the sample.
- `raw_prompt`: Raw/original prompt from the real users with the description of the RegEx.
- `refined_prompt`: Refined prompt with the description of the RegEx.
- `matches`: Matches examples for the RegEx.
- `non-matches`: Non-matches examples for the RegEx.
## Dataset Creation
### Source Data
We mined (on Aug. 16th, 2023) all the regexes from [RegExLib](https://regexlib.com/), a regular expression library. We use this library because it contains user-contributed regular expressions.
We obtained from RegExLib a list of 4,128 regular expressions along with their id, description, and list of expected matches and non-match strings.
#### Data Collection and Processing
For each sample previously collected, we perform a manual validation to (1) filter out incorrect regexes, (2) create more sample test cases (i.e., matching and non-matching string examples), and (3) create refined problem descriptions (i.e., prompts).
We excluded any regex that matched one or more of the following conditions: (i) it was missing any metadata, i.e., description and/or list of expected matches and non- matches; (ii) its description is not written in English; (iii) its description included vulgar words; (iv) its description does not provide sufficient information to understand the purpose of the regular expression; (v) it aimed to detect just one word; (vi) it is incorrect (i.e., the regex matches a string that is not supposed to match, or it does not match a string that is expected to match). After this step, we have 1,001 regex samples.
Each collected regex sample had (on average) only 4 string examples (2 that are expected matches and 2 that are expected non-matches). Thus, we manually crafted additional test cases to ensure that each sample has at least 13 matching1 and 12 non-matching string examples. After creating these additional test strings, we evaluated the regex with the new set of test cases again and excluded the failed regex samples. Hence, we have 762 samples in our final dataset.
Upon further inspection of the descriptions in the extracted sample, we observed that some of them lacked a more detailed explanation (e.g., ID#84: “SQL date format tester.”) or had extra information unrelated to the regex (e.g., ID#4: “... Other than that, this is just a really really long description of a regular expression that I’m using to test how my front page will look in the case where very long expression descriptions are used”). Thus, we created a refined prompt with a clear description of the regex that includes three match and two non-match string examples.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@inproceedings{siddiq2024regexeval,
author={Siddiq, Mohammed Latif and Zhang, Jiahao and Roney, Lindsay and Santos, Joanna C. S.},
booktitle={Proceedings of the 46th International Conference on Software Engineering, NIER Track (ICSE-NIER '24)},
title={Re(gEx|DoS)Eval: Evaluating Generated Regular Expressions and their Proneness to DoS Attacks},
year={2024}
}
```
## Dataset Card Authors and Contact
[Mohammed Latif Siddiq](http://lsiddiqsunny.github.io)
|
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] |
maritaca-ai/enem
|
maritaca-ai
| 2023-11-29T20:11:45Z | 29 | 0 |
[
"task_categories:visual-question-answering",
"task_categories:multiple-choice",
"size_categories:n<1K",
"language:pt",
"license:apache-2.0",
"arxiv:2311.14169",
"arxiv:2303.17003",
"region:us"
] |
[
"visual-question-answering",
"multiple-choice"
] | 2023-11-24T12:55:21Z |
---
license: apache-2.0
configs:
- config_name: '2022'
data_files: 2022.jsonl
- config_name: '2023'
data_files: 2023.jsonl
default: true
dataset_info:
features:
- name: id
dtype: string
- name: exam
dtype: string
- name: IU
dtype: bool
- name: ledor
dtype: bool
- name: question
dtype: string
- name: alternatives
sequence: string
- name: figures
sequence: string
- name: description
sequence: string
- name: label
dtype: string
task_categories:
- visual-question-answering
- multiple-choice
language:
- pt
pretty_name: ENEM
size_categories:
- n<1K
---
The enem 2022 and enem 2023 datasets encompass all multiple-choice questions from the last two editions of the [Exame Nacional do Ensino Médio (ENEM)](https://www.gov.br/inep/pt-br/areas-de-atuacao/avaliacao-e-exames-educacionais/enem), the main standardized entrance examination adopted by Brazilian universities. The datasets have been created to allow the evaluation of both textual-only and textual-visual language models. To evaluate textual-only models, we incorporated into the datasets the textual descriptions of the images that appear in the questions' statements from the orange ENEM exam booklet, a particular booklet that offers accessibility to people with visual impairments.
A repository containing the essential code for utilizing this dataset is accessible [here](https://github.com/piresramon/gpt-4-enem).
If you use this dataset in your research, please acknowledge the papers below by citing them:
```bibtex
@misc{pires2023evaluating,
title={Evaluating GPT-4's Vision Capabilities on Brazilian University Admission Exams},
author={Ramon Pires and Thales Sales Almeida and Hugo Abonizio and Rodrigo Nogueira},
year={2023},
eprint={2311.14169},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{nunes2023evaluating,
title={Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams},
author={Desnes Nunes and Ricardo Primi and Ramon Pires and Roberto Lotufo and Rodrigo Nogueira},
year={2023},
eprint={2303.17003},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
janakipanneerselvam/TMSL21_Sunlit_Tomatoes
|
janakipanneerselvam
| 2023-11-25T04:09:11Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-25T00:04:36Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 324313448.7
num_examples: 3342
- name: validation
num_bytes: 116465781.048
num_examples: 1098
download_size: 352836635
dataset_size: 440779229.74799997
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
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] |
dutta18/omcs_50k_with_FAISS
|
dutta18
| 2023-11-25T09:55:32Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-25T07:27:50Z |
---
dataset_info:
features:
- name: count
dtype: int64
- name: fact
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 157033742
num_examples: 50000
download_size: 186812200
dataset_size: 157033742
---
# Dataset Card for "omcs_50k_with_FAISS"
When people communicate, they rely on a large body of shared common sense knowledge in order to understand each other. Many barriers we face today in artificial intelligence and user interface design are due to the fact that computers do not share this knowledge. To improve computers' understanding of the world that people live in and talk about, we need to provide them with usable knowledge about the basic relationships between things that nearly every person knows.
The embedding for implementing FAISS indexing is given in the dataset as the 'embedding' column.
To implement FAISS indexing:
dataset.add_faiss_index(column='embeddings')
The above code needed to be executed. Then FAISS indexing can be verified.
|
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seonglae/wikipedia-256
|
seonglae
| 2023-11-26T15:41:22Z | 29 | 0 |
[
"task_categories:question-answering",
"language:en",
"wikipedia",
"region:us"
] |
[
"question-answering"
] | 2023-11-25T08:10:11Z |
---
language:
- en
task_categories:
- question-answering
dataset_info:
config_name: gpt-4
features:
- name: id
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 24166736905
num_examples: 21462234
download_size: 12274801108
dataset_size: 24166736905
configs:
- config_name: gpt-4
data_files:
- split: train
path: gpt-4/train-*
tags:
- wikipedia
---
This is Wikidedia passages dataset for ODQA retriever.
Each passages have 256~ tokens splitteed by gpt-4 tokenizer using tiktoken.
Token count
```ts
{'~128': 1415068, '128~256': 1290011,
'256~512': 18756476, '512~1024': 667,
'1024~2048': 12, '2048~4096': 0, '4096~8192': 0,
'8192~16384': 0, '16384~32768': 0, '32768~65536': 0,
'65536~128000': 0, '128000~': 0}
```
Text count
```ts
{'~512': 1556876,'512~1024': 6074975, '1024~2048': 13830329,
'2048~4096': 49, '4096~8192': 2, '8192~16384': 3, '16384~32768': 0,
'32768~65536': 0, '65536~': 0}
```
Token percent
```ts
{'~128': '6.59%', '128~256': '6.01%', '256~512': '87.39%',
'512~1024': '0.00%', '1024~2048': '0.00%', '2048~4096': '0.00%',
'4096~8192': '0.00%', '8192~16384': '0.00%', '16384~32768': '0.00%',
'32768~65536': '0.00%', '65536~128000': '0.00%', '128000~': '0.00%'}
```
Text percent
```ts
{'~512': '7.25%', '512~1024': '28.31%', '1024~2048': '64.44%',
'2048~4096': '0.00%', '4096~8192': '0.00%', '8192~16384': '0.00%',
'16384~32768': '0.00%', '32768~65536': '0.00%', '65536~': '0.00%'}
```
|
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] |
remyxai/ffmperative_dpo
|
remyxai
| 2023-11-30T02:06:50Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-27T20:00:21Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 1230727
num_examples: 1475
download_size: 352567
dataset_size: 1230727
---
# Dataset Card for "ffmperative_dpo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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kiritan/stdu_jiutian_dataset
|
kiritan
| 2023-11-30T04:11:46Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-28T15:22:29Z |
---
dataset_info:
- config_name: default
features:
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
struct:
- name: bbox
sequence:
sequence: int64
- name: categories
sequence: int64
splits:
- name: train
num_bytes: 744428594.1986415
num_examples: 1192
- name: test
num_bytes: 60867141.73735849
num_examples: 133
download_size: 800795177
dataset_size: 805295735.936
- config_name: main
features:
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
struct:
- name: bbox
sequence:
sequence: int64
- name: categories
sequence: int64
splits:
- name: train
num_bytes: 744428594.1986415
num_examples: 1192
- name: test
num_bytes: 60867141.73735849
num_examples: 133
download_size: 800795177
dataset_size: 805295735.936
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- config_name: main
data_files:
- split: train
path: main/train-*
- split: test
path: main/test-*
---
|
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] |
truong9499/zac-2023-advertising-banner-generation
|
truong9499
| 2023-11-30T07:11:34Z | 29 | 0 |
[
"region:us"
] | null | 2023-11-30T01:54:27Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 85687454.064
num_examples: 1347
download_size: 83076731
dataset_size: 85687454.064
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
lewtun/mnist-preds
|
lewtun
| 2021-07-16T09:00:01Z | 28 | 0 |
[
"benchmark:test",
"region:us"
] | null | 2022-03-02T23:29:22Z |
---
benchmark: test
---
# Dataset Card for RAFT Submission
|
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] |
valurank/hate-multi
|
valurank
| 2022-10-25T09:57:06Z | 28 | 0 |
[
"task_categories:text-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:derived",
"language:en",
"license:other",
"region:us"
] |
[
"text-classification"
] | 2022-03-02T23:29:22Z |
---
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- derived
task_categories:
- text-classification
---
# Dataset Card for hate-multi
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
## Dataset Description
### Dataset Summary
This dataset contains a collection of text labeled as hate speech (class 1) or not (class 0).
## Dataset Creation
The dataset was creating by aggregating multiple publicly available datasets.
### Source Data
The following datasets were used:
* https://huggingface.co/datasets/hate_speech18 - Filtered to remove examples labeled as 'idk/skip', 'relation'
* https://huggingface.co/datasets/hate_speech_offensive - Tweet text cleaned by lower casing, removing mentions and urls. Dropped instanced labeled as 'offensive language'
* https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech - Tweet text cleaned by lower casing, removing mentions and urls. Dropped instanced with hatespeech == 1
|
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hackathon-pln-es/Axolotl-Spanish-Nahuatl
|
hackathon-pln-es
| 2023-04-13T08:51:58Z | 28 | 8 |
[
"task_categories:text2text-generation",
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:es",
"license:mpl-2.0",
"conditional-text-generation",
"region:us"
] |
[
"text2text-generation",
"translation"
] | 2022-03-30T15:52:03Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- es
license:
- mpl-2.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
- translation
task_ids: []
pretty_name: "Axolotl Spanish-Nahuatl parallel corpus , is a digital corpus that compiles\
\ several sources with parallel content in these two languages. \n\nA parallel corpus\
\ is a type of corpus that contains texts in a source language with their correspondent\
\ translation in one or more target languages. Gutierrez-Vasques, X., Sierra, G.,\
\ and Pompa, I. H. (2016). Axolotl: a web accessible parallel corpus for spanish-nahuatl.\
\ In Proceedings of the Ninth International Conference on Language Resources and\
\ Evaluation (LREC 2016), Portoro, Slovenia. European Language Resources Association\
\ (ELRA). Grupo de Ingenieria Linguistica (GIL, UNAM). Corpus paralelo español-nahuatl.\
\ http://www.corpus.unam.mx/axolotl."
language_bcp47:
- es-MX
tags:
- conditional-text-generation
---
# Axolotl-Spanish-Nahuatl : Parallel corpus for Spanish-Nahuatl machine translation
## Table of Contents
- [Dataset Card for [Axolotl-Spanish-Nahuatl]](#dataset-card-for-Axolotl-Spanish-Nahuatl)
## Dataset Description
- **Source 1:** http://www.corpus.unam.mx/axolotl
- **Source 2:** http://link.springer.com/article/10.1007/s10579-014-9287-y
- **Repository:1** https://github.com/ElotlMX/py-elotl
- **Repository:2** https://github.com/christos-c/bible-corpus/blob/master/bibles/Nahuatl-NT.xml
- **Paper:** https://aclanthology.org/N15-2021.pdf
## Dataset Collection
In order to get a good translator, we collected and cleaned two of the most complete Nahuatl-Spanish parallel corpora available. Those are Axolotl collected by an expert team at UNAM and Bible UEDIN Nahuatl Spanish crawled by Christos Christodoulopoulos and Mark Steedman from Bible Gateway site.
