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ce9bd8a8d62facf4e879df492270d51f84595460
|
# Dataset Card for "d754a8c6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d754a8c6
|
[
"region:us"
] |
2023-05-27T04:28:06+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1338, "dataset_size": 182}}
|
2023-05-27T04:28:07+00:00
|
d12d6bd095c10cb8ba3a954559d68e37a26c7406
|
angdong/nate-news-world
|
[
"license:mit",
"region:us"
] |
2023-05-27T04:34:40+00:00
|
{"license": "mit"}
|
2023-05-27T11:18:33+00:00
|
|
effd3117f3454a61a8e80dd6e8d3849cdbec9408
|
angdong/nate-news-economy
|
[
"license:mit",
"region:us"
] |
2023-05-27T04:35:03+00:00
|
{"license": "mit"}
|
2023-05-27T10:32:22+00:00
|
|
3f6ac61976fb3712196e9fe78e5456d51fd63cd6
|
angdong/nate-news-society
|
[
"license:mit",
"region:us"
] |
2023-05-27T04:35:17+00:00
|
{"license": "mit"}
|
2023-05-27T11:17:59+00:00
|
|
641e917ffe25f21c4410839f92c96ca5bd72d2d0
|
angdong/nate-news-science
|
[
"license:mit",
"region:us"
] |
2023-05-27T04:36:01+00:00
|
{"license": "mit"}
|
2023-05-27T11:17:27+00:00
|
|
b98407c2d94c83435b3fd0b61494d3b15f77dedc
|
namphan410/Test
|
[
"license:unknown",
"region:us"
] |
2023-05-27T04:49:03+00:00
|
{"license": "unknown"}
|
2023-05-27T11:39:15+00:00
|
|
954866754d155710c805e654e9fdd6c620af6040
|
# Dataset Card for "roleplay-characters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/roleplay-characters
|
[
"region:us"
] |
2023-05-27T05:20:12+00:00
|
{"dataset_info": {"features": [{"name": "char_name", "dtype": "string"}, {"name": "char_persona", "dtype": "string"}, {"name": "world_scenario", "dtype": "string"}, {"name": "char_greeting", "dtype": "string"}, {"name": "example_dialogue", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "personality", "dtype": "string"}, {"name": "scenario", "dtype": "string"}, {"name": "first_mes", "dtype": "string"}, {"name": "mes_example", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "created", "dtype": "int64"}, {"name": "modified", "dtype": "int64"}, {"name": "source", "dtype": "null"}, {"name": "tool", "struct": [{"name": "name", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "version", "dtype": "string"}]}, {"name": "version", "dtype": "int64"}]}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 474656700.0, "num_examples": 784}], "download_size": 0, "dataset_size": 474656700.0}}
|
2023-05-27T05:22:09+00:00
|
d4d6e5c65e4127a401b26def9ac22695eeffdc5a
|
# Dataset Card for "nan_tw_soap_opera"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
thomas0104/nan_tw_soap_opera
|
[
"region:us"
] |
2023-05-27T05:36:34+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 453247324.875, "num_examples": 3435}, {"name": "test", "num_bytes": 117415627.887, "num_examples": 1063}, {"name": "validation", "num_bytes": 126781252.756, "num_examples": 1119}], "download_size": 641559372, "dataset_size": 697444205.518}}
|
2023-05-30T12:34:17+00:00
|
4f507a9cc7429907e970d9572f387f076ba25879
|
sieu-n/cola
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-27T05:43:19+00:00
|
{"license": "apache-2.0"}
|
2023-05-27T05:43:19+00:00
|
|
20298b537ded2702da18f170bf3d31431a1f033c
|
calisolo/NICE_tsv
|
[
"license:cc-by-nc-nd-4.0",
"region:us"
] |
2023-05-27T05:46:55+00:00
|
{"license": "cc-by-nc-nd-4.0"}
|
2023-05-27T05:49:04+00:00
|
|
d0095a14faeaad923263a0ce2d301f8163b94106
|
# Machine Translated Indonesian STS-B
We believe that a synthetic baseline is better than no baseline. Therefore, we followed approached done in the [Thai Sentence Vector Benchmark](https://github.com/mrpeerat/Thai-Sentence-Vector-Benchmark) project and translated the [STS-B](https://github.com/facebookresearch/SentEval) test set to Indonesian via Google Translate API. This dataset will be used to evaluate our model's Spearman correlation score on the translated test set.
You can find the latest STS results that we achieved on this dataset in [Indonesian Sentence Embeddings](https://github.com/LazarusNLP/indo-sentence-embeddings).
|
LazarusNLP/stsb_mt_id
|
[
"language:id",
"region:us"
] |
2023-05-27T08:14:38+00:00
|
{"language": ["id"], "dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "data", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "score", "dtype": "string"}, {"name": "correlation", "dtype": "string"}, {"name": "text_1", "dtype": "string"}, {"name": "text_2", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 253093, "num_examples": 1379}, {"name": "validation", "num_bytes": 305450, "num_examples": 1500}], "download_size": 268625, "dataset_size": 558543}}
|
2024-01-06T04:32:51+00:00
|
a69cdfc68bbab53762ea826af638d3d1fba6c6d2
|
# Dataset Card for "NepCov19TweetsPlus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
raygx/NepCov19TweetsPlus
|
[
"region:us"
] |
2023-05-27T08:23:10+00:00
|
{"dataset_info": {"features": [{"name": "Sentiment", "dtype": "int64"}, {"name": "Sentences", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14110875, "num_examples": 41541}], "download_size": 5219950, "dataset_size": 14110875}}
|
2023-07-01T03:10:37+00:00
|
1535d30152f6f0b73d8c06047465f66a701fe3c6
|
## For some reason I can't push the data to hub using push_to_hub() method. Kept getting identical_ok error or sometimes data didn't get uploaded even when it was successful. Well, anyways, Data can be found in [kaggle](https://www.kaggle.com/datasets/reganmaharjan/nepali-corpus-and-tokenizer)
|
raygx/Nepali-Text-Corpus
|
[
"region:us"
] |
2023-05-27T09:12:28+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1679592183.806821, "num_examples": 1895289}], "download_size": 642358730, "dataset_size": 1679592183.806821}}
|
2023-07-08T03:26:11+00:00
|
19e050bf2e8132318f1d103084f0cdd4ebf2b3ab
|
# Dataset Card for Chinese Musical Instruments Timbre Evaluation Database
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/CMITE>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A
### Dataset Summary
This database contains subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 non-Chinese terms given by 14 participants in a subjective evaluation experiment.
### Supported Tasks and Leaderboards
Musical Instruments Timbre Evaluation
### Languages
Chinese, English
## Dataset Structure
### Data Instances
.zip(.wav), .csv
### Data Fields
Traditional instruments
### Data Splits
Chinese, Non-Chinese
## Dataset Creation
### Curation Rationale
Lack of a dataset for musical instruments timbre evaluation
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC
### Annotations
#### Annotation process
Subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 Non-Chinese terms given by 14 participants in a subjective evaluation experiment
