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stringlengths 25
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|---|---|---|---|---|---|---|
1e786e36e3f33b796b38275000cfb7469066f62c
|
# Constitution Multi Lang
A collection of multiple nation, constitutional legal documents, with their official language translation.
## Rationale behind this
This project aims to get the official translation pairs of various **non-english** constitutions, of various nations.
Due to the importance of such documents on a nation, it is expected that the translation pairs are of high quality.
Additional in many cases, official goverment documents are "copyright free", removing any legal issues in the training process.
This also provides an easy scalable way to get reliable translation pairs for AI training.
## Repo links
- Github: https://github.com/PicoCreator/constitution-multi-lang
- Huggingface: https://huggingface.co/datasets/picocreator/constitution-multi-lang
## How to contribute (a public contributor)
1) Obtain official copies, and/or links and place them within the respective country folder in `raw-copies`
2) Cleanup and convert raw copies, into language markdown pairs. Line number content must 1:1 match one another into the `cleaned` folder.
3) Submit a pull request - via github
## How to followup with a completed contribution
1) Validate the cleaned markdown pairs, and ensure they are 1:1 match with the official copies.
2) Convert into translation training pairs, on the "vocab", "section", and "document" level. Generate the .jsonl files into the `parsed` folder.
3) Split out some vocab and section pairs, for them to be used in validation dataset.
## Example
Canada
- has an english copy : https://laws-lois.justice.gc.ca/eng/const/FullText.html
- and a french copy : https://laws-lois.justice.gc.ca/fra/const/TexteComplet.html
The (incompleted) converted markdown pairs (for en/fr) would be:
- https://github.com/PicoCreator/constitution-multi-lang/blob/main/cleaned/canada/fr.md
- https://github.com/PicoCreator/constitution-multi-lang/blob/main/cleaned/canada/fr.md
|
picocreator/constitution-multi-lang
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-08T09:26:04+00:00
|
{"license": "apache-2.0"}
|
2023-05-08T12:40:37+00:00
|
54c2792eca6d7442c84e5d0e0a5c22755c0c10e3
|
# Dataset Card for "diffusion.7.control_net"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lansinuote/diffusion.7.control_net
|
[
"region:us"
] |
2023-05-08T09:38:23+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 453988831.0, "num_examples": 50000}], "download_size": 0, "dataset_size": 453988831.0}}
|
2023-05-09T04:27:32+00:00
|
af3aef997ba4a67673c25a0abf53a6ed4a1877db
|
yongchoooon/fire_aihub
|
[
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-05-08T10:55:22+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["other"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "task_ids": [], "pretty_name": "fire_aihub", "tags": []}
|
2023-05-10T05:27:47+00:00
|
|
a70cb81ca309151d6229c07a4cbee40a12441b15
|
ShailaZ/US_Bonds
|
[
"license:cc",
"region:us"
] |
2023-05-08T11:03:43+00:00
|
{"license": "cc"}
|
2023-05-08T11:04:35+00:00
|
|
efd1dad8841bc45dce9dc16c55d623793cad16f9
|
# Dataset Information
## Keywords
Hebrew, handwritten, letters
## Description
HDD_v0 consists of images of isolated Hebrew characters together with training and test sets subdivision.
The images were collected from hand-filled forms.
For more details, please refer to [1].
When using this dataset in research work, please cite [1].
[1] I. Rabaev, B. Kurar Barakat, A. Churkin and J. El-Sana. The HHD Dataset. The 17th International Conference on Frontiers in Handwriting Recognition, pp. 228-233, 2020.
## Technical Details
The dataset is divided into TRAIN and TEST set (folders), each one containing 27 subfolders.
Each subfolder contains the images of a letter from the alphabet (one subfolder for each letter of the alphabet).
Train set contains 3965 samples, test set contains 1134 samples.
|
sivan22/hebrew-handwritten-dataset
|
[
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:he",
"license:cc-by-3.0",
"region:us"
] |
2023-05-08T11:36:09+00:00
|
{"language": ["he"], "license": "cc-by-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": ",", "1": "\u05d0", "2": "\u05d1", "3": "\u05d2", "4": "\u05d3", "5": "\u05d4", "6": "\u05d5", "7": "\u05d6", "8": "\u05d7", "9": "\u05d8", "10": "\u05d9", "11": "\u05da", "12": "\u05db", "13": "\u05dc", "14": "\u05dd", "15": "\u05de", "16": "\u05df", "17": "\u05e0", "18": "\u05e1", "19": "\u05e2", "20": "\u05e3", "21": "\u05e4", "22": "\u05e5", "23": "\u05e6", "24": "\u05e7", "25": "\u05e8", "26": "\u05e9", "27": "\u05ea"}}}}], "splits": [{"name": "train", "num_bytes": 29325896.28, "num_examples": 3965}, {"name": "test", "num_bytes": 9103495.104, "num_examples": 1128}], "download_size": 42332499, "dataset_size": 38429391.384}}
|
2023-05-08T18:57:55+00:00
|
34793cb8bc138ac9e3a475a2d35e90a536ecdf1c
|
Data for the Findings of ACL 2021 paper "CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation"
[GitHub repo](https://github.com/chujiezheng/CoMAE). [Original paper](https://arxiv.org/abs/2105.08316).
```bib
@inproceedings{zheng-etal-2021-comae,
title = "CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation",
author = "Zheng, Chujie and
Liu, Yong and
Chen, Wei and
Leng, Yongcai and
Huang, Minlie",
booktitle = "Findings of ACL 2021",
year = "2021"
}
```
|
chujiezheng/CoMAE
|
[
"language:en",
"license:apache-2.0",
"arxiv:2105.08316",
"region:us"
] |
2023-05-08T12:20:30+00:00
|
{"language": ["en"], "license": "apache-2.0"}
|
2023-05-08T12:24:15+00:00
|
a799ac39a2db7303ba44b7d556219e3c9143ce6e
|
Embedding similarity calculation files for the ACL 2021 paper "Towards Emotional Support Dialog Systems"
[GitHub repo](https://github.com/thu-coai/Emotional-Support-Conversation). [Original paper](https://arxiv.org/abs/2106.01144).
```bib
@inproceedings{liu-etal-2021-towards,
title={Towards Emotional Support Dialog Systems},
author={Liu, Siyang and
Zheng, Chujie and
Demasi, Orianna and
Sabour, Sahand and
Li, Yu and
Yu, Zhou and
Jiang, Yong and
Huang, Minlie},
booktitle={ACL},
year={2021}
}
```
|
chujiezheng/glove_embedding
|
[
"language:en",
"license:apache-2.0",
"arxiv:2106.01144",
"region:us"
] |
2023-05-08T12:28:53+00:00
|
{"language": ["en"], "license": "apache-2.0"}
|
2023-05-08T13:18:23+00:00
|
6ad3e109b3b9d52a28541c49b8aa175abe1fef97
|
# 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.
|
tomaarsen/conll2003
|
[
"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"
] |
2023-05-08T12:33:26+00:00
|
{"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"}]}]}
|
2023-05-08T12:34:35+00:00
|
484e8ea36929e800e26e8f21f78c050500648c19
|
Wizard-of-Wikipedia data for the Findings of EMNLP 2020 paper "Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation"
[GitHub repo](https://github.com/chujiezheng/DiffKS). [Original paper](https://arxiv.org/abs/2009.09378).