After this, we ended with 12,207 samples from Axolotl due to misalignments and duplicated texts in Spanish in both original and nahuatl columns and 7,821 samples from Bible UEDIN for a total of 20028 utterances.
## Team members
- Emilio Morales [(milmor)](https://huggingface.co/milmor)
- Rodrigo Martínez Arzate [(rockdrigoma)](https://huggingface.co/rockdrigoma)
- Luis Armando Mercado [(luisarmando)](https://huggingface.co/luisarmando)
- Jacobo del Valle [(jjdv)](https://huggingface.co/jjdv)
## Applications
- MODEL: Spanish Nahuatl Translation Task with a T5 model in ([t5-small-spanish-nahuatl](https://huggingface.co/hackathon-pln-es/t5-small-spanish-nahuatl))
- DEMO: Spanish Nahuatl Translation in ([Spanish-nahuatl](https://huggingface.co/spaces/hackathon-pln-es/Spanish-Nahuatl-Translation))
|
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] |
taln-ls2n/kptimes
|
taln-ls2n
| 2022-09-23T07:38:28Z | 28 | 1 |
[
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"text-mining",
"text-generation"
] | 2022-05-06T09:34:40Z |
---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- 100K<n<1M
pretty_name: KPTimes
---
# KPTimes Benchmark Dataset for Keyphrase Generation
## About
KPTimes is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 290K news articles in English collected from the [New York Times](https://www.nytimes.com/) and the [Japan
Times](https://www.japantimes.co.jp/).
Keyphrases were annotated by editors in a semi-automated manner (that is, editors revise a set of keyphrases proposed by an algorithm and provide additional keyphrases).
Details about the dataset can be found in the original paper [(Gallina et al., 2019)][gallina-2019].
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].
Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`. <u>P</u>resent keyphrases are ordered according to their first occurrence position in the text.
## Content and statistics
The dataset contains the following test split:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
| Train | 259,923 | 921 | 5.03 | 45.61 | 15.57 | 29.63 | 9.19 |
| Validation | 10,000 | 921 | 5.02 | 45.22 | 15.78 | 29.60 | 9.41 |
| Test | 20,000 | 648 | 5.03 | 60.64 | 8.90 | 18.95 | 11.51 |
The following data fields are available :
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
- **date**: publishing date (YYYY/MM/DD)
- **categories**: categories of the article (1 or 2 categories)
## References
- (Gallina et al., 2019) Ygor Gallina, Florian Boudin, and Beatrice Daille. 2019.
[KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents][gallina-2019].
In Proceedings of the 12th International Conference on Natural Language Generation, pages 130–135, Tokyo, Japan. Association for Computational Linguistics.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021].
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
[gallina-2019]: https://aclanthology.org/W19-8617/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/
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strombergnlp/twitter_pos
|
strombergnlp
| 2022-10-25T21:43:15Z | 28 | 2 |
[
"task_categories:token-classification",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"token-classification"
] | 2022-05-06T19:09:49Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- part-of-speech
paperswithcode_id: ritter-pos
pretty_name: Twitter Part-of-speech
---
# Dataset Card for "twitter-pos"
## 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://gate.ac.uk/wiki/twitter-postagger.html](https://gate.ac.uk/wiki/twitter-postagger.html)
- **Repository:** [https://github.com/GateNLP/gateplugin-Twitter](https://github.com/GateNLP/gateplugin-Twitter)
- **Paper:** [https://aclanthology.org/R13-1026/](https://aclanthology.org/R13-1026/)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 51.96 MiB
- **Size of the generated dataset:** 251.22 KiB
- **Total amount of disk used:** 52.05 MB
### Dataset Summary
Part-of-speech information is basic NLP task. However, Twitter text
is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style.
This dataset contains two datasets for English PoS tagging for tweets:
* Ritter, with train/dev/test
* Foster, with dev/test
Splits defined in the Derczynski paper, but the data is from Ritter and Foster.
* Ritter: [https://aclanthology.org/D11-1141.pdf](https://aclanthology.org/D11-1141.pdf),
* Foster: [https://www.aaai.org/ocs/index.php/ws/aaaiw11/paper/download/3912/4191](https://www.aaai.org/ocs/index.php/ws/aaaiw11/paper/download/3912/4191)
### Supported Tasks and Leaderboards
* [Part of speech tagging on Ritter](https://paperswithcode.com/sota/part-of-speech-tagging-on-ritter)
### Languages
English, non-region-specific. `bcp47:en`
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{'id': '0', 'tokens': ['Antick', 'Musings', 'post', ':', 'Book-A-Day', '2010', '#', '243', '(', '10/4', ')', '--', 'Gray', 'Horses', 'by', 'Hope', 'Larson', 'http://bit.ly/as8fvc'], 'pos_tags': [23, 23, 22, 9, 23, 12, 22, 12, 5, 12, 6, 9, 23, 23, 16, 23, 23, 51]}
```
### Data Fields
The data fields are the same among all splits.
#### twitter-pos
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
```
### Data Splits
| name |tokens|sentences|
|---------|----:|---------:|
|ritter train|10652|551|
|ritter dev |2242|118|
|ritter test |2291|118|
|foster dev |2998|270|
|foster test |2841|250|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
### Citation Information
```
@inproceedings{ritter2011named,
title={Named entity recognition in tweets: an experimental study},
author={Ritter, Alan and Clark, Sam and Etzioni, Oren and others},
booktitle={Proceedings of the 2011 conference on empirical methods in natural language processing},
pages={1524--1534},
year={2011}
}
@inproceedings{foster2011hardtoparse,
title={\# hardtoparse: POS Tagging and Parsing the Twitterverse},
author={Foster, Jennifer and Cetinoglu, Ozlem and Wagner, Joachim and Le Roux, Joseph and Hogan, Stephen and Nivre, Joakim and Hogan, Deirdre and Van Genabith, Josef},
booktitle={Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence},
year={2011}
}
@inproceedings{derczynski2013twitter,
title={Twitter part-of-speech tagging for all: Overcoming sparse and noisy data},
author={Derczynski, Leon and Ritter, Alan and Clark, Sam and Bontcheva, Kalina},
booktitle={Proceedings of the international conference recent advances in natural language processing ranlp 2013},
pages={198--206},
year={2013}
}
```
### Contributions
Author uploaded ([@leondz](https://github.com/leondz))
|
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] |
mbazaNLP/kinyarwanda-tts-dataset
|
mbazaNLP
| 2023-06-27T08:09:28Z | 28 | 1 |
[
"language_creators:Digital Umuganda",
"size_categories:3K<n<4K",
"size_categories:~6hours",
"language:rw",
"license:cc-by-4.0",
"region:us"
] | null | 2022-05-27T08:20:36Z |
---
language:
- rw
language_creators:
- "Digital Umuganda"
license:
- cc-by-4.0
size_categories:
- 3K<n<4K
- ~6hours
---
# Kinyarwanda TTS dataset
The dataset consists of 3992 clips of Kinyarwanda TTS corpus recorded in a studio using a voice actress, it was collected in the mbaza project
## Data structure
```
Audio: 3992 Single voice studio recordings by a voice actress
Text: CSV with audio name and corresponding written text
```
## Language
The corresponding dataset is in the Kinyarwanda Language
## Dataset Creation
- Text collected had to include Kinyarwanda syllabes, which is made by a combination of a consonant or a group of consonats (e.g. Nyw) and a vowel.
- Text were reviewed by a linguist to ensure the text fit kinyarwanda standards
- The voice were recorded in a studio albeit in a semi-professional settings (i.e. some of the audio contains reverbs)
|
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] |
pinecone/core-2020-05-10-deduplication
|
pinecone
| 2022-10-28T03:01:02Z | 28 | 1 |
[
"task_categories:other",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:unknown",
"language:en",
"license:mit",
"deduplication",
"region:us"
] |
[
"other"
] | 2022-06-18T15:43:43Z |
---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- unknown
task_categories:
- other
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
pretty_name: CORE Deduplication of Scholarly Documents
tags:
- deduplication
---
# Dataset Card for CORE Deduplication
## Dataset Description
- **Homepage:** [https://core.ac.uk/about/research-outputs](https://core.ac.uk/about/research-outputs)
- **Repository:** [https://core.ac.uk/datasets/core_2020-05-10_deduplication.zip](https://core.ac.uk/datasets/core_2020-05-10_deduplication.zip)
- **Paper:** [Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings](http://oro.open.ac.uk/id/eprint/70519)
- **Point of Contact:** [CORE Team](https://core.ac.uk/about#contact)
- **Size of downloaded dataset files:** 204 MB
### Dataset Summary
CORE 2020 Deduplication dataset (https://core.ac.uk/documentation/dataset) contains 100K scholarly documents labeled as duplicates/non-duplicates.
### Languages
The dataset language is English (BCP-47 `en`)
### Citation Information
```
@inproceedings{dedup2020,
title={Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings},
author={Gyawali, Bikash and Anastasiou, Lucas and Knoth, Petr},
booktitle = {Proceedings of 12th Language Resources and Evaluation Conference},
month = may,
year = 2020,
publisher = {France European Language Resources Association},
pages = {894-903}
}
```
|
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autoevaluate/autoeval-staging-eval-project-8ef742e5-7734972
|
autoevaluate
| 2022-06-27T07:48:40Z | 28 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-06-27T07:46:57Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: mrp/bert-finetuned-squad
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mrp/bert-finetuned-squad
* Dataset: squad
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@thomwolf](https://huggingface.co/thomwolf) for evaluating this model.
|
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readerbench/ro-fb-offense
|
readerbench
| 2023-02-20T13:26:28Z | 28 | 2 |
[
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ro",
"license:apache-2.0",
"hate-speech-detection",
"region:us"
] |
[
"text-classification"
] | 2022-07-10T17:53:14Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ro
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
pretty_name: RO-FB-Offense
extra_gated_prompt: 'Warning: this repository contains harmful content (abusive language,
hate speech).'
tags:
- hate-speech-detection
---
# Dataset Card for "RO-FB-Offense"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/readerbench/ro-fb-offense](https://github.com/readerbench/ro-fb-offense)
- **Repository:** [https://github.com/readerbench/ro-fb-offense](https://github.com/readerbench/ro-fb-offense)
- **Paper:** FB-RO-Offense – A Romanian Dataset and Baseline Models for detecting Offensive Language in Facebook Comments
- **Point of Contact:** [Andrei Paraschiv](https://github.com/AndyTheFactory)
### Dataset Summary
FB-RO-Offense corpus, an offensive speech dataset containing 4,455 user-generated comments from Facebook live broadcasts available in Romanian
The annotation follows the hierarchical tagset proposed in the Germeval 2018 Dataset.