#### Who are the annotators?
Students from CCMUSIC
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of AI in the music industry
### Discussion of Biases
Only for traditional instruments
### Other Known Limitations
Limited data
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
#### For Chinese instruments
[Yiliang, J. et al. (2020) ‘Analysis of Chinese Musical Instrument Timbre Based on Objective Features’, Journal of Fudan University(Natural Science), pp. 346-353+359. doi:10.15943/j.cnki.fdxb-jns.2020.03.014.](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=FDXB202003014&uniplatform=NZKPT&v=85qLeLUyrDt%25mmd2Btak%25mmd2BN90N7vYZSv%25mmd2BVc1EfPmaYcvpvrgY1XkL215gYG4J%25mmd2FD09viR0w)
#### For Non-Chinese instruments
[Jiang, Wei et al. “Analysis and Modeling of Timbre Perception Features of Chinese Musical Instruments.” 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS) (2019): 191-195.](https://ieeexplore.ieee.org/document/8940168)
### Licensing Information
```
MIT License
Copyright (c) CCMUSIC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
```
### Contributions
Provide a dataset for musical instruments timbre evaluation
|
ccmusic-database/instrument_timbre
|
[
"task_categories:audio-classification",
"size_categories:n<1K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] |
2023-05-27T09:31:24+00:00
|
{"language": ["zh", "en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["audio-classification"], "pretty_name": "Musical Instruments Timbre Evaluation Database", "tags": ["music", "art"], "viewer": false}
|
2023-12-04T16:04:44+00:00
|
290fd6571b5039ffb4c0dcd51170bfa27b22cf36
|
NYUMets/nyumets_brats
|
[
"license:other",
"region:us"
] |
2023-05-27T10:07:57+00:00
|
{"license": "other", "extra_gated_heading": "NYU Langone Health NYUMets Dataset Sharing Agreement", "extra_gated_prompt": "By registering for downloads from the NYUMets Dataset, I agree to this Dataset Sharing Agreement, as well as to the terms of use as posted and updated periodically at: http://nyulangone.org/policies-disclaimers/disclaimer.\nThe NYUMets Dataset is considered proprietary to and owned by New York University and NYU Langone Health (together \u201cNYU\u201d). Other than the rights granted herein, NYU retains all rights, title, and interest in the NYUMets Dataset.\nSubject to the provisions of this Agreement, NYU shall give to me access to and the right to download the NYUMets Dataset, and NYU hereby grants to me a non-exclusive, royalty-free license to use the NYUMets Dataset for internal research or educational purposes only and only as permitted by this Agreement. This Agreement conveys no other rights of any sort with respect to the NYUMets Dataset or the intellectual property rights embodied therein.\nI will receive AWS permissions to access the NYUMets Dataset without charge for internal research or educational purposes only. The link will permit me to download and access a verbatim copy of the NYUMets Dataset solely for such use. I will NOT SHARE THE DOWNLOADED DATA or AWS Account Access Credentials to the NYUMets Dataset with others. If another user within my organization or elsewhere wishes to obtain a copy of and use the NYUMets Dataset, they must register as an individual user and comply with all the terms of this Agreement.", "extra_gated_fields": {"Name": "text", "Email": "text", "Organization": "text", "Phone Number": "text", "By checking this box, you are certifying that you have read and understood the NYU Langone Health NYUMets Dataset Sharing Agreement": "checkbox"}}
|
2023-05-27T10:55:52+00:00
|
|
fe6c2f0f7b9215afaa0395c7e5f9fffaa437751d
|
Raul023/Paddy
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-27T10:08:59+00:00
|
{"license": "apache-2.0"}
|
2023-05-27T10:57:17+00:00
|
|
ad5c69122afb570e92b753947f2925befaa87e7d
|
# Dataset Card for "wikipedia_id_20230520"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LazarusNLP/wikipedia_id_20230520
|
[
"region:us"
] |
2023-05-27T10:10:26+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1977153247, "num_examples": 10059935}], "download_size": 604591152, "dataset_size": 1977153247}}
|
2023-05-27T10:11:01+00:00
|
cda57f691456e3afe1a4b9a2a8e80b961da6e416
|
This dataset contains vi subsets (first 191 examples) and auto-translation from en to vi subsets (the rest, 38346 examples) from [OASST1](https://huggingface.co/datasets/OpenAssistant/oasst1). All auto-translation examples are generated using [VietAI envit5-translation](https://huggingface.co/VietAI/envit5-translation).
The vi subsets have the same features as the original dataset. Meanwhile, the auto-translation subsets introduce two new features:
- `"text_chunks"` is a list that contains chunked text split from `"text"`, each chunk has no more than 300 tokens. The sent_tokenizer and word_tokenzier used are from spacy en_core_web_sm model.
- `"text_translation"` contains merged of all translated chunks. Due to the auto-translation model, all new-line symbols (`\n`) are removed.
The translation script can be found at `translate_en_to_vi.py`
|
Zayt/oasst1-vi
|
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:vi",
"license:apache-2.0",
"region:us"
] |
2023-05-27T10:31:11+00:00
|
{"language": ["vi"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}, {"name": "text_chunks", "sequence": "string"}, {"name": "text_translation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59922108.85834358, "num_examples": 38537}], "download_size": 39428167, "dataset_size": 59922108.85834358}}
|
2023-05-31T08:51:37+00:00
|
c1274613e7a9a0fd38425ae763ff0ea07c1f188a
|
https://github.com/causalNLP/cladder
|
tasksource/cladder
|
[
"language:en",
"license:mit",
"region:us"
] |
2023-05-27T11:14:54+00:00
|
{"language": ["en"], "license": "mit"}
|
2023-05-31T07:25:48+00:00
|
8f29d8ee25f20474971755ea9397042b4d2591de
|
# Dataset Card for A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era
## Dataset Description
- **Repository:** https://github.com/qwenzo/-IDMGSP
- **Paper:** TODO
### Dataset Summary
A benchmark for detecting machine-generated scientific papers based on their abstract, introduction and conclusion sections.
### Supported Tasks and Leaderboards
current benchmark results in terms of accuracy:
| Model | Train Dataset | TEST | OOD-GPT3 | OOD-REAL | TECG | TEST-CC |
|-----------------------------|-----------------|---------|----------|----------|---------|---------|
| LR-1gram (tf-idf) (our) | TRAIN | 95.3% | 4.0% | 94.6% | 96.1% | 7.8% |
| LR-1gram (tf-idf) (our) | TRAIN+GPT3 | 94.6% | 86.5% | 86.2% | 97.8% | 13.7% |
| LR-1gram (tf-idf) (our) | TRAIN-CG | 86.6% | 0.8% | 97.8% | 32.6% | 1.2% |
| RF-1gram (tf-idf) (our) | TRAIN | 94.8% | 24.7% | 87.3% | 100.0% | 8.1% |
| RF-1gram (tf-idf) (our) | TRAIN+GPT3 | 91.7% | 95.0% | 69.3% | 100.0% | 15.1% |
| RF-1gram (tf-idf) (our) | TRAIN-CG | 97.6% | 7.0% | 95.0% | 57.0% | 1.7% |
| [IDMGSP-Galactica-TRAIN](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN) (our) | TRAIN | 98.4% | 25.9% | 95.5% | 84.0% | 6.8% |
| [IDMGSP-Galactica-TRAIN_GPT3](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN_GPT3) (our) | TRAIN+GPT3 | 98.5% | 71.2% | 95.1% | 84.0% | 12.0% |
| [IDMGSP-Galactica-TRAIN-CG](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN-CG) (our) | TRAIN-CG | 96.4% | 12.4% | 97.6% | 61.3% | 2.4% |
| [IDMGSP-RoBERTa-TRAIN-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CONCLUSION) (our) | TRAIN | 72.3% | 55.5% | 50.0% | 100.0% | 63.5% |
| [IDMGSP-RoBERTa-TRAIN_GPT3-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN_GPT3-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN_GPT3-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-CONCLUSION) (our) | TRAIN+GPT3 | 65.7% | 100.0% | 29.1% | 100.0% | 75.0% |
| [IDMGSP-RoBERTa-TRAIN-CG-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN-CG-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN-CG-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-CONCLUSION) (our) | TRAIN-CG | 86.0% | 2.0% | 92.5% | 76.5% | 9.2% |
| GPT-3 (our) | TRAIN-SUB | 100.0% | 25.9% | 99.0% | 100.0% | N/A |
| DetectGPT | - | 61.5% | 0.0% | 99.9% | 68.7% | N/A |
| ChatGPT-IO (our)* | - | 69.0% | 49.0% | 89.0% | 0.0% | 3.0% |
| LLMFE (our)* | TRAIN+GPT3 | 80.0% | 62.0% | 70.0% | 90.0% | 33.0% |
### Languages
English
## Dataset Structure
### Data Instances
Each instance in the dataset corresponds to a row in a CSV file, encompassing the features of a paper, its label, and the paper's source.