```bib
@inproceedings{zheng-etal-2020-diffks,
title="{D}ifference-aware Knowledge Selection for Knowledge-grounded Conversation Generation",
author="Zheng, Chujie and
Cao, Yunbo and
Jiang, Daxin and
Huang, Minlie",
booktitle="Findings of EMNLP",
year="2020"
}
```
|
chujiezheng/wizard_of_wikipedia
|
[
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2009.09378",
"region:us"
] |
2023-05-08T12:35:40+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0"}
|
2023-05-08T14:05:32+00:00
|
c2ba3bf82ff975ac02b707e7cd1d70ec22041828
|
translate by @Nekofoxtweet (me)
twitter source from @RindouMikoto
|
Nekofox/ja-zh-twitter-translate
|
[
"task_categories:translation",
"size_categories:n<1K",
"language:zh",
"language:ja",
"license:mit",
"region:us"
] |
2023-05-08T12:49:39+00:00
|
{"language": ["zh", "ja"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["translation"]}
|
2023-05-08T12:55:45+00:00
|
0fb12dd18bc53295d73a49c4b01f718441ae04e9
|
# Dataset Card for Tapir-Cleaned
This is a revised version of the DAISLab dataset of IFTTT rules, which has been thoroughly cleaned, scored, and adjusted for the purpose of instruction-tuning.
## Tapir Dataset Summary
Tapir is a subset of the larger DAISLab dataset, which comprises 242,480 recipes extracted from the IFTTT platform.
After a thorough cleaning process that involved the removal of redundant and inconsistent recipes, the refined dataset was condensed to include 116,862 high-quality recipes.
This curated set of instruction data is particularly useful for conducting instruction-tuning exercises for language models,
allowing them to more accurately follow instructions and achieve superior performance.
The last version of Tapir includes a correlation score that helps to identify the most appropriate description-rule pairs for instruction tuning.
Description-rule pairs with a score greater than 0.75 are deemed good enough and are prioritized for further analysis and tuning.
### Supported Tasks and Leaderboards
The Tapir dataset designed for instruction training pretrained language models
### Languages
The data in Tapir are mainly in English (BCP-47 en).
# Dataset Structure
### Data Instances
```json
{
"instruction":"From the description of a rule: identify the 'trigger', identify the 'action', write a IF 'trigger' THEN 'action' rule.",
"input":"If lostphone is texted to my phone the volume will turn up to 100 so I can find it.",
"output":"IF Android SMS New SMS received matches search THEN Android Device Set ringtone volume",
"score":"0.804322",
"text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nFrom the description of a rule: identify the 'trigger', identify the 'action', write a IF 'trigger' THEN 'action' rule.\n\n### Input:\nIf lostphone is texted to my phone the volume will turn up to 100 so I can find it.\n\n### Response:\nIF Android SMS New SMS received matches search THEN Android Device Set ringtone volume",
}
```
### Data Fields
The data fields are as follows:
* `instruction`: describes the task the model should perform.
* `input`: context or input for the task. Each of the 116K input is unique.
* `output`: the answer taken from the original Tapir Dataset formatted as an IFTTT recipe.
* `score`: the correlation score obtained via BertForNextSentencePrediction
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors of Alpaca for fine-tuning their models.
### Data Splits
| | train |
|---------------|------:|
| tapir | 116862 |
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{tapir,
author = {Mattia Limone, Gaetano Cimino, Annunziata Elefante},
title = {TAPIR: Trigger Action Platform for Information Retrieval},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MattiaLimone/ifttt_recommendation_system}},
}
```
|
MattiaL/tapir-cleaned-116k
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"region:us"
] |
2023-05-08T13:11:40+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "Tapir-Cleaned", "tags": ["instruction-finetuning"]}
|
2023-05-09T06:59:44+00:00
|
5055e8e4704551a7c324abc4eff7d470ae559ba3
|
# Dataset Card for DIALOGSum Corpus
## Dataset Description
### Links
- **Homepage:** https://aclanthology.org/2021.findings-acl.449
- **Repository:** https://github.com/cylnlp/dialogsum
- **Paper:** https://aclanthology.org/2021.findings-acl.449
### Dataset Summary
DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics.
### Languages
Russian (translated from English by Google Translate).
## Dataset Structure
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- topic: human written topic/one liner of the dialogue.
- id: unique file id of an example.
### Data Splits
- train: 12460
- val: 500
- test: 1500
- holdout: 100 [Only 3 features: id, dialogue, topic]
## Dataset Creation
### Curation Rationale
In paper:
We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers.
Compared with previous datasets, dialogues from DialogSum have distinct characteristics:
Under rich real-life scenarios, including more diverse task-oriented scenarios;
Have clear communication patterns and intents, which is valuable to serve as summarization sources;
Have a reasonable length, which comforts the purpose of automatic summarization.
We ask annotators to summarize each dialogue based on the following criteria:
Convey the most salient information;
Be brief;
Preserve important named entities within the conversation;
Be written from an observer perspective;
Be written in formal language.
### Who are the source language producers?
linguists
### Who are the annotators?
language experts
## Licensing Information
MIT License
## Citation Information
```
@inproceedings{chen-etal-2021-dialogsum,
title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset",
author = "Chen, Yulong and
Liu, Yang and
Chen, Liang and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.449",
doi = "10.18653/v1/2021.findings-acl.449",
pages = "5062--5074",
```
## Contributions
Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.
|
d0rj/dialogsum-ru
|
[
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"annotations_creators:expert-generated",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:knkarthick/dialogsum",
"language:ru",
"license:mit",
"conversations-summarization",
"dialogue-summarization",
"region:us"
] |
2023-05-08T13:17:46+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["translated"], "language": ["ru"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["knkarthick/dialogsum"], "task_categories": ["summarization", "text2text-generation", "text-generation"], "task_ids": [], "pretty_name": "DIALOGSum Corpus (ru)", "tags": ["conversations-summarization", "dialogue-summarization"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "dialogue", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19115158, "num_examples": 12460}, {"name": "validation", "num_bytes": 746312, "num_examples": 500}, {"name": "test", "num_bytes": 2282379, "num_examples": 1500}], "download_size": 10144708, "dataset_size": 22143849}, "train-eval-index": [{"config": "samsum", "task": "summarization", "task_id": "summarization", "splits": {"eval_split": "test"}, "col_mapping": {"dialogue": "text", "summary": "target"}}]}
|
2023-05-13T05:27:30+00:00
|
b2c8b76b36045e8396e902fb3949cef462f2761b
|
mainlp/inconsistencies_companies
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-08T14:18:44+00:00
|
{"license": "cc-by-4.0"}
|
2023-05-08T14:22:13+00:00
|
|
ad2e798d76c7796008320ae5c988d039ec16bdf8
|
mainlp/inconsistencies_flights
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-08T14:20:47+00:00
|
{"license": "cc-by-4.0"}
|
2023-05-08T14:21:03+00:00
|
|
8e0c7d561440748847bcf296edfc5c4aba920f63
|
mainlp/inconsistencies_forex
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-08T14:23:50+00:00
|
{"license": "cc-by-4.0"}
|
2023-05-08T14:24:17+00:00
|
|
1f46876c8a761c4858e85fd95dbf4bedcb312ca0
|
mainlp/pervasive_imdb
|
[
"license:gpl-3.0",
"region:us"
] |
2023-05-08T14:30:32+00:00
|
{"license": "gpl-3.0"}
|
2023-05-08T14:30:54+00:00
|
|
75dbfcbb12ae1b608d1c564f177054c3885487c9
|
This repo is the unofficial FeTA-QA dataset from paper [FeTaQA: Free-form Table Question Answering](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00446/109273/FeTaQA-Free-form-Table-Question-Answering).