The following Classes are available:
* OTHER: Non-Offensive Language
* OFFENSIVE:
- PROFANITY
- INSULT
- ABUSE
### Languages
Romanian
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
'sender': '$USER1208',
'no_reacts': 1,
'text': 'PLACEHOLDER TEXT',
'label': OTHER,
}
```
### Data Fields
- `sender`: a `string` feature.
- 'no_reacts': a `integer`
- `text`: a `string`.
- `label`: categorical `OTHER`, `PROFANITY`, `INSULT`, `ABUSE`
### Data Splits
| name |train|test|
|---------|----:|---:|
|ro|x|x|
## Dataset Creation
### Curation Rationale
Collecting data for abusive language classification for Romanian Language.
### Source Data
Facebook comments
#### Initial Data Collection and Normalization
#### Who are the source language producers?
Social media users
### Annotations
#### Annotation process
#### Who are the annotators?
Native speakers
### Personal and Sensitive Information
The data was public at the time of collection. No PII removal has been performed.
## Considerations for Using the Data
### Social Impact of Dataset
The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
This data is available and distributed under Apache-2.0 license
### Citation Information
```
@inproceedings{busuioc2022fb-ro-offense,
title={FB-RO-Offense – A Romanian Dataset and Baseline Models for detecting Offensive Language in Facebook Comments},
author={ Busuioc, Gabriel-Razvan and Paraschiv, Andrei and Dascalu, Mihai},
booktitle={International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) 2022},
year={2022}
}
```
### Contributions
|
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] |
PaddlePaddle/duconv
|
PaddlePaddle
| 2022-07-29T11:44:00Z | 28 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2022-07-29T10:53:42Z |
---
license: apache-2.0
---
|
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] |
roskoN/stereoset_german
|
roskoN
| 2022-08-30T14:53:55Z | 28 | 0 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-08-30T13:49:14Z |
---
license: cc-by-sa-4.0
---
|
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skytnt/fbanimehq
|
skytnt
| 2022-10-23T14:02:23Z | 28 | 9 |
[
"task_categories:unconditional-image-generation",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:cc0-1.0",
"region:us"
] |
[
"unconditional-image-generation"
] | 2022-09-18T01:01:43Z |
---
annotations_creators: []
language: []
language_creators: []
license:
- cc0-1.0
multilinguality: []
pretty_name: Full Body Anime HQ
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- unconditional-image-generation
task_ids: []
---
## Dataset Description
FBAnimeHQ is a dataset with high-quality full-body anime girl images in a resolution of 1024 × 512.
### Dataset Summary
The dataset contains 112,806 images.
All images are on white background
### Collection Method
#### v1.0
Collect from danbooru website.
Use yolov5 to detect and clip image.
Use anime-segmentation to remove background.
Use deepdanbooru to filter image.
Finally clean the dataset manually.
#### v2.0
Base on v1.0, use Novelai image-to-image to enhance and expand the dataset.
### Contributions
Thanks to [@SkyTNT](https://github.com/SkyTNT) for adding this dataset.
|
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stochastic/random_streetview_images_pano_v0.0.2
|
stochastic
| 2022-10-14T02:05:40Z | 28 | 4 |
[
"task_categories:image-classification",
"task_ids:multi-label-image-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"license:mit",
"region:us"
] |
[
"image-classification"
] | 2022-10-05T19:39:59Z |
---
annotations_creators:
- expert-generated
language: []
language_creators:
- expert-generated
license:
- mit
multilinguality:
- multilingual
pretty_name: panoramic, street view images of random places on Earth
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
---
# Dataset Card for panoramic street view images (v.0.0.2)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The random streetview images dataset are labeled, panoramic images scraped from randomstreetview.com. Each image shows a location
accessible by Google Streetview that has been roughly combined to provide ~360 degree view of a single location. The dataset was designed with the intent to geolocate an image purely based on its visual content.
### Supported Tasks and Leaderboards
None as of now!
### Languages
labels: Addresses are written in a combination of English and the official language of country they belong to.
images: There are some images with signage that can contain a language. Albeit, they are less common.
## Dataset Structure
For now, images exist exclusively in the `train` split and it is at the user's discretion to split the dataset how they please.
### Data Instances
For each instance, there is:
- timestamped file name: '{YYYYMMDD}_{address}.jpg`
- the image
- the country iso-alpha2 code
- the latitude
- the longitude
- the address
Fore more examples see the [dataset viewer](https://huggingface.co/datasets/stochastic/random_streetview_images_pano_v0.0.2/viewer/stochastic--random_streetview_images_pano_v0.0.2/train)
```
{
filename: '20221001_Jarše Slovenia_46.1069942_14.9378597.jpg'
country_iso_alpha2 : 'SI'
latitude: '46.028223'
longitude: '14.345106'
address: 'Jarše Slovenia_46.1069942_14.9378597'
}
```
### Data Fields
- country_iso_alpha2: a unique 2 character code for each country in the world following the ISO 3166 standard
- latitude: the angular distance of a place north or south of the earth's equator
- longitude: the angular distance of a place east or west of the standard meridian of the Earth
- address: the physical address written from most micro -> macro order (Street, Neighborhood, City, State, Country)
### Data Splits
'train': all images are currently contained in the 'train' split
## Dataset Creation
### Curation Rationale
Google StreetView Images [requires money per image scraped](https://developers.google.com/maps/documentation/streetview/usage-and-billing).
This dataset provides about 10,000 of those images for free.
### Source Data
#### Who are the source image producers?
Google Street View provide the raw image, this dataset combined various cuts of the images into a panoramic.
[More Information Needed]
### Annotations
#### Annotation process
The address, latitude, and longitude are all scraped from the API response. While portions of the data has been manually validated, the assurance in accuracy is based on the correctness of the API response.
### Personal and Sensitive Information
While Google Street View does blur out images and license plates to the best of their ability, it is not guaranteed as can been seen in some photos. Please review [Google's documentation](https://www.google.com/streetview/policy/) for more information
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was designed after inspiration from playing the popular online game, [geoguessr.com[(geoguessr.com). We ask that users of this dataset consider if their geolocation based application will harm or jeopardize any fair institution or system.
### Discussion of Biases
Out of the ~195 countries that exists, this dataset only contains images from about 55 countries. Each country has an average of 175 photos, with some countries having slightly less.
The 55 countries are:
["ZA","KR","AR","BW","GR","SK","HK","NL","PE","AU","KH","LT","NZ","RO","MY","SG","AE","FR","ES","IT","IE","LV","IL","JP","CH","AD","CA","RU","NO","SE","PL","TW","CO","BD","HU","CL","IS","BG","GB","US","SI","BT","FI","BE","EE","SZ","UA","CZ","BR","DK","ID","MX","DE","HR","PT","TH"]
In terms of continental representation:
| continent | Number of Countries Represented |
|:-----------------------| -------------------------------:|
| Europe | 30 |
| Asia | 13 |
| South America | 5 |
| Africa | 3 |
| North America | 3 |
| Oceania | 2 |
This is not a fair representation of the world and its various climates, neighborhoods, and overall place. But it's a start!
### Other Known Limitations
As per [Google's policy](https://www.google.com/streetview/policy/): __"Street View imagery shows only what our cameras were able to see on the day that they passed by the location. Afterwards, it takes months to process them. This means that content you see could be anywhere from a few months to a few years old."__
### Licensing Information
MIT License
### Citation Information
### Contributions
Thanks to [@WinsonTruong](https://github.com/WinsonTruong) and [@
David Hrachovy](https://github.com/dayweek) for helping developing this dataset.
This dataset was developed for a Geolocator project with the aforementioned developers, [@samhita-alla](https://github.com/samhita-alla) and [@yiyixuxu](https://github.com/yiyixuxu).
Thanks to [FSDL](https://fullstackdeeplearning.com) for a wonderful class and online cohort.
|
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] |
severo/glue
|
severo
| 2022-10-28T16:35:04Z | 28 | 0 |
[
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"qa-nli",
"coreference-nli",
"paraphrase-identification",
"region:us"
] |
[
"text-classification"
] | 2022-10-28T21:00:14Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-scoring
- sentiment-classification
- text-scoring
paperswithcode_id: glue
pretty_name: GLUE (General Language Understanding Evaluation benchmark)
train-eval-index:
- config: cola
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
- config: sst2
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
- config: mrpc
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: qqp
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question1: text1
question2: text2
label: target
- config: stsb
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: mnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation_matched
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: mnli_mismatched
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: mnli_matched
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: qnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question: text1
sentence: text2
label: target
- config: rte
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: wnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
configs:
- ax
- cola
- mnli
- mnli_matched
- mnli_mismatched
- mrpc
- qnli
- qqp
- rte
- sst2
- stsb
- wnli
tags:
- qa-nli
- coreference-nli
- paraphrase-identification
---
# Dataset Card for GLUE
## Table of Contents
- [Dataset Card for GLUE](#dataset-card-for-glue)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [ax](#ax)
- [cola](#cola)
- [mnli](#mnli)
- [mnli_matched](#mnli_matched)
- [mnli_mismatched](#mnli_mismatched)
- [mrpc](#mrpc)
- [qnli](#qnli)
- [qqp](#qqp)
- [rte](#rte)
- [sst2](#sst2)
- [stsb](#stsb)
- [wnli](#wnli)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [ax](#ax-1)
- [cola](#cola-1)
- [mnli](#mnli-1)
- [mnli_matched](#mnli_matched-1)
- [mnli_mismatched](#mnli_mismatched-1)
- [mrpc](#mrpc-1)
- [qnli](#qnli-1)
- [qqp](#qqp-1)
- [rte](#rte-1)
- [sst2](#sst2-1)
- [stsb](#stsb-1)
- [wnli](#wnli-1)
- [Data Fields](#data-fields)
- [ax](#ax-2)
- [cola](#cola-2)
- [mnli](#mnli-2)
- [mnli_matched](#mnli_matched-2)
- [mnli_mismatched](#mnli_mismatched-2)
- [mrpc](#mrpc-2)
- [qnli](#qnli-2)
- [qqp](#qqp-2)
- [rte](#rte-2)
- [sst2](#sst2-2)
- [stsb](#stsb-2)
- [wnli](#wnli-2)
- [Data Splits](#data-splits)
- [ax](#ax-3)
- [cola](#cola-3)
- [mnli](#mnli-3)
- [mnli_matched](#mnli_matched-3)
- [mnli_mismatched](#mnli_mismatched-3)
- [mrpc](#mrpc-3)
- [qnli](#qnli-3)
- [qqp](#qqp-3)
- [rte](#rte-3)
- [sst2](#sst2-3)
- [stsb](#stsb-3)
- [wnli](#wnli-3)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://nyu-mll.github.io/CoLA/](https://nyu-mll.github.io/CoLA/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 955.33 MB
- **Size of the generated dataset:** 229.68 MB
- **Total amount of disk used:** 1185.01 MB
### Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
### Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found [at this address](https://gluebenchmark.com/). It comprises the following tasks:
#### ax
A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.
#### cola
The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.
#### mnli
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
#### mnli_matched
The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mnli_mismatched
The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mrpc
The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
#### qnli
The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.
#### qqp
The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.
#### rte
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.
#### sst2
The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.
#### stsb
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.
#### wnli
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).
### Languages
The language data in GLUE is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
#### ax
- **Size of downloaded dataset files:** 0.21 MB
- **Size of the generated dataset:** 0.23 MB
- **Total amount of disk used:** 0.44 MB
An example of 'test' looks as follows.
```
{
"premise": "The cat sat on the mat.",
"hypothesis": "The cat did not sit on the mat.",
"label": -1,
"idx: 0
}
```
#### cola
- **Size of downloaded dataset files:** 0.36 MB
- **Size of the generated dataset:** 0.58 MB
- **Total amount of disk used:** 0.94 MB
An example of 'train' looks as follows.