### Data Fields
#### classifier_input
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "chatgpt", "gpt2", "real", "scigen" or "galactica".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### train+gpt3
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "chatgpt", "gpt2", "real", "scigen" or "galactica", "gpt3".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### tecg
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. Always has the value "chatgpt".
dtype: string
- name: label
description: always having the value 1.
dtype: int64
#### train-cg
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "gpt2", "real", "scigen" or "galactica".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### ood_gpt3
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: src
description: indicator of the source of the paper. Has the value "gpt3".
dtype: string
- name: label
description: always having the value 1.
dtype: int64
#### ood_real
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: src
description: indicator of the source of the paper. Has the value "ood_real".
dtype: string
- name: label
description: always having the value 0.
dtype: int64
#### test-cc
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. Always has the value "chatgpt-paraphrased".
dtype: string
- name: paraphrased_sections
description: indicator of which sections are paraphrased. Can have the values "introduction", "conclusion", "introduction, conclusion", "abstract, introduction, conclusion".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers. Always has the value 1.
dtype: int64
### Data Splits
Table: Overview of the datasets used to train and evaluate the classifiers.
| Dataset | arXiv | ChatGPT | GPT-2 | SCIgen | Galactica | GPT-3 | ChatGPT (co-created) |
|--------------------------------------|--------|---------|--------|--------|-----------|--------|-----------------------|
| Standard train (TRAIN) | 8k | 2k | 2k | 2k | 2k | - | - |
| Standard train subset (TRAIN-SUB) | 4k | 1k | 1k | 1k | 1k | - | - |
| TRAIN without ChatGPT (TRAIN-CG) | 8k | - | 2k | 2k | 2k | - | - |
| TRAIN plus GPT-3 (TRAIN+GPT3) | 8k | 2k | 2k | 2k | 2k | 1.2k | - |
| Standard test (TEST) | 4k | 1k | 1k | 1k | 1k | - | - |
| Out-of-domain GPT-3 only (OOD-GPT3) | - | - | - | - | - | 1k | - |
| Out-of-domain real (OOD-REAL) | 4k (parsing 2) | - | - | - | - | - | - |
| ChatGPT only (TECG) | - | 1k | - | - | - | - | - |
| Co-created test (TEST-CC) | - | - | - | - | - | - | 4k |
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
tum-nlp/IDMGSP
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:openrail++",
"scientific paper",
"fake papers",
"science",
"scientific text",
"region:us"
] |
2023-05-27T11:20:55+00:00
|
{"language": ["en"], "license": "openrail++", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": " A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era (IDMGSP)", "viewer": true, "tags": ["scientific paper", "fake papers", "science", "scientific text"], "dataset_info": [{"config_name": "classifier_input", "features": [{"name": "id", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "introduction", "dtype": "string"}, {"name": "conclusion", "dtype": "string"}, {"name": "categories", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 70117904, "num_examples": 16000}, {"name": "test", "num_bytes": 34724993, "num_examples": 8000}], "download_size": 32157176, "dataset_size": 104842897}, {"config_name": "tecg", "features": [{"name": "id", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "introduction", "dtype": "string"}, {"name": "conclusion", "dtype": "string"}, {"name": "categories", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 2408633, "num_examples": 1000}], "download_size": 582824, "dataset_size": 2408633}, {"config_name": "train+gpt3", "features": [{"name": "id", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "introduction", "dtype": "string"}, {"name": "conclusion", "dtype": "string"}, {"name": "categories", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 73586425, "num_examples": 17200}], "download_size": 22487536, "dataset_size": 73586425}, {"config_name": "train-cg", "features": [{"name": "id", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "introduction", "dtype": "string"}, {"name": "conclusion", "dtype": "string"}, {"name": "categories", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 65261576, "num_examples": 14000}], "download_size": 20272344, "dataset_size": 65261576}, {"config_name": "ood_gpt3", "features": [{"name": "title", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "introduction", "dtype": "string"}, {"name": "conclusion", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3454121, "num_examples": 1200}, {"name": "test", "num_bytes": 2837275, "num_examples": 1000}], "download_size": 1708501, "dataset_size": 6291396}, {"config_name": "ood_real", "features": [{"name": "abstract", "dtype": "string"}, {"name": "introduction", "dtype": "string"}, {"name": "conclusion", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 15808225, "num_examples": 4000}], "download_size": 5336873, "dataset_size": 15808225}]}
|
2023-09-12T10:57:59+00:00
|
860a7c248978e59c055fab1226e950f5b7412878
|
zengzeng/slogan_demo
|
[
"license:bigscience-openrail-m",
"region:us"
] |
2023-05-27T11:40:24+00:00
|
{"license": "bigscience-openrail-m"}
|
2023-05-27T11:40:24+00:00
|
|
d33e2960230bb20976963294ad28c7463496886b
|
# Dataset Card for "slogan_demo_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zengzeng/slogan_demo_dataset
|
[
"region:us"
] |
2023-05-27T11:49:58+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "slogan", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10328, "num_examples": 102}], "download_size": 11740, "dataset_size": 10328}}
|
2023-05-27T11:50:00+00:00
|
255f257e77179c638acbd3c67fa14a96b28a3e67
|
# Dataset Card for "mcl_signal_processing_attacks_whisper_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/mcl_signal_processing_attacks_whisper_commonvoice
|
[
"region:us"
] |
2023-05-27T12:18:51+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0-benign", "1": "1-kenan", "2": "2-yeehaw", "3": "3-imaginary_clipping"}}}}], "splits": [{"name": "train", "num_bytes": 86186133.0, "num_examples": 200}], "download_size": 84525602, "dataset_size": 86186133.0}}
|
2023-05-27T12:20:24+00:00
|
70e09996304279bcb233e96ee35e30d9fd7b62ea
|
# Dataset Card for "sam-controlnet-4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
baptistecolle/sam-controlnet-4
|
[
"region:us"
] |
2023-05-27T12:25:52+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filepath", "dtype": "string"}, {"name": "sentids", "sequence": "int64"}, {"name": "filename", "dtype": "string"}, {"name": "imgid", "dtype": "int64"}, {"name": "split", "dtype": "string"}, {"name": "sentences", "struct": [{"name": "imgid", "dtype": "int64"}, {"name": "raw", "dtype": "string"}, {"name": "sentid", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}]}, {"name": "cocoid", "dtype": "int64"}, {"name": "masks", "sequence": {"sequence": {"sequence": "bool"}}}, {"name": "scores", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 115970746.0, "num_examples": 41}], "download_size": 6382710, "dataset_size": 115970746.0}}
|
2023-05-27T17:49:04+00:00
|
cd49ce64cb64700210523410eb1a3548495a2267
|
Nazarko/2D_GPS_Accelerometer
|
[
"license:unknown",
"region:us"
] |
2023-05-27T12:45:18+00:00
|
{"license": "unknown"}
|
2023-05-27T12:48:27+00:00
|
|
c036a4417b8dbc31c6d722cba9c00c4f2312b29f
|
# Dataset Card for "a63e0c1c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/a63e0c1c
|
[
"region:us"
] |
2023-05-27T13:57:07+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1328, "dataset_size": 182}}
|
2023-05-27T13:57:08+00:00
|
43fc072bb872fdce10890c9e811efe359f5f082c
|
jcavecilla/daisuki_df
|
[
"language:en",
"license:mit",
"region:us"
] |
2023-05-27T14:17:15+00:00
|
{"language": ["en"], "license": "mit"}
|
2023-05-27T14:58:15+00:00
|
|
dad1074f956b506f88036c6aa3a1519ebbe96495
|
# Dataset Card for "flippy_final1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kalcho100/flippy_final1
|
[
"region:us"
] |
2023-05-27T14:21:45+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1042126622.5634332, "num_examples": 763081}, {"name": "test", "num_bytes": 115792150.43656677, "num_examples": 84787}], "download_size": 623847339, "dataset_size": 1157918773.0}}
|
2023-05-27T14:22:36+00:00
|
d1e0496a64d1997768d549dc696fb3f8b743e652
|
# Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
## Features
[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
- Original txt2img and img2img modes
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Color Sketch
- Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- a man in a `((tuxedo))` - will pay more attention to tuxedo
- a man in a `(tuxedo:1.21)` - alternative syntax
- select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
- Textual Inversion
- have as many embeddings as you want and use any names you like for them
- use multiple embeddings with different numbers of vectors per token
- works with half precision floating point numbers
- train embeddings on 8GB (also reports of 6GB working)
- Extras tab with:
- GFPGAN, neural network that fixes faces
- CodeFormer, face restoration tool as an alternative to GFPGAN
- RealESRGAN, neural network upscaler
- ESRGAN, neural network upscaler with a lot of third party models
- SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
- Adjust sampler eta values (noise multiplier)
- More advanced noise setting options
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Live prompt token length validation
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- drag and drop an image/text-parameters to promptbox
- Read Generation Parameters Button, loads parameters in promptbox to UI
- Settings page
- Running arbitrary python code from UI (must run with `--allow-code` to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Tiling support, a checkbox to create images that can be tiled like textures
- Progress bar and live image generation preview
- Can use a separate neural network to produce previews with almost none VRAM or compute requirement
- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
- Styles, a way to save part of prompt and easily apply them via dropdown later
- Variations, a way to generate same image but with tiny differences
- Seed resizing, a way to generate same image but at slightly different resolution
- CLIP interrogator, a button that tries to guess prompt from an image
- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
- Batch Processing, process a group of files using img2img
- Img2img Alternative, reverse Euler method of cross attention control
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
- Reloading checkpoints on the fly
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
- separate prompts using uppercase `AND`
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
- Generate forever option
- Training tab
- hypernetworks and embeddings options
- Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
- Clip skip
- Hypernetworks
- Loras (same as Hypernetworks but more pretty)
- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
- Can select to load a different VAE from settings screen
- Estimated completion time in progress bar
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
- Now without any bad letters!