The original purpose to make it easier for users to download and use dataset. All the data is publicly avaliable on [their offical Github site](https://github.com/Yale-LILY/FeTaQA)
If there is anything wrong, please raise an issue in the community and I will fix it if I am available.
|
DongfuTingle/FeTaQA
|
[
"task_categories:table-question-answering",
"task_categories:table-to-text",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
2023-05-08T14:33:08+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["table-question-answering", "table-to-text", "question-answering"], "pretty_name": "fetaqa"}
|
2023-05-08T14:52:42+00:00
|
5bdc33e9bd7f22f8e97adac85ba3c563bb7e362f
|
# Dataset Card for "korquad_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hansollll/korquad_v1
|
[
"region:us"
] |
2023-05-08T14:33:40+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 65474804, "num_examples": 48325}, {"name": "test", "num_bytes": 16380895, "num_examples": 12082}], "download_size": 50475250, "dataset_size": 81855699}}
|
2023-05-08T14:34:44+00:00
|
405c14fdfa77a10a598b24d43d4e20d92e43cab9
|
# Dataset Card for multilingual tatoeba translations with ~3M entries (llama supported languages only).
### Dataset Summary
~3M entries. Just more user-friendly version that combines all of the entries of original dataset in a single file (llama supported languages only):
https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt
|
0x22almostEvil/tatoeba-mt-llama-only
|
[
"task_categories:translation",
"size_categories:1M<n<10M",
"language:en",
"language:ru",
"language:de",
"language:uk",
"language:sv",
"language:sr",
"language:sl",
"language:ro",
"language:pt",
"language:pl",
"language:nl",
"language:it",
"language:hu",
"language:hr",
"language:fr",
"language:es",
"language:da",
"language:cs",
"language:ca",
"language:bg",
"license:cc-by-2.0",
"tatoeba",
"Translation",
"region:us"
] |
2023-05-08T14:42:22+00:00
|
{"language": ["en", "ru", "de", "uk", "sv", "sr", "sl", "ro", "pt", "pl", "nl", "it", "hu", "hr", "fr", "es", "da", "cs", "ca", "bg"], "license": "cc-by-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["translation"], "pretty_name": "tatoeba-mt-llama-only", "tags": ["tatoeba", "Translation"]}
|
2023-05-10T08:14:37+00:00
|
1e34e41fe7d0cd0eeac00d7e53c1996ca544b942
|
paartha/so-forum
|
[
"region:us"
] |
2023-05-08T14:50:45+00:00
|
{}
|
2023-05-08T15:03:09+00:00
|
|
75a851952808e62d04ad247be1ab06b91dfacac6
|
There are two configs: `ann0` (default) and `ann1`. These correspond to the annotator ID whose annotations will be loaded.
**Important:** Annotations from annotator 1 only exist for the dev set so the training and test set will have no annotations.
It is up to the user to combine the annotations somehow.
|
matejklemen/akces_gec
|
[
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-05-08T15:44:16+00:00
|
{"license": "cc-by-nc-sa-4.0", "dataset_info": [{"config_name": "ann0", "features": [{"name": "src_tokens", "sequence": "string"}, {"name": "tgt_tokens", "sequence": "string"}, {"name": "corrections", "list": [{"name": "idx_src", "sequence": "int32"}, {"name": "idx_tgt", "sequence": "int32"}, {"name": "corr_types", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 11199287, "num_examples": 42210}, {"name": "validation", "num_bytes": 713686, "num_examples": 2485}, {"name": "test", "num_bytes": 741411, "num_examples": 2676}], "download_size": 3534547, "dataset_size": 12654384}, {"config_name": "ann1", "features": [{"name": "src_tokens", "sequence": "string"}, {"name": "tgt_tokens", "sequence": "string"}, {"name": "corrections", "list": [{"name": "idx_src", "sequence": "int32"}, {"name": "idx_tgt", "sequence": "int32"}, {"name": "corr_types", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 8124054, "num_examples": 42210}, {"name": "validation", "num_bytes": 618583, "num_examples": 2485}, {"name": "test", "num_bytes": 655536, "num_examples": 2676}], "download_size": 3534547, "dataset_size": 9398173}]}
|
2023-05-08T18:20:17+00:00
|
517874c170155d3f6810ad7c786be1b785b2dbb0
|
# Printed Photos Attacks
The dataset includes 3 different types of files of the real people: original selfies, original videos and videos of attacks with printed photos. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems.
# 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=printed_photos_attacks) to discuss your requirements, learn about the price and buy the dataset.
# Content
### The dataset contains of three folders:
- **live_selfie** contains the original selfies of people
- **live_video** includes original videos of people
- **attack** contains video of the attack with the original images from "live_selfie" folder
### File with the extension .csv
includes the following information for each media file:
- **live_selfie**: the link to access the original selfie
- **live_video**: the link to access the original video
- **attack**: the link to access the video of the attack with the printed photo
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=printed_photos_attacks) 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**
|
TrainingDataPro/printed_photos_attacks
|
[
"task_categories:image-to-image",
"task_categories:video-classification",
"language:en",
"license:cc-by-nd-4.0",
"code",
"finance",
"region:us"
] |
2023-05-08T16:02:27+00:00
|
{"language": ["en"], "license": "cc-by-nd-4.0", "task_categories": ["image-to-image", "video-classification"], "tags": ["code", "finance"]}
|
2023-09-14T15:49:56+00:00
|
2246d8f2e9b7db4dfa92aa760a80076dd834c76e
|
# Dataset Card for "VocalSound_audio_16k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
flozi00/VocalSound_audio_16k
|
[
"region:us"
] |
2023-05-08T17:25:29+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2809191838.0, "num_examples": 21024}], "download_size": 1854571426, "dataset_size": 2809191838.0}}
|
2023-05-08T18:50:23+00:00
|
2f1b92685fcf4f15480f9160be2a223777fa3fae
|
# Dataset Card for "pokemon_bulbapedia_desc_only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
matemato/pokemon_bulbapedia_desc_only
|
[
"region:us"
] |
2023-05-08T17:37:05+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 100674542.0, "num_examples": 721}], "download_size": 83888862, "dataset_size": 100674542.0}}
|
2023-05-08T17:38:57+00:00
|
2020ef59f23f15c54af15d0e29ab91553904326b
|
christinacdl/hate_speech_2_classes
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-08T17:37:28+00:00
|
{"license": "apache-2.0"}
|
2023-05-08T17:39:09+00:00
|
|
cfb9b6afbf8065eca1bde2bf706963ddbd0ddcc7
|
# Dataset Card for odia-qa-98K
## Dataset Description
- **Homepage: https://www.odiagenai.org/**
- **Repository: https://github.com/shantipriyap/OdiaGenAI**
- **Point of Contact: Shantipriya Parida, and Sambit Sekhar**