```
{
"sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
"label": 1,
"id": 0
}
```
#### mnli
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 78.65 MB
- **Total amount of disk used:** 376.95 MB
An example of 'train' looks as follows.
```
{
"premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
"hypothesis": "Product and geography are what make cream skimming work.",
"label": 1,
"idx": 0
}
```
#### mnli_matched
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 3.52 MB
- **Total amount of disk used:** 301.82 MB
An example of 'test' looks as follows.
```
{
"premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
"hypothesis": "Hierbas is a name worth looking out for.",
"label": -1,
"idx": 0
}
```
#### mnli_mismatched
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 3.73 MB
- **Total amount of disk used:** 302.02 MB
An example of 'test' looks as follows.
```
{
"premise": "What have you decided, what are you going to do?",
"hypothesis": "So what's your decision?,
"label": -1,
"idx": 0
}
```
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
#### ax
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### cola
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `unacceptable` (0), `acceptable` (1).
- `idx`: a `int32` feature.
#### mnli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_matched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_mismatched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Splits
#### ax
| |test|
|---|---:|
|ax |1104|
#### cola
| |train|validation|test|
|----|----:|---------:|---:|
|cola| 8551| 1043|1063|
#### mnli
| |train |validation_matched|validation_mismatched|test_matched|test_mismatched|
|----|-----:|-----------------:|--------------------:|-----------:|--------------:|
|mnli|392702| 9815| 9832| 9796| 9847|
#### mnli_matched
| |validation|test|
|------------|---------:|---:|
|mnli_matched| 9815|9796|
#### mnli_mismatched
| |validation|test|
|---------------|---------:|---:|
|mnli_mismatched| 9832|9847|
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
journal={arXiv preprint arXiv:1805.12471},
year={2018}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```
### Contributions
Thanks to [@patpizio](https://github.com/patpizio), [@jeswan](https://github.com/jeswan), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
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] |
svjack/pokemon-blip-captions-en-ja
|
svjack
| 2022-10-31T06:22:04Z | 28 | 3 |
[
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:huggan/few-shot-pokemon",
"language:en",
"language:ja",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-to-image"
] | 2022-10-29T07:26:57Z |
---
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
- ja
language_creators:
- other
multilinguality:
- multilingual
pretty_name: 'Pokémon BLIP captions'
size_categories:
- n<1K
source_datasets:
- huggan/few-shot-pokemon
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for Pokémon BLIP captions with English and Japanese.
Dataset used to train Pokémon text to image model, add a Japanese Column of [Pokémon BLIP captions](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions)
BLIP generated captions for Pokémon images from Few Shot Pokémon dataset introduced by Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis (FastGAN). Original images were obtained from FastGAN-pytorch and captioned with the pre-trained BLIP model.
For each row the dataset contains image en_text (caption in English) and ja_text (caption in Japanese) keys. image is a varying size PIL jpeg, and text is the accompanying text caption. Only a train split is provided.
The Japanese captions are translated by [Deepl](https://www.deepl.com/translator)
|
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mrbesher/tr-paraphrase-opensubtitles2018
|
mrbesher
| 2022-11-15T13:33:12Z | 28 | 1 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-11-15T13:18:54Z |
---
license: cc-by-4.0
---
|
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] |
arbml/arabic_pos_dialect
|
arbml
| 2022-11-18T14:40:06Z | 28 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2022-11-18T14:17:01Z |
---
license: apache-2.0
---
|
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autoevaluate/autoeval-eval-futin__feed-sen_vi-b48d12-2175169953
|
autoevaluate
| 2022-11-21T05:16:06Z | 28 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-11-21T04:36:28Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/feed
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b7
metrics: []
dataset_name: futin/feed
dataset_config: sen_vi
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-1b7
* Dataset: futin/feed
* Config: sen_vi
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
|
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liuyanchen1015/VALUE_mnli_dey_it
|
liuyanchen1015
| 2022-11-28T22:28:58Z | 28 | 0 |
[
"region:us"
] | null | 2022-11-28T22:28:35Z |
---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: train
num_bytes: 7643138
num_examples: 33927
- name: dev_matched
num_bytes: 189967
num_examples: 863
- name: dev_mismatched
num_bytes: 171667
num_examples: 709
- name: test_matched
num_bytes: 186114
num_examples: 849
- name: test_mismatched
num_bytes: 158987
num_examples: 717
download_size: 5183771
dataset_size: 8349873
---
# Dataset Card for "VALUE2_mnli_dey_it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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memray/nus
|
memray
| 2022-12-31T06:15:39Z | 28 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-12-31T06:15:13Z |
---
license: cc-by-nc-sa-4.0
---
|
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IlyaGusev/ru_stackoverflow
|
IlyaGusev
| 2023-03-09T23:48:16Z | 28 | 8 |
[
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:ru",
"license:other",
"region:us"
] |
[
"text-generation",
"question-answering"
] | 2023-02-13T14:32:35Z |
---
license: other
task_categories:
- text-generation
- question-answering
language:
- ru
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: question_id
dtype: uint32
- name: url
dtype: string
- name: answer_count
dtype: uint32
- name: text_html
dtype: string
- name: text_markdown
dtype: string
- name: score
dtype: int32
- name: title
dtype: string
- name: tags
sequence: string
- name: views
dtype: uint64
- name: author
dtype: string
- name: timestamp
dtype: uint64
- name: comments
sequence:
- name: text
dtype: string
- name: author
dtype: string
- name: comment_id
dtype: uint32
- name: score
dtype: int32
- name: timestamp
dtype: uint64
- name: answers
sequence:
- name: answer_id
dtype: uint32
- name: is_accepted
dtype: uint8
- name: text_html
dtype: string
- name: text_markdown
dtype: string
- name: score
dtype: int32
- name: author
dtype: string
- name: timestamp
dtype: uint64
- name: comments
sequence:
- name: text
dtype: string
- name: author
dtype: string
- name: comment_id
dtype: uint32
- name: score
dtype: int32
- name: timestamp
dtype: uint64
splits:
- name: train
num_bytes: 3013377174
num_examples: 437604
download_size: 670468664
dataset_size: 3013377174
---
# Russian StackOverflow dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Description](#description)
- [Usage](#usage)
- [Data Instances](#data-instances)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Licensing Information](#licensing-information)
## Description
**Summary:** Dataset of questions, answers, and comments from [ru.stackoverflow.com](https://ru.stackoverflow.com/).
**Script:** [create_stackoverflow.py](https://github.com/IlyaGusev/rulm/blob/hf/data_processing/create_stackoverflow.py)
**Point of Contact:** [Ilya Gusev]([email protected])
**Languages:** The dataset is in Russian with some programming code.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Loading:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/ru_stackoverflow', split="train")
for example in dataset:
print(example["text_markdown"])
print()
```
## Data Instances
```
{
"question_id": 11235,
"answer_count": 1,
"url": "https://ru.stackoverflow.com/questions/11235",
"score": 2,
"tags": ["c++", "сериализация"],
"title": "Извлечение из файла, запись в файл",
"views": 1309,
"author": "...",
"timestamp": 1303205289,
"text_html": "...",
"text_markdown": "...",
"comments": {
"text": ["...", "...",
"author": ["...", "..."],
"comment_id": [11236, 11237],
"score": [0, 0],
"timestamp": [1303205411, 1303205678]
},
"answers": {
"answer_id": [11243, 11245],
"timestamp": [1303207791, 1303207792],
"is_accepted": [1, 0],
"text_html": ["...", "..."],
"text_markdown": ["...", "..."],
"score": [3, 0],
"author": ["...", "..."],
"comments": {
"text": ["...", "..."],
"author": ["...", "..."],
"comment_id": [11246, 11249],
"score": [0, 0],
"timestamp": [1303207961, 1303207800]
}
}
}
```
You can use this little helper to unflatten sequences:
```python
def revert_flattening(records):
fixed_records = []
for key, values in records.items():
if not fixed_records:
fixed_records = [{} for _ in range(len(values))]
for i, value in enumerate(values):
fixed_records[i][key] = value
return fixed_records
```
The original JSONL is already unflattened.
## Source Data
* The data source is the [Russian StackOverflow](https://ru.stackoverflow.com/) website.
* Original XMLs: [ru.stackoverflow.com.7z](https://ia600107.us.archive.org/27/items/stackexchange/ru.stackoverflow.com.7z).
* Processing script is [here](https://github.com/IlyaGusev/rulm/blob/hf/data_processing/create_stackoverflow.py).
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.
## Licensing Information
According to the license of original data, this dataset is distributed under [CC BY-SA 2.5](https://creativecommons.org/licenses/by-sa/2.5/).
|
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jonathan-roberts1/GID
|
jonathan-roberts1
| 2023-03-31T15:38:31Z | 28 | 0 |
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
[
"image-classification",
"zero-shot-image-classification"
] | 2023-02-15T16:42:03Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': arbor woodland
'1': artificial grassland
'2': dry cropland
'3': garden plot
'4': industrial land
'5': irrigated land
'6': lake
'7': natural grassland
'8': paddy field
'9': pond
'10': river
'11': rural residential
'12': shrub land
'13': traffic land
'14': urban residential
splits:
- name: train
num_bytes: 1777210275
num_examples: 30000
download_size: 1263253291
dataset_size: 1777210275
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "GID"
## Dataset Description
- **Paper** [Land-cover classification with high-resolution remote sensing images using transferable deep models](https://www.sciencedirect.com/science/article/pii/S0034425719303414)
### Licensing Information
Public domain.
## Citation Information
[Land-cover classification with high-resolution remote sensing images using transferable deep models](https://www.sciencedirect.com/science/article/pii/S0034425719303414)
```
@article{GID2020,
title = {Land-cover classification with high-resolution remote sensing images using transferable deep models},
author = {Tong, Xin-Yi and Xia, Gui-Song and Lu, Qikai and Shen, Huanfeng and Li, Shengyang and You, Shucheng and Zhang, Liangpei},
year = 2020,
journal = {Remote Sensing of Environment},
volume = 237,
pages = 111322
}
```
|
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] |
KocLab-Bilkent/turkish-constitutional-court
|
KocLab-Bilkent
| 2023-02-20T19:53:46Z | 28 | 0 |
[
"task_categories:text-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:tr",
"license:cc-by-4.0",
"region:us"
] |
[
"text-classification"
] | 2023-02-18T15:03:21Z |
---
license: cc-by-4.0
task_categories:
- text-classification
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
language:
- tr
size_categories:
- 10M<n<100M
pretty_name: predicting-turkish-constitutional-court-decisions
source_datasets:
- original
---
## 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)
- **Homepage:**
- **Repository:** https://github.com/koc-lab/law-turk
- **Paper:** https://doi.org/10.1016/j.ipm.2021.102684
- **Point of Contact:** [Ceyhun Emre Öztürk](mailto:[email protected])
### Dataset Summary
This dataset is extracted from the following Github repo, which was created for the journal paper with URL https://www.sciencedirect.com/science/article/abs/pii/S0306457321001692.
https://github.com/koc-lab/law-turk
The dataset includes 1290 court case decision texts from the Turkish Court of Cassation. Each sample has one label, which is the ruling of the court. The possible rulings are "Violation" and "No violation". There are 1290 samples. 1141 of these samples are labeled as "Violation".
### Supported Tasks and Leaderboards
Legal Judgment Prediction
### Languages
Turkish
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present (train, validation and test) for each configuration.
### Data Fields
The dataset contains the following fields:
- `Text`: Legal case decision texts
- `Label`: The ruling of the court.
- 'Violation': The court decides for the legal case that there is a violation of the constitution.
- 'No violation': The court decides for the legal case that there is no violation of the constitution.
### Data Splits
The data has been split randomly into 70% train (903), 15% validation (195), 15% test (195).
## Dataset Creation
### Curation Rationale
This dataset was created to further the research on developing models for predicting Brazilian court decisions that are
also able to predict whether the decision will be unanimous.