- Load checkpoints in safetensors format
- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
- Now with a license!
- Reorder elements in the UI from settings screen
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
### Automatic Installation on Linux
1. Install the dependencies:
```bash
# Debian-based:
sudo apt install wget git python3 python3-venv
# Red Hat-based:
sudo dnf install wget git python3
# Arch-based:
sudo pacman -S wget git python3
```
2. Navigate to the directory you would like the webui to be installed and execute the following command:
```bash
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
3. Run `webui.sh`.
4. Check `webui-user.sh` for options.
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
## Contributing
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
|
Dokumento/sd-webui
|
[
"arxiv:2211.06679",
"region:us"
] |
2023-05-27T14:32:31+00:00
|
{}
|
2023-05-27T14:33:35+00:00
|
fe5b880c47ed29517447067524375eb72a5d9acc
|
# Dataset Card for "avatar-the-last-airbender-urls"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lumenggan/avatar-the-last-airbender-urls
|
[
"size_categories:10K<n<100K",
"license:openrail",
"art",
"region:us"
] |
2023-05-27T14:55:59+00:00
|
{"license": "openrail", "size_categories": ["10K<n<100K"], "pretty_name": "Avatar: The Last Airbender - Links to images", "dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "urls", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 2718856, "num_examples": 3}], "download_size": 260408, "dataset_size": 2718856}, "tags": ["art"]}
|
2023-05-27T15:04:57+00:00
|
082eed36505334fe5c121dc3b162554feeb3872f
|
staxjp/tick
|
[
"license:bsd",
"region:us"
] |
2023-05-27T15:41:05+00:00
|
{"license": "bsd"}
|
2023-05-27T19:28:34+00:00
|
|
3437b5f0b56ecf5ed7a072920a38cb090f175afc
|
# Dataset Card for "flippy_final2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kalcho100/flippy_final2
|
[
"region:us"
] |
2023-05-27T16:19:50+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1047628246.0021477, "num_examples": 761834}, {"name": "test", "num_bytes": 116404207.9978523, "num_examples": 84649}], "download_size": 628911397, "dataset_size": 1164032454.0}}
|
2023-05-27T16:20:48+00:00
|
5e24025ef7dd1981bca115b7923af6a6ea8d90e5
|
xzxy2023412/diyici
|
[
"license:openrail",
"region:us"
] |
2023-05-27T16:59:54+00:00
|
{"license": "openrail"}
|
2023-05-27T16:59:54+00:00
|
|
f085d7cd730af08a6a4f87b792969b4bfd258454
|
# Dataset Card for "a4544219"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/a4544219
|
[
"region:us"
] |
2023-05-27T17:08:37+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1336, "dataset_size": 186}}
|
2023-05-27T17:08:38+00:00
|
65f68cba5d70aa6c0331ab42703c4fe4c269acb5
|
# Dataset Card for "avatar-the-last-airbender"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lumenggan/avatar-the-last-airbender
|
[
"region:us"
] |
2023-05-27T17:44:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1465863874.344, "num_examples": 13896}], "download_size": 1427257543, "dataset_size": 1465863874.344}}
|
2023-05-27T17:45:40+00:00
|
447fd90539cae95efd801caa748ee5aecb62f1a2
|
# Dataset Card for "TinyImagenet_800_validation_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_800"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/TinyImagenet_800_validation_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_800
|
[
"region:us"
] |
2023-05-27T17:45:39+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 334861, "num_examples": 800}], "download_size": 101976, "dataset_size": 334861}}
|
2023-05-27T18:03:45+00:00
|
0cd15ae0b3bb462527366c7dde5e3bb2099d63ab
|
# Dataset Card for "TinyImagenet_200_validation_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/TinyImagenet_200_validation_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_200
|
[
"region:us"
] |
2023-05-27T18:08:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 88119, "num_examples": 200}], "download_size": 37433, "dataset_size": 88119}}
|
2023-05-27T18:24:27+00:00
|
7a0f8767f5a803a97cb7ecf77aeec17d987d5b8d
|
Thaweewat/HealthCareMagic-100k-th
|
[
"size_categories:100K<n<1M",
"language:th",
"region:us"
] |
2023-05-27T18:49:58+00:00
|
{"language": ["th"], "size_categories": ["100K<n<1M"]}
|
2023-05-27T18:53:34+00:00
|
|
80cda905c8647246e3bbee6ac6451e4cb99f51b3
|
joachimsallstrom/lux_np
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-05-27T18:54:42+00:00
|
{"license": "creativeml-openrail-m"}
|
2023-05-27T18:55:01+00:00
|
|
e78551a0e45c2bf4a826026ec08f961e9e6cce7f
|
Thaweewat/chatmed-5k-th
|
[
"size_categories:1K<n<10K",
"language:th",
"region:us"
] |
2023-05-27T18:58:08+00:00
|
{"language": ["th"], "size_categories": ["1K<n<10K"]}
|
2023-05-27T18:59:34+00:00
|
|
7abee6c92db3a4618890afae74cfe439ed21411e
|
Kefasu/My-Data
|
[
"license:openrail",
"region:us"
] |
2023-05-27T19:13:59+00:00
|
{"license": "openrail"}
|
2023-05-27T19:13:59+00:00
|
|
a3567c959627f12bbfee9ee7ddfbe9ce73271ee4
|
# Counter Strike Map Dataset
This dataset consists of Counter Strike map images along with their corresponding labels and x-y coordinates. The dataset is suitable for image classification tasks and includes the necessary information for each image.
## Dataset Details
- Total Images: [1424]
- Classes: [5]
- Image Size: [1920x1080]
- Format: [png]
## Files
The dataset includes the following files:
- **maps/train/**: This folder contains the Counter Strike map images. The images are named in a consistent format, typically with a prefix or unique identifier followed by the file extension.
- **metadata.csv**: This CSV file contains the annotations for each image in the dataset. It has the following columns:
- `file_name`: The relative or absolute path to the image file.
- `label`: The label or class of the image.
- `x`: The x-coordinate of a specific point of interest within the image.
- `y`: The y-coordinate of the same point of interest within the image.