### Dataset Summary
### Supported Tasks and Leaderboards
Large Language Model (LLM)
### Languages
Odia
## Dataset Structure
JSON
### Data Fields
instruction (string)
english_instruction (string)
input (string)
english_input (string)
output (string)
english_output (string)
### Licensing Information
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
### Citation Information
If you find this repository useful, please consider giving 👏 and citing:
```
@misc{OdiaGenAI,
author = {Shantipriya Parida and Sambit Sekhar and Subhadarshi Panda and Soumendra Kumar Sahoo and Swateek Jena and Abhijeet Parida and Arghyadeep Sen and Satya Ranjan Dash and Deepak Kumar Pradhan},
title = {OdiaGenAI: Generative AI and LLM Initiative for the Odia Language},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}
```
### Contributions
- Shantipriya Parida
- Sambit Sekhar
|
OdiaGenAI/odia_context_qa_98k
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:or",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-05-08T17:59:53+00:00
|
{"language": ["or"], "license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "odia-qa-98K"}
|
2023-05-08T18:04:53+00:00
|
4339f5692616bb35fdc47e33f19f07778b4e7f5b
|
# Dataset Card for "folktables-acs-income"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
birkhoffg/folktables-acs-income
|
[
"task_categories:tabular-classification",
"size_categories:1M<n<10M",
"language:en",
"adult",
"region:us"
] |
2023-05-08T18:07:24+00:00
|
{"language": ["en"], "size_categories": ["1M<n<10M"], "task_categories": ["tabular-classification"], "dataset_info": {"features": [{"name": "AGEP", "dtype": "float64"}, {"name": "COW", "dtype": "float64"}, {"name": "SCHL", "dtype": "float64"}, {"name": "MAR", "dtype": "float64"}, {"name": "OCCP", "dtype": "float64"}, {"name": "POBP", "dtype": "float64"}, {"name": "RELP", "dtype": "float64"}, {"name": "WKHP", "dtype": "float64"}, {"name": "SEX", "dtype": "float64"}, {"name": "RAC1P", "dtype": "float64"}, {"name": "STATE", "dtype": "string"}, {"name": "YEAR", "dtype": "int64"}, {"name": "PINCP", "dtype": "float64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 808018860, "num_examples": 7345626}, {"name": "test", "num_bytes": 269339730, "num_examples": 2448543}], "download_size": 197308481, "dataset_size": 1077358590}, "tags": ["adult"]}
|
2023-05-08T18:31:11+00:00
|
b7bff466d04acee1c7bbd08566d26c1f66194025
|
# Dataset Card for "twitter_posts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ummagumm-a/twitter_posts
|
[
"region:us"
] |
2023-05-08T19:10:25+00:00
|
{"dataset_info": {"features": [{"name": "retweetCount", "dtype": "int64"}, {"name": "num_mentioned_users", "dtype": "int64"}, {"name": "lang", "dtype": "string"}, {"name": "num_outlinks", "dtype": "int64"}, {"name": "likeCount", "dtype": "int64"}, {"name": "num_hashtags", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "quoteCount", "dtype": "int64"}, {"name": "date", "dtype": "string"}, {"name": "user", "dtype": "string"}, {"name": "replyCount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 42654407, "num_examples": 153340}], "download_size": 23097728, "dataset_size": 42654407}}
|
2023-05-08T19:35:57+00:00
|
d0d131f936e283cd8b1f6af5ae5ec4e5aba80c4d
|
# Dataset Card for OdiEnCorp_translation_instructions_25k
## Dataset Description
- **Homepage: https://www.odiagenai.org/**
- **Repository: https://github.com/shantipriyap/OdiaGenAI**
- **Point of Contact: Shantipriya Parida, and Sambit Sekhar**
### Dataset Summary
This dataset is the English-to-Odia translation instruction set. The instruction set is built using the OdienCorp_1.0 English-Odia parallel dataset. The instruction set contains input, and output strings.
### Supported Tasks and Leaderboards
Large Language Model (LLM)
### Languages
Odia
## Dataset Structure
JSON
### Data Fields
instruction (string)
output (string)
### Licensing Information
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
### Citation Information
If you find this repository useful, please consider giving 👏 and citing:
```
@misc{OdiaGenAI,
author = {Shantipriya Parida and Sambit Sekhar and Subhadarshi Panda and Soumendra Kumar Sahoo and Swateek Jena and Abhijeet Parida and Arghyadeep Sen and Satya Ranjan Dash and Deepak Kumar Pradhan},
title = {OdiaGenAI: Generative AI and LLM Initiative for the Odia Language},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}
```
### Contributions
- Shantipriya Parida
- Sambit Sekhar
|
OdiaGenAI/OdiEnCorp_translation_instructions_25k
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:or",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-05-08T19:14:47+00:00
|
{"language": ["or"], "license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "OdiEnCorp_translation_instructions_25k"}
|
2023-05-10T02:33:29+00:00
|
f55056701f7e695a0334073164bcc782efb90c65
|
matejklemen/falko_merlin
|
[
"license:cc-by-sa-4.0",
"region:us"
] |
2023-05-08T19:30:48+00:00
|
{"license": "cc-by-sa-4.0", "dataset_info": {"features": [{"name": "src_tokens", "sequence": "string"}, {"name": "tgt_tokens", "sequence": "string"}, {"name": "corrections", "list": [{"name": "idx_src", "sequence": "int32"}, {"name": "idx_tgt", "sequence": "int32"}, {"name": "corr_type", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 6981243, "num_examples": 19237}, {"name": "validation", "num_bytes": 902510, "num_examples": 2503}, {"name": "test", "num_bytes": 836757, "num_examples": 2337}], "download_size": 85667586, "dataset_size": 8720510}}
|
2023-05-08T19:56:31+00:00
|
|
363037b6bf3888eea3f3a63d1dc1c520b1c89049
|
# Dataset Card for "miniwob_plusplus_task_randomized_ccnet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LucasThil/miniwob_plusplus_task_randomized_ccnet
|
[
"region:us"
] |
2023-05-08T19:50:17+00:00
|
{"dataset_info": {"features": [{"name": "task", "dtype": "string"}, {"name": "history_episodes", "dtype": "string"}, {"name": "html_snippets", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "actions", "dtype": "string"}, {"name": "refs", "dtype": "string"}, {"name": "keydown_texts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 263829273, "num_examples": 14494}], "download_size": 37741436, "dataset_size": 263829273}}
|
2023-05-08T19:50:23+00:00
|
8e249d33bbba11f7af08932a60876c7a4088b715
|
ShamsAldeenAlburaihi/Economic_quantity_to_boy
|
[
"license:openrail",
"region:us"
] |
2023-05-08T20:12:23+00:00
|
{"license": "openrail"}
|
2023-05-08T20:12:23+00:00
|
|
38fd3268c41e20cbbe353e6960f41e4cf743796f
|
Modification of the cnn_dailymail dataset in Hugging Face. The main goal is to reproduce the results on BART.
References: https://github.com/facebookresearch/fairseq/issues/1401
Major changes:
1. remove the space in " ." in fix_missing_period.
2. remove "(CNN)" in article.