### Source Data
The data were collected from *Türkiye Cumhuriyeti Anayasa Mahkemesi* (T.C. AYM, Turkish Constitutional Court).
#### Initial Data Collection and Normalization
The data were collected from the official website of the Turkish Contitutional Court: https://www.anayasa.gov.tr/tr/kararlar-bilgi-bankasi/.
#### Who are the source language producers?
The source language producers are judges.
### Annotations
#### Annotation process
The dataset was not annotated.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The court decisions might contain sensitive information about individuals.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
### Dataset Curators
The data collection was done by Emre Mumcuoğlu ([Email](mailto:[email protected])).
### Licensing Information
No licensing information was provided for this dataset. However, please make sure that you use the dataset according to
Turkish law.
### Citation Information
```
@article{mumcuoglu21natural,
title = {{Natural language processing in law: Prediction of outcomes in the higher courts of Turkey}},
journal = {Information Processing \& Management},
volume = {58},
number = {5},
year = {2021},
author = {Mumcuoğlu, Emre and Öztürk, Ceyhun E. and Ozaktas, Haldun M. and Koç, Aykut}
}
```
|
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] |
mstz/arhythmia
|
mstz
| 2023-04-15T11:37:57Z | 28 | 0 |
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"arrhythmia",
"tabular_classification",
"multiclass_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-05T12:44:38Z |
---
language:
- en
tags:
- arrhythmia
- tabular_classification
- multiclass_classification
- binary_classification
- UCI
pretty_name: Arhythmia
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- arhytmia
- has_arhytmia
license: cc
---
# Arhythmia
The [Arrhythmia dataset](https://archive.ics.uci.edu/ml/datasets/Arrhythmia) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Does the patient have arhythmia? If so, what type?
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| arhytmia | Multiclass classification | What type of arhythmia does the patient have? |
| has_arhytmia | Binary classification | Does the patient have arhythmia? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/arhythmia", "arhythmia")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
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] |
distil-whisper/voxpopuli
|
distil-whisper
| 2023-09-25T10:30:13Z | 28 | 0 |
[
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc0-1.0",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2023-04-07T17:10:56Z |
---
license: cc0-1.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: VoxPopuli
---
# Distil Whisper: VoxPopuli
This is a variant of the [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/facebook/voxpopuli).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/voxpopuli", "en")
# take the first sample of the validation set
sample = dataset["validation"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/voxpopuli", "en", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under cc0-1.0.
|
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lansinuote/diffsion_from_scratch
|
lansinuote
| 2023-04-14T06:36:47Z | 28 | 0 |
[
"region:us"
] | null | 2023-04-14T06:34:05Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 119417305.0
num_examples: 833
download_size: 99672356
dataset_size: 119417305.0
---
# Dataset Card for "diffsion_from_scratch"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
mstz/gisette
|
mstz
| 2023-04-17T10:55:16Z | 28 | 0 |
[
"task_categories:tabular-classification",
"language:en",
"gisette",
"tabular_classification",
"binary_classification",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-17T10:43:21Z |
---
language:
- en
tags:
- gisette
- tabular_classification
- binary_classification
pretty_name: Gisette
task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
- tabular-classification
configs:
- gisette
---
# Gisette
The [Gisette dataset](https://archive-beta.ics.uci.edu/dataset/170/gisette) from the [UCI repository](https://archive-beta.ics.uci.edu/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-----------------------|---------------------------|-------------------------|
| gisette | Binary classification.| |
|
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] |
gimmaru/story_cloze-2016
|
gimmaru
| 2023-05-08T03:00:51Z | 28 | 1 |
[
"region:us"
] | null | 2023-05-08T03:00:22Z |
---
dataset_info:
features:
- name: story_id
dtype: string
- name: input_sentence_1
dtype: string
- name: input_sentence_2
dtype: string
- name: input_sentence_3
dtype: string
- name: input_sentence_4
dtype: string
- name: sentence_quiz1
dtype: string
- name: sentence_quiz2
dtype: string
- name: answer_right_ending
dtype: int32
splits:
- name: test
num_bytes: 326264
num_examples: 1000
download_size: 0
dataset_size: 326264
---
# Dataset Card for "story_cloze-2016"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Den4ikAI/ru_sberquad_long_answers
|
Den4ikAI
| 2023-05-29T05:32:22Z | 28 | 5 |
[
"task_categories:question-answering",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:mit",
"region:us"
] |
[
"question-answering",
"text2text-generation"
] | 2023-05-28T17:25:41Z |
---
license: mit
task_categories:
- question-answering
- text2text-generation
language:
- ru
size_categories:
- 10K<n<100K
---
UPD 29.05.2023: Добавлены негативные примеры.
Датасет для ответов на вопросы по тексту.
Сгенерирован моделью Den4ikAI/FRED-T5-XL_instructor
Отличия от sberquad, xquad и т.д:
1. Ответы не односложные, развернутые, представляют несколько предложений
2. Не подходит для обучения энкодерных моделей!
|
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TigerResearch/dev_sft
|
TigerResearch
| 2023-06-16T01:55:22Z | 28 | 2 |
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:zh",
"license:apache-2.0",
"region:us"
] |
[
"text-generation"
] | 2023-05-29T12:23:31Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 29836
num_examples: 80
- name: validation
num_bytes: 9086
num_examples: 20
download_size: 0
dataset_size: 38922
license: apache-2.0
task_categories:
- text-generation
language:
- zh
size_categories:
- n<1K
---
# Dataset Card for "dev_sft"
[Tigerbot模型](https://github.com/TigerResearch/TigerBot#%E6%A8%A1%E5%9E%8B%E4%B8%8B%E8%BD%BD)develop sft数据。
在[train_sft.py](https://github.com/TigerResearch/TigerBot/blob/main/train/train_sft.py)中被使用。
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/dev_sft')
```
## Field
- instruction: 指令
- input: 上下文信息(Optional)
- output: 生成目标
|
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CIRAL/ciral
|
CIRAL
| 2023-08-21T15:49:42Z | 28 | 1 |
[
"task_categories:text-retrieval",
"language:ha",
"language:so",
"language:sw",
"language:yo",
"license:apache-2.0",
"region:us"
] |
[
"text-retrieval"
] | 2023-06-12T20:06:09Z |
---
license: apache-2.0
language:
- ha
- so
- sw
- yo
task_categories:
- text-retrieval
mutilinguality:
- multilingual
viewer: true
---
# Dataset Summary
CIRAL is a collection for cross-lingual information retrieval research across four (4) African languages. The collection comprises English queries and query-passage relevance judgements for passages in the African languages.
This dataset repo contains only the queries and relevance judgements. The corpus collection can be found here [here](https://huggingface.co/datasets/CIRAL/ciral-corpus)
# Dataset Structure
1. To download the files: The queries can be found under `ciral-{lang}/topics` and are in `.tsv` formats with each line in the form:
```
qid\tquery
```
while the judgements are in the folder `ciral-{lang}/qrels`, with each file in the standard TREC format:
```
qid Q0 docid relevance
```
2. To access the dataset via `datasets`:
```
ciral_dataset = load_dataset("ciral/ciral", "hausa") #or swahili, somali, yoruba
for data in ciral_data['train']: # or 'test'
query_id = data['query_id']
query = data['query']
pos_qrels = data['positive_passages']
neg_qrels = data['negative_passages']
for qrel in pos_qrels:
docid = qrel['docid']
text = qrel['text']
```
## Citation
...
|
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] |
BerMaker/test
|
BerMaker
| 2023-06-13T09:44:54Z | 28 | 0 |
[
"task_categories:text-classification",
"size_categories:n<1K",
"license:apache-2.0",
"code",
"art",
"region:us"
] |
[
"text-classification"
] | 2023-06-13T08:12:00Z |
---
license: apache-2.0
task_categories:
- text-classification
tags:
- code
- art
size_categories:
- n<1K
---
|
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hkust-nlp/felm
|
hkust-nlp
| 2023-10-03T17:29:57Z | 28 | 10 |
[
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2310.00741",
"region:us"
] |
[
"text-generation"
] | 2023-06-14T07:24:44Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- en
pretty_name: FELM
---
# Dataset Card for FELM
## Table of Contents
- [Dataset Card for FELM](#dataset-card-for-FELM)
- [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)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Initial Data Collection and Clean](#initial-data-collection-and-clean)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/hkust-nlp/felm
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
[[Paper]](https://arxiv.org/abs/2310.00741) [[Github Repo]](https://github.com/hkust-nlp/felm)
FELM is a meta-benchmark to evaluate factuality evaluation for large language models.<br>
The benchmark comprises 847 questions that span five distinct domains: world knowledge, science/technology, writing/recommendation, reasoning, and math. We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.<br>
We then obtain responses from ChatGPT for these prompts. For each response, we employ fine-grained annotation at the segment level, which includes reference links, identified error types, and the reasons behind these errors as provided by our annotators.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
An example looks as follows:
```python
{"index": "0",
"source": "quora",
"prompt": "Which country or city has the maximum number of nuclear power plants?",
"response": "The United States has the highest number of nuclear power plants in the world, with 94 operating reactors. Other countries with a significant number of nuclear power plants include France, China, Russia, and South Korea.",
"segmented_response": ["The United States has the highest number of nuclear power plants in the world, with 94 operating reactors.", "Other countries with a significant number of nuclear power plants include France, China, Russia, and South Korea."],
"labels": [false, true],
"comment": ["As of December 2022, there were 92 operable nuclear power reactors in the United States.", ""],
"type": ["knowledge_error", null],
"ref": ["https://www.eia.gov/tools/faqs/faq.php?id=207&t=3"]}
```
### Data Fields
| Field Name | Field Value | Description |
| ----------- | ----------- | ------------------------------------------- |
| index | Integer | the order number of the data point |
| source | string | the prompt source |
| prompt | string | the prompt for generating response |
| response | string | the response of ChatGPT for prompt |
| segmented_response | list | segments of reponse |
| labels | list | factuality labels for segmented_response |
| comment | list | error reasons for segments with factual error |
| type | list | error types for segments with factual error |
| ref | list | reference links |
## Dataset Creation
### Source Data
#### Initial Data Collection and Clean
We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.
The data is cleaned by authors.
### Annotations
#### Annotation process
We have developed an annotation tool and established annotation guidelines. All annotations undergo a double-check process, which involves review by both other annotators and an expert reviewer.
#### Who are the annotators?
The authors of the paper; Yuzhen Huang, Yikai Zhang, Tangjun Su.
## Additional Information
### Licensing Information
This dataset is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/)).
### Citation Information
```bibtex
@inproceedings{
chen2023felm,
title={FELM: Benchmarking Factuality Evaluation of Large Language Models},
author={Chen, Shiqi and Zhao, Yiran and Zhang, Jinghan and Chern, I-Chun and Gao, Siyang and Liu, Pengfei and He, Junxian},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={http://arxiv.org/abs/2310.00741}
}
```
### Contributions
[Needs More Information]
|
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] |
RepoFusion/Stack-Repo
|
RepoFusion
| 2023-07-10T19:43:46Z | 28 | 5 |
[
"license:other",
"arxiv:2206.12839",
"arxiv:2306.10998",
"region:us"
] | null | 2023-06-14T16:23:30Z |
---
license: other
---
# Summary of the Dataset
## Description
Stack-Repo is a dataset of 200 Java repositories from GitHub with permissive licenses and near-deduplicated files that are augmented with three types of repository contexts.
- Prompt Proposal (PP) Contexts: These contexts are based on the prompt proposals from the paper [Repository-Level Prompt Generation for Large Language Models of Code](https://arxiv.org/abs/2206.12839).
- BM25 Contexts: These contexts are obtained based on the BM25 similarity scores.
- RandomNN Contexts: These contexts are obtained using the nearest neighbors in the representation space of an embedding model.