|
HOXSEC/csgo-maps
|
[
"task_categories:image-classification",
"size_categories:1K<n<10K",
"license:mit",
"region:us"
] |
2023-05-27T19:16:34+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification"], "pretty_name": "Counter Strike Maps"}
|
2023-05-30T19:39:07+00:00
|
1f84fb73791c9f21fb54e2d2f982714c0ad5b065
|
# Dataset Card for "medication_chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
stoddur/medication_chat
|
[
"region:us"
] |
2023-05-27T19:20:33+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 371157528.0, "num_examples": 240387}], "download_size": 14253912, "dataset_size": 371157528.0}}
|
2023-05-31T22:07:49+00:00
|
9c92c929c4bec7692b48695cdc0cb1c73cdc239f
|
# Dataset Card for "avatar-the-last-airbender-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lumenggan/avatar-the-last-airbender-tagged
|
[
"task_categories:image-to-text",
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"art",
"anime",
"atla",
"region:us"
] |
2023-05-27T19:21:16+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["image-to-text", "image-classification"], "pretty_name": "Avatar: The Last Airbender - Tagged Screencaps", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1467443424.776, "num_examples": 13896}], "download_size": 1427401832, "dataset_size": 1467443424.776}, "tags": ["art", "anime", "atla"]}
|
2023-05-27T20:50:06+00:00
|
7c2ba440b594ae3b2b63d5ea5bdd96bc18944684
|
**Kana Kojima** from **Nande Koko ni Sensei ga!?**
- *Trained with anime (full-final-pruned) model.*
- *Works best with ALL, MIDD, OUTD, and OUTALL LoRA weight blocks, and with 0.7+ weights.*
|
Cheetor1996/Kana_Kojima
|
[
"language:en",
"license:cc-by-2.0",
"art",
"region:us"
] |
2023-05-27T19:28:56+00:00
|
{"language": ["en"], "license": "cc-by-2.0", "tags": ["art"]}
|
2023-05-27T19:33:03+00:00
|
496ea28a2930ccf3fdb5a0402a1e50f72d07e448
|
# Dataset Card for "autotrain-data-8g7k-mxd7-vuc8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adityavelusamy/questy-v2
|
[
"region:us"
] |
2023-05-27T19:29:58+00:00
|
{"dataset_info": {"features": [{"name": "distractor3", "dtype": "string"}, {"name": "distractor1", "dtype": "string"}, {"name": "distractor2", "dtype": "string"}, {"name": "correct_answer", "dtype": "string"}, {"name": "support", "dtype": "string"}, {"name": "autotrain_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1309908, "num_examples": 2398}, {"name": "validation", "num_bytes": 354111, "num_examples": 600}], "download_size": 1048645, "dataset_size": 1664019}}
|
2023-05-27T19:30:00+00:00
|
b9b4a0d42eda5c8db05e5ca7f9b8b0a38e257f90
|
**Rika Minami** from **Highschool of the Dead**
- *Trained with anime (full-final-pruned) model*
- *Works best with ALL, MIDD, OUTD, and OUTALL LoRA weight block weights, and with 0.7+ weights*
|
Cheetor1996/Rika_Minami
|
[
"language:en",
"license:cc-by-2.0",
"art",
"region:us"
] |
2023-05-27T19:33:40+00:00
|
{"language": ["en"], "license": "cc-by-2.0", "tags": ["art"]}
|
2023-05-27T19:36:06+00:00
|
351a93be292010c226ecb3040628003de2dd7233
|
Richard9777/prueba
|
[
"license:openrail",
"region:us"
] |
2023-05-27T20:04:04+00:00
|
{"license": "openrail"}
|
2023-05-27T20:04:04+00:00
|
|
c659e4318981a9d3d8c2791462f7624ba9aab2e1
|
remg1997/speech_wikimedia
|
[
"license:cc",
"region:us"
] |
2023-05-27T20:34:17+00:00
|
{"license": "cc"}
|
2023-06-20T14:54:22+00:00
|
|
97b964cce42a7214e7c74aa7383fc383f3f0f344
|
# Dataset Card for "sam-controlnet-5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
baptistecolle/sam-controlnet-5
|
[
"region:us"
] |
2023-05-27T20:40:26+00:00
|
{"dataset_info": {"features": [{"name": "masks", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 140788170.0, "num_examples": 1000}], "download_size": 0, "dataset_size": 140788170.0}}
|
2023-05-29T19:14:20+00:00
|
07b44e5d82ec08f42405b8f88bc9c006c4fc2880
|
# Dataset Card for "lotr-book"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
psandev/lotr-book
|
[
"region:us"
] |
2023-05-27T20:50:01+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 2196528.0, "num_examples": 268}, {"name": "test", "num_bytes": 245880.0, "num_examples": 30}], "download_size": 1126733, "dataset_size": 2442408.0}}
|
2023-05-27T20:50:30+00:00
|
00f573a65fce6d26e97642aad06ff6a94a975eae
|
Here are HQ summaries of each ~2000 texts from
- Project Gutenbberg
- Pubmed
- Arxiv
- Wikipedia
- Soda
with the original texts and instructions which include the word counts of the summaries.
|
ChristophSchuhmann/gutenberg-wiki-arxiv-pubmed-soda-summaries
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-27T20:51:13+00:00
|
{"license": "apache-2.0"}
|
2023-05-27T20:56:50+00:00
|
831dabac2283d99420cda0b673d7a2a43849f17a
|
This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0
|
timdettmers/openassistant-guanaco
|
[
"region:us"
] |
2023-05-27T20:56:25+00:00
|
{}
|
2023-05-27T21:40:40+00:00
|
c49b9dbd50e07e1c2b40f55c996adbe258c7ce47
|
# Dataset Card for "MD_NoPunctuation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cburger/MD_NoPunctuation
|
[
"region:us"
] |
2023-05-27T23:49:38+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": " Allergy / Immunology", "1": " Autopsy", "2": " Bariatrics", "3": " Cardiovascular / Pulmonary", "4": " Chiropractic", "5": " Consult - History and Phy.", "6": " Cosmetic / Plastic Surgery", "7": " Dentistry", "8": " Dermatology", "9": " Diets and Nutritions", "10": " Discharge Summary", "11": " ENT - Otolaryngology", "12": " Emergency Room Reports", "13": " Endocrinology", "14": " Gastroenterology", "15": " General Medicine", "16": " Hematology - Oncology", "17": " Hospice - Palliative Care", "18": " IME-QME-Work Comp etc.", "19": " Lab Medicine - Pathology", "20": " Letters", "21": " Nephrology", "22": " Neurology", "23": " Neurosurgery", "24": " Obstetrics / Gynecology", "25": " Office Notes", "26": " Ophthalmology", "27": " Orthopedic", "28": " Pain Management", "29": " Pediatrics - Neonatal", "30": " Physical Medicine - Rehab", "31": " Podiatry", "32": " Psychiatry / Psychology", "33": " Radiology", "34": " Rheumatology", "35": " SOAP / Chart / Progress Notes", "36": " Sleep Medicine", "37": " Speech - Language", "38": " Surgery", "39": " Urology"}}}}], "splits": [{"name": "train", "num_bytes": 15217808, "num_examples": 4966}], "download_size": 7116577, "dataset_size": 15217808}}
|
2023-05-28T00:51:02+00:00
|
d2729f40e287e5cdf60156679d283349de0f4432
|
This dataset was using "kunishou/databricks-dolly-15k-ja"
This dataset is licensed under CC BY SA 3.0
Last Update : 2023-05-28
databricks-dolly-15k-ja-gozaru
kunishou/databricks-dolly-15k-ja
https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja
|
bbz662bbz/databricks-dolly-15k-ja-gozaru
|
[
"license:cc-by-sa-3.0",
"region:us"
] |
2023-05-27T23:51:18+00:00
|
{"license": "cc-by-sa-3.0"}
|
2023-05-29T11:58:37+00:00
|
50b751a7087959d10271bc720a25b4f204e6d058
|
TH 1.7M Arithmetic Tasks dataset inspired from [Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks](https://arxiv.org/abs/2305.14201) & [Author's HF](https://huggingface.co/datasets/tiedong/goat)