|
yuyang/bart_cnndm
|
[
"region:us"
] |
2023-05-08T21:12:05+00:00
|
{}
|
2023-05-08T21:12:43+00:00
|
a1c35c028f14401967fb9c749996f06627c270b2
|
# Dataset Card for "Hatefulmemes_test_google_flan_t5_small_mode_T_A_C_OCR_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_small_mode_T_A_C_OCR_rices_ns_1000
|
[
"region:us"
] |
2023-05-08T21:28:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 1009239, "num_examples": 1000}], "download_size": 196548, "dataset_size": 1009239}}
|
2023-05-08T21:28:03+00:00
|
e3115f0ae204e6bdff6897bd0d622cb2786e8363
|
# Dataset Card for "ios_icons_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
akadhim-ai/ios_icons_5
|
[
"region:us"
] |
2023-05-08T22:05:18+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 768688.0, "num_examples": 10}], "download_size": 769873, "dataset_size": 768688.0}}
|
2023-05-08T22:05:24+00:00
|
ec57603904b810d387b750adb56073721cb8ec2e
|
# Dataset Card for "model-evaluation-arena"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/model-evaluation-arena
|
[
"region:us"
] |
2023-05-08T22:54:15+00:00
|
{"dataset_info": {"features": [{"name": "user_state", "struct": [{"name": "botLabel", "dtype": "string"}, {"name": "bot_id", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "developerUid", "dtype": "string"}, {"name": "firstMessage", "dtype": "string"}, {"name": "imageUrl", "dtype": "string"}, {"name": "introduction", "dtype": "string"}, {"name": "memory", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "private", "dtype": "bool"}, {"name": "prompt", "dtype": "string"}, {"name": "sfw", "dtype": "bool"}, {"name": "userLabel", "dtype": "string"}]}, {"name": "vote", "dtype": "string"}, {"name": "model_tag_a", "dtype": "string"}, {"name": "model_tag_b", "dtype": "string"}, {"name": "conversation_a", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "conversation_b", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "is_anonymous", "dtype": "bool"}, {"name": "timestamp", "dtype": "float64"}, {"name": "bot_id", "dtype": "string"}, {"name": "model_a", "dtype": "string"}, {"name": "model_b", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7712, "num_examples": 3}], "download_size": 33224, "dataset_size": 7712}}
|
2023-05-09T01:25:07+00:00
|
8aeb5a8f48fb9c393bc9d6b981d661b9439def51
|
arandu/arandu
|
[
"license:cc",
"region:us"
] |
2023-05-08T23:38:25+00:00
|
{"license": "cc"}
|
2023-05-08T23:38:25+00:00
|
|
89c480f509b0ad8c38e847b1b895d6c295d94750
|
chailey/EthTransactions_V1
|
[
"license:openrail",
"region:us"
] |
2023-05-08T23:52:27+00:00
|
{"license": "openrail"}
|
2023-05-08T23:52:27+00:00
|
|
3a5935ade64980bcdc9e5d8c8bd053c2684d44d1
|
# Dataset Card for "KoInstruct-QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GSON-backup/KoInstruct-Base
|
[
"region:us"
] |
2023-05-08T23:55:54+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "template", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 279249821, "num_examples": 50169}], "download_size": 128982824, "dataset_size": 279249821}}
|
2023-05-08T23:56:03+00:00
|
5b541030fd6dc9b23072727679034153dd56fd2d
|
# Dataset Card for "KoInstruct-QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GSON-backup/KoInstruct-QA
|
[
"region:us"
] |
2023-05-08T23:58:26+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "template", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 237493038, "num_examples": 50276}], "download_size": 113325801, "dataset_size": 237493038}}
|
2023-05-08T23:58:34+00:00
|
8d5c47e30f9b4b84b96a496ada974fe9109f3127
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xxl_mode_T_A_C_Q_rices_ns_5046"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xxl_mode_T_A_C_Q_rices_ns_5046
|
[
"region:us"
] |
2023-05-09T00:10:43+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 5458212, "num_examples": 5046}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 5814379, "num_examples": 5046}], "download_size": 2675960, "dataset_size": 11272591}}
|
2023-05-09T02:00:34+00:00
|
8eef5d31b12807e48226c77090541d8146ce770a
|
# MODIS Water Lake Powell Toy Dataset
### Dataset Summary
Tabular dataset comprised of MODIS surface reflectance bands along with calculated indices and a label (water/not-water)
## Dataset Structure
### Data Fields
- `water`: Label, water or not-water (binary)
- `sur_refl_b01_1`: MODIS surface reflection band 1 (-100, 16000)
- `sur_refl_b02_1`: MODIS surface reflection band 2 (-100, 16000)
- `sur_refl_b03_1`: MODIS surface reflection band 3 (-100, 16000)
- `sur_refl_b04_1`: MODIS surface reflection band 4 (-100, 16000)
- `sur_refl_b05_1`: MODIS surface reflection band 5 (-100, 16000)
- `sur_refl_b06_1`: MODIS surface reflection band 6 (-100, 16000)
- `sur_refl_b07_1`: MODIS surface reflection band 7 (-100, 16000)
- `ndvi`: Normalized differential vegetation index (-20000, 20000)
- `ndwi1`: Normalized differential water index 1 (-20000, 20000)
- `ndwi2`: Normalized differential water index 2 (-20000, 20000)
### Data Splits
Train and test split. Test is 200 rows, train is 800.
## Dataset Creation
## Source Data
[MODIS MOD44W](https://lpdaac.usgs.gov/products/mod44wv006/)
[MODIS MOD09GA](https://lpdaac.usgs.gov/products/mod09gav006/)
[MODIS MOD09GQ](https://lpdaac.usgs.gov/products/mod09gqv006/)
## Annotation process
Labels were created by using the MOD44W C6 product to designate pixels in MODIS surface reflectance products as land or water.
|
wateryhcho/modis-lake-powell-toy-dataset
|
[
"size_categories:n<1K",
"license:apache-2.0",
"region:us"
] |
2023-05-09T00:35:51+00:00
|
{"license": "apache-2.0", "size_categories": ["n<1K"]}
|
2023-05-09T00:59:21+00:00
|
c98e0f9b2c86f6378ed7882faca6f6252736d42e
|
# Dataset Card for "atomic2020-comet-origin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Estwld/atomic2020-comet-origin
|
[
"region:us"
] |
2023-05-09T00:41:14+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 64203342, "num_examples": 1008254}, {"name": "test", "num_bytes": 9404615, "num_examples": 143736}, {"name": "validation", "num_bytes": 6314227, "num_examples": 94614}], "download_size": 21711502, "dataset_size": 79922184}}
|
2023-05-09T00:45:03+00:00
|
a1c1cd6bb789b0fc02c614730e07fb4826027519
|
# Dataset Card for "pali-english-devanagari"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
buddhist-nlp/pali-english-devanagari
|
[
"region:us"
] |
2023-05-09T00:41:22+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "target_text", "dtype": "string"}, {"name": "file_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49124865, "num_examples": 132151}, {"name": "validation", "num_bytes": 2927142, "num_examples": 7832}, {"name": "test", "num_bytes": 2906649, "num_examples": 7832}, {"name": "test_500", "num_bytes": 176101, "num_examples": 499}, {"name": "validation_500", "num_bytes": 190038, "num_examples": 499}], "download_size": 25233152, "dataset_size": 55324795}}
|
2023-05-09T00:41:35+00:00
|
89ce58ec371f8af2b6f6a23eb336d36f2c9d3a73
|
# Dataset Card for "summary-auto-train-small-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
huizhoucheng/summary-auto-train-small-2
|
[
"region:us"
] |
2023-05-09T00:50:27+00:00
|
{"dataset_info": {"features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10060340, "num_examples": 2571}, {"name": "validation", "num_bytes": 485669, "num_examples": 133}, {"name": "test", "num_bytes": 399200, "num_examples": 114}], "download_size": 6609537, "dataset_size": 10945209}}
|
2023-05-09T00:50:30+00:00
|
e30fc6ca11d53e40897702ee8a394bb682d059f3
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_5046"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_5046
|
[
"region:us"
] |
2023-05-09T01:01:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 49493860, "num_examples": 5046}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 49672229, "num_examples": 5046}], "download_size": 16604017, "dataset_size": 99166089}}
|
2023-05-09T02:51:07+00:00
|
a2b2f6f8a2ff9c3a434071ff35c6c9b712edaaf7
|
# Dataset Card for "mtg-data"
### Dataset Summary
The "mtg-data" dataset is a collection of prompts and responses related to Magic: The Gathering (MTG), a popular collectible card game.
The dataset contains various types of question and answer pairs, including official rulings as responses and corresponding questions generated by GPT-3.5,
Q&A data scraped from the web, glossary terms alongside their descriptions, and official rules formatted into Q/A pairs.