For more details, please check our paper [RepoFusion: Training Code Models to Understand Your Repository](https://arxiv.org/abs/2306.10998).
The original Java source files are obtained using a [modified version](https://huggingface.co/datasets/bigcode/the-stack-dedup) of [The Stack](https://huggingface.co/datasets/bigcode/the-stack).
## Data Splits
The dataset consists of three splits: `train`, `validation` and `test`, comprising of 100, 50, and 50 repositories, respectively.
## Data Organization
Each split contains separate folder for a repository where each repository contains all `.java` source code files in the repository in the original directory structure along with three `.json` files corresponding to the PP, BM25 and RandomNN repo contexts. In terms of the HuggingFace Datasets terminology, we have four subdatasets or configurations.
- `PP_contexts`: Propmt Proposal repo contexts.
- `bm25_contexts`: BM25 repo contexts.
- `randomNN_contexts`: RandomNN repo contexts.
- `sources`: actual java (`.java`) source code files
# Dataset Usage
To clone the dataset locally
```
git clone https://huggingface.co/datasets/RepoFusion/Stack-Repo <local_path>
```
To load the dataset desired configuration and split:
```python
import datasets
ds = datasets.load_dataset(
"RepoFusion/Stack-Repo",
name="<configuration_name>",
split="<split_name>"
data_dir="<local_path>"
)
```
NOTE: The configurations for the repo contexts `bm25_contexts`, `PP_contexts` and `randomNN_contexts` can be loaded directly by specifying the corresponding
`<configuration_name>` along with the `<split_name>` in the load_dataset command listed above without cloning the repo locally.
For the `sources` if not cloned beforehand or `data_dir` not specified, `ManualDownloadError` will be raised.
## Data Format
The expected data format of the `.json` files is a list of target holes and corresponding repo contexts where each entry in the `.json` file corresponds to a target hole consisting of the location of the target hole, the target hole as a string, the surrounding context as a string and a list of repo-contexts as strings. Specifically, each row is a dictionary containing
- `id`: hole_id (location of the target hole)
- `question`: surrounding context
- `target`: target hole
- `ctxs`: a list of repo contexts where each item is a dictionary containing
- `title`: name of the repo context
- `text`: content of the repo context
The actual java sources can be accessed via file system directly. The format is like this `[<data_set_root>/data/<split_name>/<github_user>/<repo_name>/<path/to/every/java/file/in/the/repo>.java]`. When accessed through `Datasets.load_dataset`, the data fields for the `sources` can be specified as below.
```python
features = datasets.Features({
'file': datasets.Value('string'),
'content': datasets.Value('string')
})
```
When accessed through `Datasets.load_dataset`, the data fields for the repo contexts can be specified as below.
```python
features = datasets.Features({
'id': datasets.Value('string'),
'hole_file': datasets.Value('string'),
'hole_line': datasets.Value('int32'),
'hole_pos': datasets.Value('int32'),
'question': datasets.Value('string'),
'target': datasets.Value('string'),
'answers': datasets.Sequence(
datasets.Value('string')
),
'ctxs': [{
'title': datasets.Value('string'),
'text': datasets.Value('string'),
'score': datasets.Value('float64')
}]
})
```
# Additional Information
## Dataset Curators
- Disha Shrivastava, [email protected]
- Denis Kocetkov, [email protected]
## Licensing Information
Stack-Repo is derived from a modified version of The Stack. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.
The list of [SPDX license identifiers](https://spdx.org/licenses/) included in the dataset can be found [here](https://huggingface.co/datasets/bigcode/the-stack-dedup/blob/main/licenses.json).
## Citation
```
@article{shrivastava2023repofusion,
title={RepoFusion: Training Code Models to Understand Your Repository},
author={Shrivastava, Disha and Kocetkov, Denis and de Vries, Harm and Bahdanau, Dzmitry and Scholak, Torsten},
journal={arXiv preprint arXiv:2306.10998},
year={2023}
}
```
|
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MoritzLaurer/sentiment_economy_news
|
MoritzLaurer
| 2023-06-28T10:28:33Z | 28 | 2 |
[
"region:us"
] | null | 2023-06-28T10:28:19Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: labels
dtype: string
- name: articleid
dtype: string
- name: relevance
dtype: string
- name: positivity
dtype: string
- name: split
dtype: string
- name: positivity_rounded
dtype: string
- name: idx
dtype: int64
splits:
- name: train
num_bytes: 5122725
num_examples: 3000
- name: test
num_bytes: 653059
num_examples: 382
- name: train_sample
num_bytes: 1684685
num_examples: 1000
- name: train_sample_numeric
num_bytes: 1720504
num_examples: 1000
download_size: 5611673
dataset_size: 9180973
---
# Dataset Card for "sentiment_economy_news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
iceberg-nlp/climabench
|
iceberg-nlp
| 2023-09-10T22:05:20Z | 28 | 0 |
[
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"arxiv:2301.04253",
"region:us"
] |
[
"text-classification"
] | 2023-06-29T22:37:24Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
paperswithcode_id: climabench
pretty_name: "ClimaBench: A Benchmark Dataset For Climate Change Text Understanding in English"
config_names:
- climate_stance
- climate_eng
- climate_fever
- climatext
- clima_insurance
- clima_insurance_plus
- clima_cdp
- clima_qa
---
### Citation Information
```
@misc{spokoyny2023answering,
title={Towards Answering Climate Questionnaires from Unstructured Climate Reports},
author={Daniel Spokoyny and Tanmay Laud and Tom Corringham and Taylor Berg-Kirkpatrick},
year={2023},
eprint={2301.04253},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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unwilledset/raven-data
|
unwilledset
| 2023-10-21T05:13:54Z | 28 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-07-10T09:54:46Z |
---
license: apache-2.0
---
|
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] |
Gustrd/dolly-15k-libretranslate-pt
|
Gustrd
| 2023-07-18T02:04:29Z | 28 | 4 |
[
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:pt",
"license:cc-by-sa-3.0",
"region:us"
] |
[
"question-answering",
"summarization"
] | 2023-07-13T12:30:13Z |
---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
language:
- pt
size_categories:
- 10K<n<100K
---
# Summary
databricks-dolly-15k ( https://huggingface.co/datasets/databricks/databricks-dolly-15k/ ) is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.
This is a portuguese translation done with libretranslate ( https://github.com/LibreTranslate/LibreTranslate ).
This dataset can be used for any purpose, whether academic or commercial, under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported License.
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Portuguese
Version: 1.0
---
# Original Readme
Dataset Overview
databricks-dolly-15k is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category.
Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly.
For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the context field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. [42]) which we recommend users remove for downstream applications.
Intended Uses
While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories.
Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets.
Dataset
Purpose of Collection
As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications.
Sources
Human-generated data: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories.
Wikipedia: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages.
Annotator Guidelines
To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor.
The annotation guidelines for each of the categories are as follows:
Creative Writing: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better.
Closed QA: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form.
Open QA: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation.
Summarization: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form.
Information Extraction: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form.
Classification: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better.
Brainstorming: Think up lots of examples in response to a question asking to brainstorm ideas.
Personal or Sensitive Data
This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information.
Known Limitations
Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia
Some annotators may not be native English speakers
Annotator demographics and subject matter may reflect the makeup of Databricks employees
---
license: cc-by-sa-3.0
---
|
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] |
SCM-LAB/ViQP
|
SCM-LAB
| 2023-11-08T16:13:36Z | 28 | 1 |
[
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:vi",
"license:mit",
"doi:10.57967/hf/1333",
"region:us"
] |
[
"text2text-generation"
] | 2023-07-16T14:57:48Z |
---
license: mit
viewer: true
task_categories:
- text2text-generation
language:
- vi
size_categories:
- 10K<n<100K
---
# ViQP: Dataset for Vietnamese Question Paraphrasing
## Dataset sample
An example of 'viqp_train.json' looks as follows.
```json
{
"source": "Trong thuật toán Caesar Cipher, ký tự K với mã hóa k=4 thì sẽ được chữ mới gì?",
"target": [
"Ký tự K với mã hóa k=4 trong thuật toán Caesar Cipher thì sẽ được chữ gì?",
"Ký tự K với mã hóa k=4 trong thuật toán Caesar Cipher thì sẽ được chữ mới gì?",
"Trong thuật toán Caesar Cipher, ký tự K với mã hóa k=4 thì sẽ được chữ gì?",
"Trong thuật toán Caesar Cipher, mã hóa k=4 thì ký tự K sẽ là chữ gì?"
],
"type": "ENTY_letter"
}
```
# Model resource
Question paraphrasing model after training with ViQP is available at [here](https://huggingface.co/SCM-LAB/vietnamese_question_paraphrasing_ViT5_base)
```cmd
pip install simplet5
```
```python
from simplet5 import SimpleT5
model = SimpleT5()
model.load_model("t5", "SCM-LAB/vietnamese_question_paraphrasing_ViT5_base", use_gpu=True)
question = "Tác phẩm Lục Vân Tiên được sáng tác năm nào?"
model.predict(f"question paraphrasing: {question}", num_return_sequences= 5, num_beams= 5)
>>> ['Tác phẩm Lục Vân Tiên được sáng tác lúc nào?',
'Tác phẩm Lục Vân Tiên được sáng tác khi nào?',
'Tác phẩm Lục Vân Tiên được sáng tác vào năm nào?',
'Lúc nào tác phẩm Lục Vân Tiên được sáng tác?',
'Tác phẩm Lục Vân Tiên được sáng tác vào lúc nào?']
```
# Citation Information
```
@INPROCEEDINGS{10288738,
author={Nguyen, Sang Quang and Vo, Thuc Dinh and Nguyen, Duc P.A and Tran, Dang T. and Nguyen, Kiet Van},
booktitle={2023 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)},
title={ViQP: Dataset for Vietnamese Question Paraphrasing},
year={2023},
volume={},
number={},
pages={1-6},
doi={10.1109/MAPR59823.2023.10288738}
}
```
**Please CITE** our paper when ViQP is used to help produce published results or is incorporated into other software.
|
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] |
SachinKaushik/LlamaV2InstructCode
|
SachinKaushik
| 2023-07-21T19:17:00Z | 28 | 3 |
[
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:en",
"python",
"llamav2",
"instruction",
"code",
"region:us"
] |
[
"text-generation",
"text2text-generation"
] | 2023-07-21T17:41:06Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: llamaV2Instruct
dtype: string
splits:
- name: train
num_bytes: 241331660
num_examples: 121959
download_size: 0
dataset_size: 241331660
task_categories:
- text-generation
- text2text-generation
language:
- en
tags:
- python
- llamav2
- instruction
- code
---
# Dataset Card for "LlamaV2InstructCode"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
medarc/pubmed
|
medarc
| 2023-07-27T21:28:00Z | 28 | 0 |
[
"region:us"
] | null | 2023-07-27T20:21:24Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: meta
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 93599167012
num_examples: 2964753
download_size: 41109917186
dataset_size: 93599167012
---
# Dataset Card for "pubmed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nisaar/Indian_Const_Articles_LLAMA2_Format
|
nisaar
| 2023-07-30T15:19:50Z | 28 | 3 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-07-30T15:18:37Z |
---
license: apache-2.0
---
|
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] |
kayteekay/bookimg_dataset
|
kayteekay
| 2023-08-04T06:15:58Z | 28 | 0 |
[
"region:us"
] | null | 2023-08-04T04:43:10Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 289585512.68
num_examples: 32581
download_size: 0
dataset_size: 289585512.68
---
# Dataset Card for "bookimg_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nampdn-ai/tiny-orca-textbooks
|
nampdn-ai
| 2023-09-28T02:15:06Z | 28 | 12 |
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.05463",
"arxiv:2305.07759",
"region:us"
] |
[
"text-generation"
] | 2023-08-04T09:44:37Z |
---
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Orca Textbooks
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# Textbook-like Dataset: A Comprehensive Resource for Text-Based Skills Development in Small Language Models
This dataset is a collection of **147k synthetic textbooks** designed to enhance the text-based skills of small language models. The curriculum is meticulously structured to progress from simple to complex tasks, ensuring a gradual and effective learning experience during pretraining or finetuning SLMs.