**FYI:** Use columns "instruction" and "output" if you plan to instruct fine-tuning.
|
Thaweewat/goat-th
|
[
"size_categories:1M<n<10M",
"language:th",
"arxiv:2305.14201",
"region:us"
] |
2023-05-28T00:03:03+00:00
|
{"language": ["th"], "size_categories": ["1M<n<10M"]}
|
2023-05-28T00:17:46+00:00
|
fac5197b135550f48c06738412b85aeb3ecd4607
|
# Dataset Card for "MD_raw_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cburger/MD_raw_2
|
[
"region:us"
] |
2023-05-28T00:20:42+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": " Allergy / Immunology", "1": " Autopsy", "2": " Bariatrics", "3": " Cardiovascular / Pulmonary", "4": " Chiropractic", "5": " Consult - History and Phy.", "6": " Cosmetic / Plastic Surgery", "7": " Dentistry", "8": " Dermatology", "9": " Diets and Nutritions", "10": " Discharge Summary", "11": " ENT - Otolaryngology", "12": " Emergency Room Reports", "13": " Endocrinology", "14": " Gastroenterology", "15": " General Medicine", "16": " Hematology - Oncology", "17": " Hospice - Palliative Care", "18": " IME-QME-Work Comp etc.", "19": " Lab Medicine - Pathology", "20": " Letters", "21": " Nephrology", "22": " Neurology", "23": " Neurosurgery", "24": " Obstetrics / Gynecology", "25": " Office Notes", "26": " Ophthalmology", "27": " Orthopedic", "28": " Pain Management", "29": " Pediatrics - Neonatal", "30": " Physical Medicine - Rehab", "31": " Podiatry", "32": " Psychiatry / Psychology", "33": " Radiology", "34": " Rheumatology", "35": " SOAP / Chart / Progress Notes", "36": " Sleep Medicine", "37": " Speech - Language", "38": " Surgery", "39": " Urology"}}}}], "splits": [{"name": "train", "num_bytes": 15217808, "num_examples": 4966}], "download_size": 7299369, "dataset_size": 15217808}}
|
2023-05-28T00:33:12+00:00
|
5d5cb99051fce4da45af1c20dc8e489758c8fc2f
|
# Dataset Card for "Chinese_modern_classical"
数据来自于[NiuTrans/Classical-Modern: 非常全的文言文（古文）-现代文平行语料 (github.com)](https://github.com/NiuTrans/Classical-Modern)。
由于原始数据中部分古文没有译文，所以本数据集的数据仅包括了[双语数据 ](https://github.com/NiuTrans/Classical-Modern/tree/main/双语数据)。
|
xmj2002/Chinese_modern_classical
|
[
"task_categories:translation",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"region:us"
] |
2023-05-28T01:14:34+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["translation"], "dataset_info": {"features": [{"name": "info", "dtype": "string"}, {"name": "modern", "dtype": "string"}, {"name": "classical", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 209412286, "num_examples": 972467}], "download_size": 123454543, "dataset_size": 209412286}}
|
2023-05-30T05:26:32+00:00
|
381129816e52a0c4c562d2f26bacc5cfb9d1ceb4
|
# Dataset Card for "interior_style_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hammer888/interior_style_dataset
|
[
"region:us"
] |
2023-05-28T01:16:44+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1345413786.311, "num_examples": 7233}], "download_size": 0, "dataset_size": 1345413786.311}}
|
2023-05-28T15:26:01+00:00
|
7b170ec71bca24af459b008264b2088ad2a4248c
|
huolongguo10/MultiChat
|
[
"task_categories:conversational",
"language:zh",
"license:openrail",
"code",
"region:us"
] |
2023-05-28T01:36:00+00:00
|
{"language": ["zh"], "license": "openrail", "task_categories": ["conversational"], "tags": ["code"]}
|
2023-05-29T12:28:41+00:00
|
|
41be7e000d971bacc8c0478cc4d7ec7e425bf9e5
|
indikamk/misconceptions
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"region:us"
] |
2023-05-28T01:44:09+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "Misconceptions about Electrical Circuits"}
|
2023-05-30T10:07:40+00:00
|
|
91990669d7ada421ebbdf7066e168668b92053d2
|
terrakom/dataset
|
[
"license:mit",
"region:us"
] |
2023-05-28T01:46:04+00:00
|
{"license": "mit"}
|
2023-05-28T01:49:55+00:00
|
|
c5b105bd65ec346c3de2d91e748287c680b9ee9f
|
kishan84/kht_tr_aining
|
[
"size_categories:n<1K",
"region:us"
] |
2023-05-28T01:59:33+00:00
|
{"size_categories": ["n<1K"], "pretty_name": "D"}
|
2023-05-28T02:01:47+00:00
|
|
972b50e77f89b2d85f5fa950030e44a50861003e
|
<p align="center"><h1> Legal case retrieval with Korean Precedents (powered by https://law.go.kr/)</h1></p>
This dataset repository maintains files required for legal case retrieval using Korean Precedents acquired from https://law.go.kr/
For codes and more information, refer to **[GitHub page](https://github.com/jaeminSon/law.go.kr-cases/tree/main)**
|
woalsdnd/law.go.kr
|
[
"task_categories:text-retrieval",
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"language:ko",
"license:mit",
"legal",
"region:us"
] |
2023-05-28T02:33:14+00:00
|
{"language": ["ko"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-retrieval", "feature-extraction"], "pretty_name": "Legal case retrieval with Korean Precedents", "tags": ["legal"]}
|
2023-07-02T13:08:50+00:00
|
36ee0e363c09263125520f5db1679f41649449df
|
amjad101/marnics
|
[
"region:us"
] |
2023-05-28T03:48:23+00:00
|
{}
|
2023-05-28T03:49:25+00:00
|
|
e54073cd64d675fbbef9b0a0743f3d6d63a3ca06
|
- This is a little bit different version of [`kunishou/hh-rlhf-49k-ja`](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) without `ng_translation == 1` examples.
- Please also refer to the original dataset [`kunishou/hh-rlhf-49k-ja`](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja).
|
fujiki/japanese_hh-rlhf-49k
|
[
"language:ja",
"license:mit",
"region:us"
] |
2023-05-28T04:55:53+00:00
|
{"language": ["ja"], "license": "mit", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "index", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34168978, "num_examples": 49332}], "download_size": 18427777, "dataset_size": 34168978}}
|
2023-05-28T05:08:04+00:00
|
6a10e3029a06c4513ade1b7d9b5a50cc1f18e327
|
# Dataset Card for "EN_PARAGRAPH_GPT_JOINED"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bot-yaya/EN_PARAGRAPH_GPT_JOINED
|
[
"region:us"
] |
2023-05-28T05:20:45+00:00
|
{"dataset_info": {"features": [{"name": "record", "dtype": "string"}, {"name": "raw_text", "dtype": "string"}, {"name": "is_hard_linebreak", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 6311655, "num_examples": 196}], "download_size": 3088802, "dataset_size": 6311655}}
|
2023-05-28T05:21:42+00:00
|
f094783ec48db51988e7fe3c7c4ab3e7314b3e2d
|
katinameadows/shin-masked-rider
|
[
"license:openrail",
"region:us"
] |
2023-05-28T05:36:36+00:00
|
{"license": "openrail"}
|
2023-05-28T05:36:36+00:00
|
|
12c0322e9f3f9b3625a3718fc27edb8193e989cc
|
# Dataset Card for "viet_vlsp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/viet_vlsp
|
[
"region:us"
] |
2023-05-28T05:47:26+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24074955754.41959, "num_examples": 171441}, {"name": "validation", "num_bytes": 1053341643.8704103, "num_examples": 7501}], "download_size": 25080680499, "dataset_size": 25128297398.29}}
|
2023-05-28T09:08:57+00:00
|
31ed5706099e416e77040f0ac0d198430f89c135
|
zz17/medicare-test
|
[
"license:mit",
"region:us"
] |
2023-05-28T05:55:44+00:00
|
{"license": "mit"}
|
2023-05-28T05:58:51+00:00
|
|
acebe7788dfb55733c81cce5b121fae832b69eaf
|
# Dataset Card for "89cada8f"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/89cada8f
|
[
"region:us"
] |
2023-05-28T06:09:11+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1337, "dataset_size": 180}}
|
2023-05-28T06:09:12+00:00
|
d0b9f956ca5d2ed88f61e9dafa357f0720469e96
|
# Dataset Card for "midjourney-prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
wtcherr/midjourney-prompts
|
[
"region:us"
] |
2023-05-28T07:12:57+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15638175, "num_examples": 221743}, {"name": "validation", "num_bytes": 847580, "num_examples": 12318}, {"name": "test", "num_bytes": 870613, "num_examples": 12320}], "download_size": 10978861, "dataset_size": 17356368}}
|
2023-05-28T07:13:01+00:00
|
d2c3a8327534b29320c3ab638099c09c3b98fd84
|
# Dataset Card for "minipile_recreation_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
alexedw/minipile_recreation_tiny
|
[
"region:us"
] |
2023-05-28T07:23:16+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "cluster_id", "dtype": "int64"}, {"name": "distance", "dtype": "float32"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "__index_level_1__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 12992632, "num_examples": 10000}], "download_size": 7861091, "dataset_size": 12992632}}
|
2023-05-28T08:25:11+00:00
|
70bd6eb9c7369513174d60d346a2ef13031ffe84
|
This is the training dataset used to train the first version of the SpellMapper model.
It consists of 33'547'074 training examples.
Actually, [this checkpoint](https://huggingface.co/bene-ges/spellmapper_asr_customization_en) was trained only 1/3 of the full dataset.
Paper: [SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings](https://arxiv.org/abs/2306.02317)
## Citation
```bibtex
@inproceedings{inproceedings,
author = {Antonova, Alexandra and Bakhturina, Evelina and Ginsburg, Boris},
year = {2023},
month = {08},
pages = {1404-1408},
title = {SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings},
doi = {10.21437/Interspeech.2023-768}
}
```
|
bene-ges/spellmapper_en_train_v1
|
[
"task_categories:token-classification",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-4.0",
"arxiv:2306.02317",
"region:us"
] |
2023-05-28T07:40:07+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10M<n<100M"], "task_categories": ["token-classification"]}
|
2023-12-13T13:47:30+00:00
|
ad8b28bcfd94473b5c32d3720a1ad7d65dace3ab
|
Project Vulcan is an open-source initiative with an ambitious goal - to embed the industrial internet. We're living in a world where we face a plethora of problems. You'd be surprised to find out how many of these challenges are just about search, clustering, recommendation, or classification - things at which embeddings excel.
|
mitkox/vulcan
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-28T07:43:55+00:00
|
{"license": "apache-2.0"}
|
2023-05-28T07:44:38+00:00
|
1932cf29fc3185bcc85132ecba297f5162e83092
|
# Dataset Details
This dataset is a modified version of [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
This dataset is used in fine tuning [Panther](https://huggingface.co/Rardilit/Panther_v1) - an state of the art LLM funtuned on llama-7b pretrained model.