This dataset is designed to facilitate the development and research of AI models focused on understanding game dynamics, card interactions,
and providing judge rulings.
|
nelsntk/mtg-data
|
[
"size_categories:10K<n<100K",
"language:en",
"region:us"
] |
2023-05-09T01:02:57+00:00
|
{"language": "en", "size_categories": ["10K<n<100K"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "rulings", "num_bytes": 8005972, "num_examples": 26718}, {"name": "scraped", "num_bytes": 11066974, "num_examples": 14255}, {"name": "rules_qna", "num_bytes": 150903, "num_examples": 493}, {"name": "glossary", "num_bytes": 110922, "num_examples": 654}, {"name": "rules", "num_bytes": 1006878, "num_examples": 2840}], "download_size": 10395273, "dataset_size": 20341649}}
|
2023-07-28T05:20:17+00:00
|
5aec1c209997ddb6c14ba9e5f1e3ed44f1b9e0f1
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xl_mode_T_A_C_Q_rices_ns_5046"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xl_mode_T_A_C_Q_rices_ns_5046
|
[
"region:us"
] |
2023-05-09T01:11:43+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 5449076, "num_examples": 5046}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 5805316, "num_examples": 5046}], "download_size": 2663600, "dataset_size": 11254392}}
|
2023-05-09T03:01:33+00:00
|
5d08e9754048c8a185db38982237f5ec0f2505d8
|
# Dataset Card for "wikiartfaces"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/wikiartfaces
|
[
"region:us"
] |
2023-05-09T01:21:17+00:00
|
{"dataset_info": {"features": [{"name": "img", "dtype": "image"}, {"name": "style", "dtype": "string"}, {"name": "split", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 65503646.137, "num_examples": 32097}], "download_size": 49391264, "dataset_size": 65503646.137}}
|
2023-05-09T01:21:47+00:00
|
6333e5dc5d9cf898fb9857d3c8230496b9a6661e
|
This dataset is the Open Assistant dataset https://huggingface.co/datasets/OpenAssistant/oasst1, and formatted to be easier to convert to whatever finetune data format you want, removes 75 instances of alignment and 81 dupes.
oasst_clean_format_dedupe.py was first ran on 2023-04-12_oasst_all.trees.jsonl from OpenAssistant/oasst1
Inspired by https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
Credit to anon8231489123 for the cleanup script that I adapted to wizardlm_clean.py, I then took this script and adapted it to oasst_clean_data_format.py
I converted to trees so each object in the output has a messages list, each message has a role and content. Each starting prompt in the oasst dataset will appear in the output n times where n is the number of replies the prompt had (unless it had a duplicate).
so if the oasst tree looked like
```
user: aaa
assistant: bbb
user: ccc
assistant: ddd
assistant: eee
user: fff
assistant: ggg
user: hhh
assistant: iii
```
then the output would look like
```
{
messages: [
{ "role": "user", "content": "aaa" },
{ "role": "model", "content": "bbb" },
{ "role": "user", "content": "ccc" },
{ "role": "model", "content": "ddd" }
]
}
{
messages: [
{ "role": "user", "content": "aaa" },
{ "role": "model", "content": "eee" }
// if tree ends with a user msg then it is not included in the output, this can be changed by passing --save-user-ends to the script
]
}
{
messages: [
{ "role": "user", "content": "aaa" },
{ "role": "model", "content": "ggg" },
{ "role": "user", "content": "hhh" },
{ "role": "model", "content": "iii" }
]
}
```
|
ewof/oasst-convo-unfiltered-deduped
|
[
"region:us"
] |
2023-05-09T01:29:11+00:00
|
{}
|
2023-05-13T02:55:04+00:00
|
27791fa1029a4b5aa3318570ab972a26db9a4517
|
# Dataset Card for "art_bar_renn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/art_bar_renn
|
[
"region:us"
] |
2023-05-09T01:29:43+00:00
|
{"dataset_info": {"features": [{"name": "img", "dtype": "image"}, {"name": "style", "dtype": "string"}, {"name": "split", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12758150.608, "num_examples": 8688}], "download_size": 8156798, "dataset_size": 12758150.608}}
|
2023-05-09T03:39:07+00:00
|
69c56c53d76e2ca9c9a41078e64227ecee478e90
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_5046"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_5046
|
[
"region:us"
] |
2023-05-09T01:44:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 49486823, "num_examples": 5046}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 49665385, "num_examples": 5046}], "download_size": 16591454, "dataset_size": 99152208}}
|
2023-05-09T03:34:39+00:00
|
f5ca5d7242ebaf3dc217bf80c46e6811c879a4e4
|
# h2oGPT Data Card
## Summary
H2O.ai's `h2ogpt-oig-oasst1-instruct-cleaned-v3` is an open-source instruct-type dataset for fine-tuning of large language models, licensed for commercial use.
- Number of rows: `269406`
- Number of columns: `4`
- Column names: `['input', 'source', 'prompt_type', 'id']`
## Source
- [Original LAION OIG Dataset](https://github.com/LAION-AI/Open-Instruction-Generalist)
- [LAION OIG data detoxed and filtered down by scripts in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/main/FINETUNE.md#high-quality-oig-based-instruct-data)
- [Original Open Assistant data in tree structure](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [This flattened dataset created by script in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/6728938a262d3eb5e8db1f252bbcd7de838da452/create_data.py#L1415)
|
h2oai/h2ogpt-oig-oasst1-instruct-cleaned-v3
|
[
"language:en",
"license:apache-2.0",
"gpt",
"llm",
"large language model",
"open-source",
"region:us"
] |
2023-05-09T02:08:38+00:00
|
{"language": ["en"], "license": "apache-2.0", "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico", "tags": ["gpt", "llm", "large language model", "open-source"]}
|
2023-05-09T03:58:54+00:00
|
edd1c13a585f8fe05783178b6109b79f64e07c55
|
# h2oGPT Data Card
## Summary
H2O.ai's `openassistant_oasst1_h2ogpt_graded` is an open-source instruct-type dataset for fine-tuning of large language models, licensed for commercial use.
- Number of rows: `30368`
- Number of columns: `5`
- Column names: `['input', 'source', 'prompt_type', 'grade_deberta', 'id']`
## Source
- [Original Open Assistant data in tree structure](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [This flattened dataset created by script in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/d1f8ce975a46056d41135d126dd33de8499aa26e/create_data.py#L1259)
|
h2oai/openassistant_oasst1_h2ogpt_graded
|
[
"language:en",
"license:apache-2.0",
"gpt",
"llm",
"large language model",
"open-source",
"region:us"
] |
2023-05-09T02:10:07+00:00
|
{"language": ["en"], "license": "apache-2.0", "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico", "tags": ["gpt", "llm", "large language model", "open-source"]}
|
2023-05-09T02:22:25+00:00
|
207bf63b3bdf2abd146f11ffdf6a2f6b3b64ab1d
|
# h2oGPT Data Card
## Summary
H2O.ai's `h2ogpt-fortune2000-personalized` is an open-source instruct-type dataset for fine-tuning of large language models, licensed for commercial use.