The inspiration for this dataset comes from the technical report paper, [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463). The source texts incorporated in this dataset are derived from the [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) dataset, a well-known resource in the field.
Emphasizing text-based skills, this dataset serves as a practical reasoning for small language models to learn and do exercise, providing them with a diverse range of skills to learn and adapt from. The step-by-step progression mirrors the structure of a textbook, making it an ideal in-context learning sample.
### Disclaimer
While every effort has been made to ensure the accuracy of the information contained within this dataset, please note that it is provided 'as is' and without any warranties.
The use of the `textbook` field in this dataset is intended for research purposes only. You are advised to verify any information obtained from this dataset before acting upon it.
## Tiny Series
Explore the possibilities and limitations of building Small Language Models with these tiny gems of data!
- [TinyStories](https://arxiv.org/abs/2305.07759): The paper that sparked my interest in the journey of the tiny-* series.
- [tiny-codes](https://huggingface.co/datasets/nampdn-ai/tiny-codes): Collection of 1.6M short and clear code snippets that can help LLM models learn how to reason.
- [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks): 420k "things of internet" synthetic textbooks.
- [tiny-webtext](https://huggingface.co/datasets/nampdn-ai/tiny-webtext): A 6GB (4.5M records) variety of diverse webtext enriched with critical thinking methods to make unbiased English dataset.
- [tiny-lessons](https://huggingface.co/datasets/nampdn-ai/tiny-lessons): Subset of *tiny-textbooks* dataset, various lessons about "things of internet" augmented in a bite-sized textbook Markdown format.
- [tiny-bridgedict](https://huggingface.co/datasets/nampdn-ai/tiny-bridgedict): A dataset that links and transfers knowledge between English, Vietnamese, Chinese in a tiny multilingual models.
### Others small HQ datasets with textbook-like quality
- [devdocs.io](https://huggingface.co/datasets/nampdn-ai/devdocs.io): FreeCodeCamp has provided 189k comprehensive API documentation across a wide range of tech stacks and programming languages.
- [sciphi-python-textbook](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-python-textbook)
- [textbook_quality_programming](https://huggingface.co/datasets/vikp/textbook_quality_programming)
- [sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need)
|
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TrainingDataPro/bald-people-segmentation-dataset
|
TrainingDataPro
| 2023-09-14T16:35:35Z | 28 | 1 |
[
"task_categories:image-segmentation",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"medical",
"region:us"
] |
[
"image-segmentation"
] | 2023-08-04T13:34:54Z |
---
license: cc-by-nc-nd-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- code
- medical
---
# Bald People Segmentation Dataset
The dataset consists of images of bald people and corresponding segmentation masks.
Segmentation masks highlight the regions of the images that delineate the bald scalp. By using these segmentation masks, researchers and practitioners can focus only on the areas of interest.
The dataset is designed to be accessible and easy to use, providing high-resolution images and corresponding segmentation masks in PNG format.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=bald-people-segmentation-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Content
### The dataset includes 2 folders:
- **Female** - the folder includes folders corresponding to each woman in the sample. Each of the subfolders contains of top images of women's heads and segmentation masks for the original photos.
- **Male** - the folder includes folders corresponding to each man in the sample. Each of the subfolders contains of front and top images of men's heads from and segmentation masks for the original photos.

### File with the extension .csv
- **link**: link to access the media file,
- **type**: type of the image,
- **gender**: gender of the person in the photo
# Bald People Segmentation might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=bald-people-segmentation-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
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EgilKarlsen/CSIC_GPT2_Baseline
|
EgilKarlsen
| 2023-08-17T18:30:52Z | 28 | 0 |
[
"region:us"
] | null | 2023-08-11T00:33:15Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115621178.4375
num_examples: 37500
- name: test
num_bytes: 38540392.5
num_examples: 12500
download_size: 211849176
dataset_size: 154161570.9375
---
# Dataset Card for "CSIC_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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wetdog/TUT-urban-acoustic-scenes-2018-development
|
wetdog
| 2023-08-19T00:08:29Z | 28 | 0 |
[
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"license:afl-3.0",
"region:us"
] |
[
"audio-classification"
] | 2023-08-17T16:14:41Z |
---
dataset_info:
features:
- name: scene_label
dtype: string
- name: identifier
dtype: string
- name: source_label
dtype: string
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 24883936611.28
num_examples: 8640
download_size: 24885037396
dataset_size: 24883936611.28
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: afl-3.0
task_categories:
- audio-classification
size_categories:
- 1K<n<10K
---
# Dataset Card for "TUT-urban-acoustic-scenes-2018-development"
## Dataset Description
- **Homepage: https://zenodo.org/record/1228142**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact: Toni Heittola ([email protected], http://www.cs.tut.fi/~heittolt/)**
### Dataset Summary
TUT Urban Acoustic Scenes 2018 development dataset consists of 10-seconds audio segments from 10 acoustic scenes:
Airport - airport
Indoor shopping mall - shopping_mall
Metro station - metro_station
Pedestrian street - street_pedestrian
Public square - public_square
Street with medium level of traffic - street_traffic
Travelling by a tram - tram
Travelling by a bus - bus
Travelling by an underground metro - metro
Urban park - park
Each acoustic scene has 864 segments (144 minutes of audio). The dataset contains in total 24 hours of audio.
The dataset was collected in Finland by Tampere University of Technology between 02/2018 - 03/2018.
The data collection has received funding from the European Research Council under the ERC Grant Agreement 637422 EVERYSOUND.
### Supported Tasks and Leaderboards
- `audio-classification`: The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* [metric name](https://huggingface.co/metrics/metric_name).
- The ([model name](https://huggingface.co/model_name) or [model class](https://huggingface.co/transformers/model_doc/model_class.html)) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard
- which can be found at [leaderboard url]() and ranks models based on [metric name](https://huggingface.co/metrics/metric_name) while also reporting [other metric name](https://huggingface.co/metrics/other_metric_name).
## Dataset Structure
### Data Instances
```
{
'scene_label': 'airport',
'identifier': 'barcelona-0',
'source_label': 'a',
'audio': {'path': '/data/airport-barcelona-0-0-a.wav'
'array': array([-1.91628933e-04, -1.18494034e-04, -1.87635422e-04, ...,
4.90546227e-05, -4.98890877e-05, -4.66108322e-05]),
'sampling_rate': 48000}
}
```
### Data Fields
- `scene_label`: acoustic scene label from the 10 class set,
- `identifier`: city-location id 'barcelona-0',
- `source_label: device id, for this dataset is always the same 'a',
Filenames of the dataset have the following pattern:
[scene label]-[city]-[location id]-[segment id]-[device id].wav
### Data Splits
A suggested training/test partitioning of the development set is provided in order to make results reported with this dataset uniform. The partitioning is done such that the segments recorded at the same location are included into the same subset - either training or testing. The partitioning is done aiming for a 70/30 ratio between the number of segments in training and test subsets while taking into account recording locations, and selecting the closest available option.
| Scene class | Train / Segments | Train / Locations | Test / Segments | Test / Locations |
| ------------------ | ---------------- | ----------------- | --------------- | ---------------- |
| Airport | 599 | 15 | 265 | 7 |
| Bus | 622 | 26 | 242 | 10 |
| Metro | 603 | 20 | 261 | 9 |
| Metro station | 605 | 28 | 259 | 12 |
| Park | 622 | 18 | 242 | 7 |
| Public square | 648 | 18 | 216 | 6 |
| Shopping mall | 585 | 16 | 279 | 6 |
| Street, pedestrian | 617 | 20 | 247 | 8 |
| Street, traffic | 618 | 18 | 246 | 7 |
| Tram | 603 | 24 | 261 | 11 |
| **Total** | **6122** | **203** | **2518** | **83** |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The dataset was recorded in six large European cities: Barcelona, Helsinki, London, Paris, Stockholm, and Vienna. For all acoustic scenes, audio was captured in multiple locations: different streets, different parks, different shopping malls. In each location, multiple 2-3 minute long audio recordings were captured in a few slightly different positions (2-4) within the selected location. Collected audio material was cut into segments of 10 seconds length.
The equipment used for recording consists of a binaural [Soundman OKM II Klassik/studio A3](http://www.soundman.de/en/products/) electret in-ear microphone and a [Zoom F8](https://www.zoom.co.jp/products/handy-recorder/zoom-f8-multitrack-field-recorder) audio recorder using 48 kHz sampling rate and 24 bit resolution. During the recording, the microphones were worn by the recording person in the ears, and head movement was kept to minimum.
### Annotations
#### Annotation process
Post-processing of the recorded audio involves aspects related to privacy of recorded individuals, and possible errors in the recording process. Some interferences from mobile phones are audible, but are considered part of real-world recording process.
#### Who are the annotators?
* Ronal Bejarano Rodriguez
* Eemi Fagerlund
* Aino Koskimies
* Toni Heittola
### Personal and Sensitive Information
The material was screened for content, and segments containing close microphone conversation were eliminated.
## 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
Toni Heittola ([email protected], http://www.cs.tut.fi/~heittolt/)
Annamaria Mesaros ([email protected], http://www.cs.tut.fi/~mesaros/)
Tuomas Virtanen ([email protected], http://www.cs.tut.fi/~tuomasv/)
### Licensing Information
Copyright (c) 2018 Tampere University of Technology and its licensors
All rights reserved.
Permission is hereby granted, without written agreement and without license or royalty
fees, to use and copy the TUT Urban Acoustic Scenes 2018 (“Work”) described in this document
and composed of audio and metadata. This grant is only for experimental and non-commercial
purposes, provided that the copyright notice in its entirety appear in all copies of this Work,
and the original source of this Work, (Audio Research Group from Laboratory of Signal
Processing at Tampere University of Technology),
is acknowledged in any publication that reports research using this Work.
Any commercial use of the Work or any part thereof is strictly prohibited.
Commercial use include, but is not limited to:
- selling or reproducing the Work
- selling or distributing the results or content achieved by use of the Work
- providing services by using the Work.