A very small portion i.e. 5.3% of prompts and responses were taken from this dataset to finetune and train [Panther](https://huggingface.co/Rardilit/Panther_v1)
## Dataset Details
### Dataset Structure
### Train
Train rows : 377k
### Validation
Validation rows : 20.3k
### Dataset Format
```python
input : "prompt"
output : "response"
```
## How to Use
```python
from datasets import load_dataset
dataset = load_dataset("Rardilit/Panther-dataset_v1")
```
|
Rardilit/Panther-dataset_v1
|
[
"task_categories:text-generation",
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:other",
"text generation",
"panther",
"region:us"
] |
2023-05-28T07:56:29+00:00
|
{"language": ["en"], "license": "other", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "conversational", "question-answering", "text2text-generation"], "pretty_name": "Panther", "tags": ["text generation", "panther"]}
|
2023-05-29T10:18:55+00:00
|
ea9557caba581969dd950135733e11b949458031
|
rezaakb/VizWiz-Classification
|
[
"license:mit",
"region:us"
] |
2023-05-28T08:30:10+00:00
|
{"license": "mit"}
|
2023-05-28T08:30:10+00:00
|
|
8fdf4e94826f5eac1af6c460986768f00424da96
|
aleph-null/thesis
|
[
"license:unknown",
"region:us"
] |
2023-05-28T08:41:21+00:00
|
{"license": "unknown"}
|
2023-06-30T06:21:36+00:00
|
|
5586046c49df88cf700ef60d75395a0c25ea558f
|
# tpu generated foxes
|
neggles/tpufoxes
|
[
"region:us"
] |
2023-05-28T09:05:14+00:00
|
{}
|
2023-05-28T09:14:59+00:00
|
87ba41f9ff587838bf26c06fe5746ed78c8f0fae
|
# Dataset Card for "MMLab-documentation-MMEngine"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mubarak-alketbi/MMLab-documentation-MMEngine
|
[
"region:us"
] |
2023-05-28T09:52:57+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 910848, "num_examples": 70}], "download_size": 332061, "dataset_size": 910848}}
|
2023-05-28T09:53:02+00:00
|
859fcfa43c37416fa5b6c1d0b0599e59c780d568
|
# Dataset Card for "MMLab-documentation-MMPreTrain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mubarak-alketbi/MMLab-documentation-MMPreTrain
|
[
"region:us"
] |
2023-05-28T09:53:03+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 486101, "num_examples": 103}], "download_size": 210125, "dataset_size": 486101}}
|
2023-05-28T09:53:09+00:00
|
d63165d20d1cdef8ddb92db5d2a9ead0730610eb
|
# Dataset Card for "MMLab-documentation-examples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mubarak-alketbi/MMLab-documentation-examples
|
[
"region:us"
] |
2023-05-28T09:53:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 605546, "num_examples": 718}], "download_size": 112311, "dataset_size": 605546}}
|
2023-05-28T09:53:14+00:00
|
f93a2e062850b31361e7c32542f36a407edfd1f3
|
# Dataset Card for "part_1_imda_100k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
averageandyyy/part_1_imda_100k
|
[
"region:us"
] |
2023-05-28T10:01:38+00:00
|
{"dataset_info": {"features": [{"name": "transcript", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "waveform", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 67028014130.12406, "num_examples": 100000}], "download_size": 16182561860, "dataset_size": 67028014130.12406}}
|
2023-05-28T11:19:10+00:00
|
51193e3ba36e97772ec438269816faad4936afc7
|
Preprocessed dataset, generated as described in the SAIL paper: https://arxiv.org/abs/2305.15225
|
lukasmoeller/sail_preprocessed
|
[
"arxiv:2305.15225",
"region:us"
] |
2023-05-28T10:05:21+00:00
|
{}
|
2023-05-30T16:11:55+00:00
|
3996204438abe9f59d97078fc0bba63569bfe380
|
# Dataset Card for d0rj/conv_ai_3_ru
## Dataset Description
- **Homepage:** https://github.com/aliannejadi/ClariQ
- **Repository:** https://github.com/aliannejadi/ClariQ
- **Paper:** https://arxiv.org/abs/2009.11352
### Dataset Summary
This is translated version of [conv_ai_3](https://huggingface.co/datasets/conv_ai_3) dataset to Russian language.
### Languages
Russian (translated from English).
## Dataset Structure
### Data Fields
- `topic_id`: the ID of the topic (`initial_request`).
- `initial_request`: the query (text) that initiates the conversation.
- `topic_desc`: a full description of the topic as it appears in the TREC Web Track data.
- `clarification_need`: a label from 1 to 4, indicating how much it is needed to clarify a topic. If an `initial_request` is self-contained and would not need any clarification, the label would be 1. While if a `initial_request` is absolutely ambiguous, making it impossible for a search engine to guess the user's right intent before clarification, the label would be 4.
- `facet_id`: the ID of the facet.
- `facet_desc`: a full description of the facet (information need) as it appears in the TREC Web Track data.
- `question_id`: the ID of the question..
- `question`: a clarifying question that the system can pose to the user for the current topic and facet.
- `answer`: an answer to the clarifying question, assuming that the user is in the context of the current row (i.e., the user's initial query is `initial_request`, their information need is `facet_desc`, and `question` has been posed to the user).
### Citation Information
@misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.
|
d0rj/conv_ai_3_ru
|
[
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:conv_ai_3",
"language:ru",
"license:unknown",
"evaluating-dialogue-systems",
"arxiv:2009.11352",
"region:us"
] |
2023-05-28T10:30:25+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["translated"], "language": ["ru"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["conv_ai_3"], "task_categories": ["conversational", "text-classification"], "task_ids": ["text-scoring"], "pretty_name": "conv_ai_3 (ru)", "tags": ["evaluating-dialogue-systems"], "dataset_info": {"features": [{"name": "topic_id", "dtype": "int32"}, {"name": "initial_request", "dtype": "string"}, {"name": "topic_desc", "dtype": "string"}, {"name": "clarification_need", "dtype": "int32"}, {"name": "facet_id", "dtype": "string"}, {"name": "facet_desc", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "config_name": "conv_ai_3", "splits": [{"name": "train", "num_examples": 9176}, {"name": "validation", "num_examples": 2313}]}}
|
2023-05-28T10:49:49+00:00
|
b09c387d69c528bbbc3e642d9e69c66680f38869
|
The data was generated by gpt-4, and therefore is subject to OpenAI ToS. The tool used to generate the data [airoboros](https://github.com/jondurbin/airoboros) is apache-2.
Specific areas of focus for this training data:
* trivia
* math
* nonsensical math
* coding
* closed context question answering
* closed context question answering, with multiple contexts to choose from as confounding factors
* writing
* multiple choice
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
jondurbin/airoboros-gpt4
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-05-28T10:47:57+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-06-22T14:00:49+00:00
|
c3461d9a8e924b23ceb7957e4301410b0487ac39
|
You must download the dataset files manually. You can visit [this](https://github.com/jbrownlee/Datasets/releases/tag/Flickr8k) page or run `download.sh` to get files.