- Number of rows: `11363`
- Number of columns: `4`
- Column names: `['input', 'prompt_type', 'source', 'id']`
## Source
- [Fortune 2000 companies from Wikipedia](https://github.com/h2oai/h2ogpt/blob/b1ea74c0088884ebff97f1ccddbfb3f393e29e44/create_data.py#L1743)
|
h2oai/h2ogpt-fortune2000-personalized
|
[
"language:en",
"license:apache-2.0",
"gpt",
"llm",
"large language model",
"open-source",
"region:us"
] |
2023-05-09T04:06:47+00:00
|
{"language": ["en"], "license": "apache-2.0", "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico", "tags": ["gpt", "llm", "large language model", "open-source"]}
|
2023-05-09T04:08:02+00:00
|
c78242931a8cea766b27f5ea57c30fda7b2a0831
|
# Dataset Card for "artfaces_padded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/artfaces_padded
|
[
"region:us"
] |
2023-05-09T04:36:09+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43730727.5, "num_examples": 2332}], "download_size": 43648808, "dataset_size": 43730727.5}}
|
2023-05-09T04:39:40+00:00
|
d975b088fc07773b2464999e90a3db90a22f5208
|
# Dataset Card for "kids_phoneme_md"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mirfan899/kids_phoneme_md
|
[
"language:en",
"license:bsd",
"region:us"
] |
2023-05-09T05:03:20+00:00
|
{"language": ["en"], "license": "bsd", "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "phonetic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 707377196.786, "num_examples": 2999}], "download_size": 691898690, "dataset_size": 707377196.786}}
|
2023-06-16T11:28:41+00:00
|
112ac27228d17cb3e045788ef1627483c663382b
|
# Dataset Card for "kids_phoneme_sm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mirfan899/kids_phoneme_sm
|
[
"region:us"
] |
2023-05-09T05:07:49+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "phonetic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 353563753.786, "num_examples": 1499}], "download_size": 354119911, "dataset_size": 353563753.786}}
|
2023-05-17T03:30:17+00:00
|
decd9cb162f15955e8091206ae74439269c72c77
|
# Dataset Card for "Dutch_MLM_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_1
|
[
"region:us"
] |
2023-05-09T05:53:21+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 58518489, "num_examples": 25000}], "download_size": 34788019, "dataset_size": 58518489}}
|
2023-05-09T05:53:27+00:00
|
7f11f3e95da2326dd8611253286b79b4493e78c4
|
# Dataset Card for "Dutch_MLM_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_2
|
[
"region:us"
] |
2023-05-09T05:59:06+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57141220, "num_examples": 25000}], "download_size": 34091523, "dataset_size": 57141220}}
|
2023-05-09T05:59:09+00:00
|
d03cacf13ed7e97bb496474471a8df0fc50864bd
|
# Dataset Card for "Dutch_MLM_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_3
|
[
"region:us"
] |
2023-05-09T05:59:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 54501690, "num_examples": 25000}], "download_size": 32424106, "dataset_size": 54501690}}
|
2023-05-09T05:59:12+00:00
|
d790bb11ed54486560bd7f8e8a587ff062c4951b
|
# Dataset Card for "Dutch_MLM_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_4
|
[
"region:us"
] |
2023-05-09T05:59:12+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55961171, "num_examples": 25000}], "download_size": 33158194, "dataset_size": 55961171}}
|
2023-05-09T05:59:15+00:00
|
0a07f0ac4d618e2c09a58c4ae501001d59708b4c
|
# Dataset Card for "Dutch_MLM_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_5
|
[
"region:us"
] |
2023-05-09T05:59:15+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 54072475, "num_examples": 25000}], "download_size": 32212850, "dataset_size": 54072475}}
|
2023-05-09T05:59:18+00:00
|
11a63638f6d66def4121c52ad8b08ce574e09a8e
|
# Dataset Card for "Dutch_MLM_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_6
|
[
"region:us"
] |
2023-05-09T05:59:18+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51203473, "num_examples": 25000}], "download_size": 30609231, "dataset_size": 51203473}}
|
2023-05-09T05:59:21+00:00
|
6188604f49489ff681d43af02d14fd61954c94ce
|
# Dataset Card for "Dutch_MLM_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_7
|
[
"region:us"
] |
2023-05-09T05:59:22+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 52391081, "num_examples": 25000}], "download_size": 31155158, "dataset_size": 52391081}}
|
2023-05-09T05:59:24+00:00
|
1324466c4a3fc17dc1d8663c96f868a19ab88f16
|
# Dataset Card for "Dutch_MLM_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_8
|
[
"region:us"
] |
2023-05-09T05:59:25+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55307428, "num_examples": 25000}], "download_size": 33148580, "dataset_size": 55307428}}
|
2023-05-09T05:59:27+00:00
|
6915db78e5460315e8336115535e8d544e751377
|
# Dataset Card for "Dutch_MLM_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_9
|
[
"region:us"
] |
2023-05-09T05:59:27+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 56283820, "num_examples": 25000}], "download_size": 33611577, "dataset_size": 56283820}}
|
2023-05-09T05:59:30+00:00
|
52bdd19408a1895feefffacc161f3448307a1367
|
# Dataset Card for "Dutch_MLM_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_10
|
[
"region:us"
] |
2023-05-09T05:59:30+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53484104, "num_examples": 25000}], "download_size": 32103697, "dataset_size": 53484104}}
|
2023-05-09T05:59:33+00:00
|
e736664f6b86d4104abf51d6e80ea95c1a72afea
|
# Dataset Card for "Dutch_MLM_11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Dutch_MLM_11
|
[
"region:us"
] |
2023-05-09T05:59:33+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55829949, "num_examples": 25000}], "download_size": 33209982, "dataset_size": 55829949}}
|
2023-05-09T05:59:36+00:00
|
16f01c50c624f824fc523e4cb63dbb39783f9eaa
|
Starlee822/dataset1
|
[
"license:openrail",
"region:us"
] |
2023-05-09T06:16:27+00:00
|
{"license": "openrail"}
|
2023-05-09T06:16:27+00:00
|
|
21ff8b5f6474e6e0ffe7ed3549a00e5f7eaa678a
|
# Dataset Card for "mmlu-abstract_algebra-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-abstract_algebra-neg-answer
|
[
"region:us"
] |
2023-05-09T06:24:57+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 20641, "num_examples": 100}], "download_size": 10947, "dataset_size": 20641}}
|
2023-05-15T04:20:32+00:00
|
924794c83c2be1484bc8d6068816d6086d49212f
|
# Dataset Card for "mmlu-anatomy-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-anatomy-neg-answer
|
[
"region:us"
] |
2023-05-09T06:25:41+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 38830, "num_examples": 135}], "download_size": 24408, "dataset_size": 38830}}
|
2023-05-15T04:21:55+00:00
|
bdb743e30deac526b8a3ad0bda6128816869f3c2
|
# Dataset Card for "mmlu-astronomy-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-astronomy-neg-answer
|
[
"region:us"
] |
2023-05-09T06:26:16+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 55722, "num_examples": 152}], "download_size": 34163, "dataset_size": 55722}}
|
2023-05-15T04:22:53+00:00
|
3bdf242d8950c5a1d94e0abaf4f6184f6afd3ce1
|
# Dataset Card for "mmlu-business_ethics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-business_ethics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:26:48+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 37485, "num_examples": 100}], "download_size": 24992, "dataset_size": 37485}}
|
2023-05-15T04:23:49+00:00
|
9ea124009d211fb778e3d277b5a1d7aaf456bbf2
|
# Dataset Card for "mmlu-clinical_knowledge-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-clinical_knowledge-neg-answer
|
[
"region:us"
] |
2023-05-09T06:27:46+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 74962, "num_examples": 265}], "download_size": 49647, "dataset_size": 74962}}
|
2023-05-15T04:25:32+00:00
|
0f96f61d91a88b13fb5125766581454fb01efa5d
|
# Dataset Card for "mmlu-college_biology-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-college_biology-neg-answer
|
[
"region:us"
] |
2023-05-09T06:28:20+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 56357, "num_examples": 144}], "download_size": 37571, "dataset_size": 56357}}
|
2023-05-15T04:26:35+00:00
|
0e092b0919e5a473c48e14eae5edb88ebaeec8a7
|
# Dataset Card for "mmlu-college_chemistry-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-college_chemistry-neg-answer
|
[
"region:us"
] |
2023-05-09T06:28:50+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 27423, "num_examples": 100}], "download_size": 20256, "dataset_size": 27423}}
|
2023-05-15T04:27:37+00:00
|
ecf4502600291032a369d9b5f245636e7065c0a8
|