IN NO EVENT SHALL TAMPERE UNIVERSITY OF TECHNOLOGY OR ITS LICENSORS BE LIABLE TO ANY PARTY
FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE
OF THIS WORK AND ITS DOCUMENTATION, EVEN IF TAMPERE UNIVERSITY OF TECHNOLOGY OR ITS
LICENSORS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
TAMPERE UNIVERSITY OF TECHNOLOGY AND ALL ITS LICENSORS SPECIFICALLY DISCLAIMS ANY
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE. THE WORK PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND
THE TAMPERE UNIVERSITY OF TECHNOLOGY HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT,
UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
### Citation Information
[](https://doi.org/10.5281/zenodo.1228142)
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ProgramComputer/voxceleb
|
ProgramComputer
| 2023-11-04T21:44:05Z | 28 | 6 |
[
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_categories:image-classification",
"task_categories:video-classification",
"size_categories:100K<n<1M",
"license:cc-by-4.0",
"arxiv:1706.08612",
"doi:10.57967/hf/0999",
"region:us"
] |
[
"automatic-speech-recognition",
"audio-classification",
"image-classification",
"video-classification"
] | 2023-08-17T18:57:37Z |
---
task_categories:
- automatic-speech-recognition
- audio-classification
- image-classification
- video-classification
size_categories:
- 100K<n<1M
license: cc-by-4.0
datasets:
- voxceleb
- voxceleb2
---
## Dataset Description
- **Homepage:** [VoxCeleb](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/)
This dataset includes both VoxCeleb and VoxCeleb2
# Multipart Zips
Already joined zips for convenience but these specified files are *NOT* part of the original datasets
vox2_mp4_1.zip - vox2_mp4_6.zip
vox2_aac_1.zip - vox2_aac_2.zip
# Joining Zip
```
cat vox1_dev* > vox1_dev_wav.zip
```
```
cat vox2_dev_aac* > vox2_aac.zip
```
```
cat vox2_dev_mp4* > vox2_mp4.zip
```
### Citation Information
```
@article{Nagrani19,
author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman",
title = "Voxceleb: Large-scale speaker verification in the wild",
journal = "Computer Science and Language",
year = "2019",
publisher = "Elsevier",
}
@inProceedings{Chung18b,
author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.",
title = "VoxCeleb2: Deep Speaker Recognition",
booktitle = "INTERSPEECH",
year = "2018",
}
@article{DBLP:journals/corr/NagraniCZ17,
author = {Arsha Nagrani and
Joon Son Chung and
Andrew Zisserman},
title = {VoxCeleb: a large-scale speaker identification dataset},
journal = {CoRR},
volume = {abs/1706.08612},
year = {2017},
url = {http://arxiv.org/abs/1706.08612},
eprinttype = {arXiv},
eprint = {1706.08612},
timestamp = {Mon, 13 Aug 2018 16:47:04 +0200},
biburl = {https://dblp.org/rec/journals/corr/NagraniCZ17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@ProgramComputer](https://github.com/ProgramComputer) for adding this dataset.
|
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] |
Isotonic/marketing_email_samples
|
Isotonic
| 2023-08-24T13:17:29Z | 28 | 0 |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-08-24T13:16:18Z |
---
license: cc-by-nc-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: product
dtype: string
- name: description
dtype: string
- name: marketing_email
dtype: string
splits:
- name: train
num_bytes: 549973.1724738675
num_examples: 487
- name: test
num_bytes: 98249.8275261324
num_examples: 87
download_size: 376029
dataset_size: 648223.0
---
|
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TrainingDataPro/ripe-strawberries-detection
|
TrainingDataPro
| 2023-09-26T08:38:14Z | 28 | 2 |
[
"task_categories:image-classification",
"task_categories:image-to-image",
"task_categories:object-detection",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"biology",
"region:us"
] |
[
"image-classification",
"image-to-image",
"object-detection"
] | 2023-09-08T09:29:07Z |
---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- image-to-image
- object-detection
tags:
- code
- biology
dataset_info:
features:
- name: id
dtype: int32
- name: name
dtype: string
- name: image
dtype: image
- name: mask
dtype: image
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: shapes
sequence:
- name: label
dtype:
class_label:
names:
'0': strawberry
- name: type
dtype: string
- name: points
sequence:
sequence: float32
- name: rotation
dtype: float32
- name: attributes
sequence:
- name: name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 127730244
num_examples: 40
download_size: 126412271
dataset_size: 127730244
---
# Ripe Strawberries Detection
The dataset consists of photos of strawberries for the identification and recognition of **ripe berries**.
The images are annotated with **bounding boxes** that accurately demarcate the location of the ripe strawberries within the image.
Each image in the dataset showcases a strawberry plantation, and includes a diverse range of *backgrounds, lighting conditions, and orientations*. The photos are captured from various *angles and distances*, providing a realistic representation of strawberries.
The dataset can be utilised for enabling advancements in *strawberry production, quality control, and greater precision in agricultural practices*.

# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ripe-strawberries-detection) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **images** - contains of original images of strawberries
- **boxes** - includes bounding box labeling for the original images
- **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes for ripe strawberries detection. For each point, the x and y coordinates are provided. Visibility of the ripe strawberry is also provided by the attribute **occluded** (0, 1).
# Example of XML file structure

# Strawberry Detection might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ripe-strawberries-detection) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
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sdadas/gpt-exams
|
sdadas
| 2023-09-09T12:06:12Z | 28 | 1 |
[
"task_categories:question-answering",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:pl",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"question-answering"
] | 2023-09-09T11:25:39Z |
---
language:
- pl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- question-answering
pretty_name: GPT-exams
dataset_info:
features:
- name: _id
dtype: int32
- name: question
dtype: string
- name: answer
dtype: string
- name: domain
dtype: string
splits:
- name: train
num_bytes: 17237681
num_examples: 8131
---
# GPT-exams
### Dataset summary
The dataset contains 8131 multi-domain question-answer pairs. It was created semi-automatically using the `gpt-3.5-turbo-0613` model available in the OpenAI API. The process of building the dataset was as follows:
1. We manually prepared a list of 409 university-level courses from various fields. For each course, we instructed the model with the prompt: "Wygeneruj 20 przykładowych pytań na egzamin z [nazwa przedmiotu]" (Generate 20 sample questions for the [course name] exam).
2. We then parsed the outputs of the model to extract individual questions and performed their deduplication.
3. In the next step, we requested the model to generate the answer to each of the collected questions. We used the following prompt: "Odpowiedz na następujące pytanie z dziedziny [nazwa przedmiotu]: [treść pytania]" (Answer the following question from [course name]: [question content]). Along with the prompt, we also sent the following system message: "Jesteś ekspertem w dziedzinie [nazwa przedmiotu]. Udzielasz specjalistycznych i wyczerpujących odpowiedzi na pytania." (You are an expert in [course name]. You provide knowledgeable and comprehensive answers to questions).
4. In the last step, we manually removed from the dataset the cases in which the model refused to answer the question. We searched for occurrences of phrases such as "model języka" (language model), "nie jestem" (I'm not), or "nie mogę" (I can't).
### Data Instances
Example instance:
```
{
"_id": 2338,
"domain": "wzorców projektowych w oprogramowaniu",
"question": "Co to jest dependency injection i jak może być wykorzystane w kontekście wzorców projektowych?",
"answer": "Dependency injection (DI) to technika wstrzykiwania zależności, która polega na dostarczaniu obiektowi (...)"
}
```
### Data Fields
- _id: record id
- question: question text
- answer: answer text
- domain: name of the course / field / domain
|
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HuggingFaceH4/lima_llama2
|
HuggingFaceH4
| 2023-09-17T04:03:38Z | 28 | 4 |
[
"region:us"
] | null | 2023-09-17T04:03:27Z |
---
dataset_info:
features:
- name: conversations
sequence: string
- name: source
dtype: string
- name: length
dtype: int64
- name: prompt_id
dtype: string
- name: prompt
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: source
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8806712
num_examples: 1000
- name: test
num_bytes: 188848
num_examples: 300
download_size: 5237615
dataset_size: 8995560
---
# Dataset Card for "lima_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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M-A-D/Mixed-Arabic-Dataset-Main
|
M-A-D
| 2023-10-06T17:56:33Z | 28 | 3 |
[
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:translation",
"task_categories:summarization",
"language:ar",
"region:us"
] |
[
"conversational",
"text-generation",
"text2text-generation",
"translation",
"summarization"
] | 2023-09-25T10:52:11Z |
---
language:
- ar
task_categories:
- conversational
- text-generation
- text2text-generation
- translation
- summarization
pretty_name: MAD
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: GenId
dtype: int64
- name: SubId
dtype: int64
- name: DatasetName
dtype: string
- name: DatasetLink
dtype: string
- name: Text
dtype: string
- name: MetaData
struct:
- name: AboutAuthor
dtype: string
- name: AboutBook
dtype: string
- name: Author
dtype: string
- name: AuthorName
dtype: string
- name: BookLink
dtype: string
- name: BookName
dtype: string
- name: ChapterLink
dtype: string
- name: ChapterName
dtype: string
- name: Tags
dtype: float64
- name: __index_level_0__
dtype: float64
- name: created_date
dtype: string
- name: deleted
dtype: bool
- name: detoxify
dtype: 'null'
- name: emojis
struct:
- name: count
sequence: int32
- name: name
sequence: string
- name: id
dtype: string
- name: labels
struct:
- name: count
sequence: int32
- name: name
sequence: string
- name: value
sequence: float64
- name: lang
dtype: string
- name: message_id
dtype: string
- name: message_tree_id
dtype: string
- name: model_name
dtype: 'null'
- name: parent_id
dtype: string
- name: query_id
dtype: string
- name: rank
dtype: float64
- name: review_count
dtype: float64
- name: review_result
dtype: bool
- name: role
dtype: string
- name: synthetic
dtype: bool
- name: title
dtype: string
- name: tree_state
dtype: string
- name: url
dtype: string
- name: user_id
dtype: string
- name: ConcatenatedText
dtype: int64
- name: __index_level_0__
dtype: float64
splits:
- name: train
num_bytes: 1990497610
num_examples: 131393
download_size: 790648134
dataset_size: 1990497610
---
# Dataset Card for "Mixed-Arabic-Dataset"
## Mixed Arabic Datasets (MAD)
The Mixed Arabic Datasets (MAD) project provides a comprehensive collection of diverse Arabic-language datasets, sourced from various repositories, platforms, and domains. These datasets cover a wide range of text types, including books, articles, Wikipedia content, stories, and more.
### MAD Repo vs. MAD Main
#### MAD Repo
- **Versatility**: In the MAD Repository (MAD Repo), datasets are made available in their original, native form. Researchers and practitioners can selectively download specific datasets that align with their specific interests or requirements.
- **Independent Access**: Each dataset is self-contained, enabling users to work with individual datasets independently, allowing for focused analyses and experiments.
#### MAD Main or simply MAD
- **Unified Dataframe**: MAD Main represents a harmonized and unified dataframe, incorporating all datasets from the MAD Repository. It provides a seamless and consolidated view of the entire MAD collection, making it convenient for comprehensive analyses and applications.
- **Holistic Perspective**: Researchers can access a broad spectrum of Arabic-language content within a single dataframe, promoting holistic exploration and insights across diverse text sources.
### Why MAD Main?
- **Efficiency**: Working with MAD Main streamlines the data acquisition process by consolidating multiple datasets into one structured dataframe. This is particularly beneficial for large-scale projects or studies requiring diverse data sources.
- **Interoperability**: With MAD Main, the datasets are integrated into a standardized format, enhancing interoperability and compatibility with a wide range of data processing and analysis tools.
- **Meta-Analysis**: Researchers can conduct comprehensive analyses, such as cross-domain studies, trend analyses, or comparative studies, by leveraging the combined richness of all MAD datasets.
### Getting Started
- To access individual datasets in their original form, refer to the MAD Repository ([Link to MAD Repo](https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Datasets-Repo)).
- For a unified view of all datasets, conveniently organized in a dataframe, you are here in the right place.
```python
from datasets import load_dataset
dataset = load_dataset("M-A-D/Mixed-Arabic-Dataset-Main")
```
### Join Us on Discord
For discussions, contributions, and community interactions, join us on Discord! [](https://discord.gg/2NpJ9JGm)
### How to Contribute
Want to contribute to the Mixed Arabic Datasets project? Follow our comprehensive guide on Google Colab for step-by-step instructions: [Contribution Guide](https://colab.research.google.com/drive/1w7_7lL6w7nM9DcDmTZe1Vfiwkio6SA-w?usp=sharing).
**Note**: If you'd like to test a contribution before submitting it, feel free to do so on the [MAD Test Dataset](https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Dataset-test).
## Citation
```
@dataset{
title = {Mixed Arabic Datasets (MAD)},
author = {MAD Community},
howpublished = {Dataset},
url = {https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Datasets-Repo},
year = {2023},
}
```
|
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] |
abdiharyadi/nafkhan_ft_dataset_with_id_amr
|
abdiharyadi
| 2023-10-13T00:19:59Z | 28 | 0 |
[
"region:us"
] | null | 2023-09-28T08:44:38Z |
---
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: en_amr
dtype: string
- name: id_amr
dtype: string
- name: en
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 106147279
num_examples: 92867
- name: validation
num_bytes: 2278476
num_examples: 1722
- name: test
num_bytes: 1866019
num_examples: 1371
download_size: 41233166
dataset_size: 110291774
---
# Dataset Card for "nafkhan_ft_dataset_with_id_amr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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