After, you can load dataset by referencing the directory:
```py
import datasets
ds = datasets.load_dataset("atasoglu/flickr8k-dataset", data_dir="data")
print(ds)
```
```
DatasetDict({
train: Dataset({
features: ['image_id', 'image_path', 'captions'],
num_rows: 6000
})
test: Dataset({
features: ['image_id', 'image_path', 'captions'],
num_rows: 1000
})
validation: Dataset({
features: ['image_id', 'image_path', 'captions'],
num_rows: 1000
})
})
```
I don't own the copyright of the images. Please [visit](https://forms.illinois.edu/sec/1713398) for more.
|
atasoglu/flickr8k-dataset
|
[
"task_categories:image-to-text",
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
2023-05-28T10:52:48+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["image-to-text", "text-to-image"], "pretty_name": "flickr8k"}
|
2023-05-28T11:12:43+00:00
|
c116076c5a5d3d848d14c4fbcc32cd89ede43204
|
# Dataset Card for "piqa_ru"
This is translated version of [piqa dataset](https://huggingface.co/datasets/piqa) into Russian.
|
d0rj/piqa_ru
|
[
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:piqa",
"language:ru",
"license:unknown",
"region:us"
] |
2023-05-28T11:22:41+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["translated"], "language": ["ru"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["piqa"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "piqa", "pretty_name": "Physical Interaction: Question Answering (ru)", "dataset_info": {"features": [{"name": "goal", "dtype": "string"}, {"name": "sol1", "dtype": "string"}, {"name": "sol2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 7787368, "num_examples": 16113}, {"name": "test", "num_bytes": 1443681, "num_examples": 3084}, {"name": "validation", "num_bytes": 877142, "num_examples": 1838}], "download_size": 5253717, "dataset_size": 10108191}}
|
2023-06-05T13:06:07+00:00
|
592796878678f6e290c06e54f98ba9a9947788b5
|
hasibul1ah/bangla-quote
|
[
"region:us"
] |
2023-05-28T11:26:55+00:00
|
{}
|
2023-05-28T12:19:51+00:00
|
|
def4c0831fd80df4e0711c3c6590210ba52e7345
|
SilpaCS/kneeosteoarthritis
|
[
"task_categories:image-classification",
"language:en",
"region:us"
] |
2023-05-28T11:47:37+00:00
|
{"language": ["en"], "task_categories": ["image-classification"]}
|
2023-05-28T11:51:06+00:00
|
|
6a9451624798936dc2b12ce000634e5de4e46378
|
https://github.com/keimaruO/YTSceneSearch
|
keimaru/JP_Holo_Subtitles
|
[
"license:mit",
"region:us"
] |
2023-05-28T12:51:36+00:00
|
{"license": "mit"}
|
2023-05-29T09:35:44+00:00
|
1fbc2a801d1b223bb2003e5ccfeff66b0537127a
|
inventivework/react
|
[
"region:us"
] |
2023-05-28T13:29:47+00:00
|
{}
|
2023-05-28T13:52:13+00:00
|
|
4b6504de23823540466aa58cb5d0d01c23ee0daf
|
# Dataset Card for "futurama-blip-captions-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Norod78/futurama-blip2-captions-512
|
[
"language:en",
"region:us"
] |
2023-05-28T13:48:57+00:00
|
{"language": "en", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 233975670.0, "num_examples": 834}], "download_size": 233996558, "dataset_size": 233975670.0}}
|
2023-07-13T10:27:07+00:00
|
e18f7332efa6574e950ce7eb58cd05776e47b785
|
Amit/ddpm-butterflies-128
|
[
"license:unknown",
"region:us"
] |
2023-05-28T14:25:33+00:00
|
{"license": "unknown"}
|
2023-05-28T14:25:33+00:00
|
|
10b3dfe9cfce41ce93350fdcb162d7fdf8748f96
|
# Dataset Card for "md_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cburger/md_cleaned
|
[
"region:us"
] |
2023-05-28T14:32:22+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": " Allergy / Immunology", "1": " Autopsy", "2": " Bariatrics", "3": " Cardiovascular / Pulmonary", "4": " Chiropractic", "5": " Consult - History and Phy.", "6": " Cosmetic / Plastic Surgery", "7": " Dentistry", "8": " Dermatology", "9": " Diets and Nutritions", "10": " Discharge Summary", "11": " ENT - Otolaryngology", "12": " Emergency Room Reports", "13": " Endocrinology", "14": " Gastroenterology", "15": " General Medicine", "16": " Hematology - Oncology", "17": " Hospice - Palliative Care", "18": " IME-QME-Work Comp etc.", "19": " Lab Medicine - Pathology", "20": " Letters", "21": " Nephrology", "22": " Neurology", "23": " Neurosurgery", "24": " Obstetrics / Gynecology", "25": " Office Notes", "26": " Ophthalmology", "27": " Orthopedic", "28": " Pain Management", "29": " Pediatrics - Neonatal", "30": " Physical Medicine - Rehab", "31": " Podiatry", "32": " Psychiatry / Psychology", "33": " Radiology", "34": " Rheumatology", "35": " SOAP / Chart / Progress Notes", "36": " Sleep Medicine", "37": " Speech - Language", "38": " Surgery", "39": " Urology"}}}}], "splits": [{"name": "train", "num_bytes": 15217210, "num_examples": 4948}], "download_size": 7196712, "dataset_size": 15217210}}
|
2023-05-28T14:36:29+00:00
|
8219486a32061e58528a30834e26d1b2b99513d0
|
Hobis/bark-polish-semantic-wav-training
|
[
"language:pl",
"region:us"
] |
2023-05-28T14:42:22+00:00
|
{"language": ["pl"]}
|
2023-05-28T14:55:49+00:00
|
|
cc40ad3e30039e4ec6d7f84f47874426439b0f28
|
# Dataset Card for "xview_captions_gt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Braddy/xview_captions_gt
|
[
"region:us"
] |
2023-05-28T14:49:00+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "sequence": "string"}, {"name": "file_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5117788.0, "num_examples": 47}], "download_size": 5117884, "dataset_size": 5117788.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T11:44:19+00:00
|
a46edd8d636029b31dec9382d9c033b72bf7da26
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://www.circl.lu/opendata/datasets/circl-ail-dataset-01/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** @Electronic{CIRCL-AILDS2019, author = {Vincent Falconieri}, month = {07}, year = {2019}, title = {CIRCL Images AIL Dataset}, organization = {CIRCL}, address = {CIRCL - Computer Incident Response Center Luxembourg c/o "security made in Lëtzebuerg" (SMILE) g.i.e. 122, rue Adolphe Fischer L-1521 Luxembourg Grand-Duchy of Luxembourg}, url = {https://www.circl.lu/opendata/circl-ail-dataset-01/}, abstract = {This dataset is named circl-ail-dataset-01 and is composed of Tor hidden services websites screenshots. Around 37000+ pictures are in this dataset to date.}, }
### Dataset Summary
---
task_categories:
- image-classification
pretty_name: Subset of circl-ail-dataset-01
size_categories:
- 1K<n<10K
---
This is a subset of circl-ail-dataset-01 dataset with these labels ["marketplace","forum","general"] each label has 1000 images
circl-ail-dataset-01
This dataset is named circl-ail-dataset-01 and is composed of AIL’s scraped onion websites. Around 37500 pictures are in this dataset to date.
Only one label-classification (DataTurks direct output) is provided along with the dataset. This classification is per part and will be improved and updated as soon as classification operations had been achieved.
Direct link : https://www.circl.lu/opendata/datasets/circl-ail-dataset-01/
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
https://www.circl.lu/opendata/datasets/circl-ail-dataset-01/
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
Abhilashvj/CIRCL_website_subset
|
[
"region:us"
] |
2023-05-28T15:36:05+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "forum", "1": "general", "2": "marketplace"}}}}], "splits": [{"name": "train", "num_bytes": 2109417862.525, "num_examples": 3005}, {"name": "test", "num_bytes": 59369011.0, "num_examples": 81}], "download_size": 1946901450, "dataset_size": 2168786873.525}}
|
2023-05-28T15:49:26+00:00
|
6937c1c3bad5076de9c32fb0e5faef5f1be2e5f8
|
qanh308/autotrain-data-AutoTrainTest
|
[
"license:mit",
"region:us"
] |
2023-05-28T15:49:46+00:00
|
{"license": "mit"}
|
2023-05-28T15:49:47+00:00
|
|
1826e202d4f5f3b0c8b2dbc5c8c94bd6ca1d1d75
|
# Dataset Card for "hanon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roszcz/hanon
|
[
"region:us"
] |
2023-05-28T16:06:07+00:00
|
{"dataset_info": {"features": [{"name": "notes", "struct": [{"name": "end", "sequence": "float64"}, {"name": "pitch", "sequence": "int64"}, {"name": "start", "sequence": "float64"}, {"name": "velocity", "sequence": "int64"}]}, {"name": "label", "dtype": "string"}, {"name": "control_changes", "struct": [{"name": "number", "sequence": "int64"}, {"name": "time", "sequence": "float64"}, {"name": "value", "sequence": "int64"}]}, {"name": "midi_filename", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4302584, "num_examples": 680}], "download_size": 792983, "dataset_size": 4302584}}
|
2023-05-28T16:12:09+00:00
|
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