# Dataset Card for "mmlu-college_computer_science-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-college_computer_science-neg-answer
|
[
"region:us"
] |
2023-05-09T06:29:17+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 46024, "num_examples": 100}], "download_size": 30765, "dataset_size": 46024}}
|
2023-05-15T04:28:23+00:00
|
f97ec050f1be6b2dc1e4672857fc15c795311cd9
|
# Dataset Card for "mmlu-college_mathematics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-college_mathematics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:29:44+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 26817, "num_examples": 100}], "download_size": 18378, "dataset_size": 26817}}
|
2023-05-15T04:29:12+00:00
|
41dedf5fd62a1342778fb144fb9ed0c6634a3a5a
|
# Dataset Card for "mmlu-college_medicine-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-college_medicine-neg-answer
|
[
"region:us"
] |
2023-05-09T06:30:25+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 89807, "num_examples": 173}], "download_size": 48334, "dataset_size": 89807}}
|
2023-05-15T04:30:26+00:00
|
0f5f55356b66fddf7f9ac64ed38fe586c2477e33
|
# Dataset Card for "mmlu-college_physics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-college_physics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:30:51+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 32519, "num_examples": 102}], "download_size": 20426, "dataset_size": 32519}}
|
2023-05-15T04:31:13+00:00
|
6961fc7bd55f64c4d6d8dc53ffba1c154fa1ccc0
|
# Dataset Card for "mmlu-computer_security-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-computer_security-neg-answer
|
[
"region:us"
] |
2023-05-09T06:31:15+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 31239, "num_examples": 100}], "download_size": 22397, "dataset_size": 31239}}
|
2023-05-15T04:32:02+00:00
|
a2833071f292add657c567030bf22bed40563cc6
|
# Dataset Card for "mmlu-conceptual_physics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-conceptual_physics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:32:01+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 46275, "num_examples": 235}], "download_size": 29112, "dataset_size": 46275}}
|
2023-05-15T04:33:29+00:00
|
ee045bd6a8701a89767f587e6d198296c8e64923
|
# Dataset Card for "mmlu-econometrics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-econometrics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:32:32+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 51553, "num_examples": 114}], "download_size": 27844, "dataset_size": 51553}}
|
2023-05-15T04:34:25+00:00
|
d1ac2308e39fe5d2a04efc7ab5c18cf79b2385ba
|
# Dataset Card for "cl-signal_processing_attacks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/cl-signal_processing_attacks_mini_whisper_librispeech
|
[
"region:us"
] |
2023-05-09T06:32:42+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "attacked", "1": "original"}}}}], "splits": [{"name": "train", "num_bytes": 47447367.0, "num_examples": 120}, {"name": "test", "num_bytes": 24062500.0, "num_examples": 60}], "download_size": 66557876, "dataset_size": 71509867.0}}
|
2023-05-09T06:33:13+00:00
|
bc9d341db391e8a298121c828e0916f4544d7ba4
|
# Dataset Card for "mmlu-electrical_engineering-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-electrical_engineering-neg-answer
|
[
"region:us"
] |
2023-05-09T06:33:08+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 28832, "num_examples": 145}], "download_size": 20564, "dataset_size": 28832}}
|
2023-05-15T04:35:32+00:00
|
ab3389435a8e276f960cfd2bc0e863ec67e8f3d8
|
# Dataset Card for "mmlu-elementary_mathematics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-elementary_mathematics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:34:29+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 76525, "num_examples": 378}], "download_size": 46293, "dataset_size": 76525}}
|
2023-05-15T04:37:57+00:00
|
7aaf730b3884a62caa4207b0bdad1cde1c6a848a
|
# Dataset Card for "mmlu-formal_logic-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-formal_logic-neg-answer
|
[
"region:us"
] |
2023-05-09T06:35:04+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 55219, "num_examples": 126}], "download_size": 24998, "dataset_size": 55219}}
|
2023-05-15T04:38:54+00:00
|
62d2e9d167d47226360649d78310e3b9b211fd06
|
# Dataset Card for "mmlu-global_facts-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-global_facts-neg-answer
|
[
"region:us"
] |
2023-05-09T06:35:26+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 19969, "num_examples": 100}], "download_size": 12966, "dataset_size": 19969}}
|
2023-05-15T04:39:31+00:00
|
cdae0cd97bc797f27689cd382de81ce84884facd
|
kwakhyok/high-quality-unsplash-tags
|
[
"license:mit",
"region:us"
] |
2023-05-09T06:36:00+00:00
|
{"license": "mit"}
|
2023-05-09T06:36:00+00:00
|
|
41ca5c668e3e7632ce3743398bf4fa9506fa3394
|
# Dataset Card for "mmlu-high_school_biology-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_biology-neg-answer
|
[
"region:us"
] |
2023-05-09T06:36:22+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 126929, "num_examples": 310}], "download_size": 74346, "dataset_size": 126929}}
|
2023-05-15T04:41:19+00:00
|
e5fa1bdcec313cebf4393d83fa537ff71dc54dfe
|
# Dataset Card for "mmlu-high_school_chemistry-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_chemistry-neg-answer
|
[
"region:us"
] |
2023-05-09T06:37:14+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 66318, "num_examples": 203}], "download_size": 38675, "dataset_size": 66318}}
|
2023-05-15T04:42:47+00:00
|
62dc659bb80aef59d646d43bbf7f96a5204df5ee
|
# Dataset Card for "mmlu-high_school_computer_science-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_computer_science-neg-answer
|
[
"region:us"
] |
2023-05-09T06:37:43+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 50115, "num_examples": 100}], "download_size": 31373, "dataset_size": 50115}}
|
2023-05-15T04:43:39+00:00
|
73f3fedbede7085ed77539d9b2ba697542e281c9
|
# Dataset Card for "mmlu-high_school_european_history-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_european_history-neg-answer
|
[
"region:us"
] |
2023-05-09T06:38:18+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 281184, "num_examples": 165}], "download_size": 150430, "dataset_size": 281184}}
|
2023-05-15T04:44:47+00:00
|
8b0814b226ef7d149819aec723b47955f4752730
|
# Dataset Card for "mmlu-high_school_geography-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_geography-neg-answer
|
[
"region:us"
] |
2023-05-09T06:39:03+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 48732, "num_examples": 198}], "download_size": 33478, "dataset_size": 48732}}
|
2023-05-15T04:46:10+00:00
|
77fb94460d89a557af156ffc8d11b2ba095d8108
|
# Dataset Card for "mmlu-high_school_government_and_politics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_government_and_politics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:39:37+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 78372, "num_examples": 193}], "download_size": 48366, "dataset_size": 78372}}
|
2023-05-15T04:47:10+00:00
|
6807bee6d98db41a01bcaf1374ac35e2db9e0f76
|
# Dataset Card for "mmlu-high_school_macroeconomics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_macroeconomics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:40:33+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 137273, "num_examples": 390}], "download_size": 65743, "dataset_size": 137273}}
|
2023-05-15T04:48:44+00:00
|
bc0243a7aacb059e7406835c742e44f48a28ed17
|
# Dataset Card for "mmlu-high_school_mathematics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_mathematics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:41:33+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 58794, "num_examples": 270}], "download_size": 37009, "dataset_size": 58794}}
|
2023-05-15T04:50:43+00:00
|
3cc77cff15c0444b39327168ce69e5ddf0d25840
|
estefanodi/dataset
|
[
"license:mit",
"region:us"
] |
2023-05-09T06:42:02+00:00
|
{"license": "mit"}
|
2023-05-09T07:53:01+00:00
|
|
9b8e6fa60fcdc93e2e18fc6f62bb92a1c74233d1
|
# Dataset Card for "mmlu-high_school_microeconomics-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/mmlu-high_school_microeconomics-neg-answer
|
[
"region:us"
] |
2023-05-09T06:42:13+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}, {"name": "neg_answer", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 88985, "num_examples": 238}], "download_size": 46595, "dataset_size": 88985}}
|
2023-05-15T04:51:50+00:00
|
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