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2cb89c6fbe4d9ca3ac1d59a32b992b4e727ce339 | wizrb47/test-json | [
"license:gpl-3.0",
"region:us"
]
| 2023-01-02T17:34:55+00:00 | {"license": "gpl-3.0"} | 2023-01-02T17:35:37+00:00 |
|
879b465814c965cb874747a97e58d14e7e9f7f0f | # Dataset Card for "blip-preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | peeper/blip-preprocessed | [
"region:us"
]
| 2023-01-02T17:45:30+00:00 | {"dataset_info": {"features": [{"name": "labels", "sequence": "int64"}, {"name": "pixel_values", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 7522975512, "num_examples": 4238}, {"name": "test", "num_bytes": 2508250212, "num_examples": 1413}], "download_size": 2847165063, "dataset_size": 10031225724}} | 2023-01-03T10:37:36+00:00 |
b4257d1956ca01a9584ce1e18505a2053dcf6903 | javisanxe/jsanxe | [
"license:unknown",
"region:us"
]
| 2023-01-02T17:52:29+00:00 | {"license": "unknown"} | 2023-01-02T17:52:29+00:00 |
|
b0b0fd8e86e0179c81a84e1651d6f4502230ce5e |
# Shylily Character Embedding / Textual Inversion
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/shylily/resolve/main/shylily_showcase.png"/>
## Disclaimer
This is an embedding based on the VTuber Shylily, which can be found / watched on Twitch:
https://www.twitch.tv/shylily
## Usage
To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
To use it in a prompt: ```"shy_lily"```
Personally, I would recommend to use my embeddings with a strength of 0.8, like ```"(shy_lily:0.8)"```, but in this case the embedding basically works on almost all strength.
I hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: "Nerfgun3#7508"
## License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | Nerfgun3/shylily | [
"language:en",
"license:creativeml-openrail-m",
"stable-diffusion",
"text-to-image",
"image-to-image",
"region:us"
]
| 2023-01-02T18:45:06+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "thumbnail": "https://huggingface.co/datasets/Nerfgun3/shylily/resolve/main/shylily_showcase.png", "tags": ["stable-diffusion", "text-to-image", "image-to-image"], "inference": false} | 2023-01-02T18:49:16+00:00 |
7321296d0db2953997096254d43abb79d5dd0d3c | # Dataset Card for "vitmae-roberta-processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | peeper/vitmae-roberta-processed | [
"region:us"
]
| 2023-01-02T19:43:21+00:00 | {"dataset_info": {"features": [{"name": "labels", "sequence": "int64"}, {"name": "pixel_values", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 2567566872, "num_examples": 4238}, {"name": "test", "num_bytes": 856057572, "num_examples": 1413}], "download_size": 1000718544, "dataset_size": 3423624444}} | 2023-01-02T19:45:13+00:00 |
62093e4100fd5c64090ec50e7e366300cef776f1 | # Dataset Card for "es_Nautical_Text_NGRAMS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | alvarochelo/es_Nautical_Text_NGRAMS | [
"region:us"
]
| 2023-01-02T20:00:38+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 473, "num_examples": 1}], "download_size": 0, "dataset_size": 473}} | 2023-01-03T21:46:45+00:00 |
984e8a95dd5a663c67e92a0abb4fd549c024b177 |
### Roboflow Dataset Page
[https://universe.roboflow.com/riis/aerial-sheep/dataset/1](https://universe.roboflow.com/riis/aerial-sheep/dataset/1?ref=roboflow2huggingface)
### Dataset Labels
```
['sheep']
```
### Citation
```
@misc{ aerial-sheep_dataset,
title = { Aerial Sheep Dataset },
type = { Open Source Dataset },
author = { Riis },
howpublished = { \\url{ https://universe.roboflow.com/riis/aerial-sheep } },
url = { https://universe.roboflow.com/riis/aerial-sheep },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { jun },
note = { visited on 2023-01-02 },
}
```
### License
Public Domain
### Dataset Summary
This dataset was exported via roboflow.com on December 2, 2022 at 4:47 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 4133 images.
Sheep are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 600x600 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
* 50% probability of horizontal flip
* 50% probability of vertical flip
* Randomly crop between 0 and 20 percent of the image
* Random brigthness adjustment of between -15 and +15 percent
* Random exposure adjustment of between -10 and +10 percent
| keremberke/aerial-sheep-object-detection | [
"task_categories:object-detection",
"roboflow",
"region:us"
]
| 2023-01-02T20:17:28+00:00 | {"task_categories": ["object-detection"], "tags": ["roboflow"]} | 2023-01-05T08:02:23+00:00 |
c32ff18f192978e4644325109788dd8383da6825 | reshinthadith/synthetic_program_synthesis_python_1M | [
"license:mit",
"region:us"
]
| 2023-01-02T20:18:40+00:00 | {"license": "mit"} | 2023-01-02T20:21:19+00:00 |
|
c85ee099f4a4ef35662c9745c3104d14504a9be0 | # Dataset Card for MiningLegalArguments
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/trusthlt/mining-legal-arguments)
- **Repository:**
- **Paper:** [ArXiv](https://arxiv.org/pdf/2208.06178.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JoelNiklaus](https://github.com/JoelNiklaus) for adding this dataset.
| joelniklaus/mining_legal_arguments_agent | [
"license:apache-2.0",
"arxiv:2208.06178",
"region:us"
]
| 2023-01-02T20:42:53+00:00 | {"license": "apache-2.0"} | 2023-01-02T20:51:41+00:00 |
1e659f6090028fa1d8eeedba98ada104bf4bfc98 | # Dataset Card for MiningLegalArguments
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/trusthlt/mining-legal-arguments)
- **Repository:**
- **Paper:** [ArXiv](https://arxiv.org/pdf/2208.06178.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JoelNiklaus](https://github.com/JoelNiklaus) for adding this dataset.
| joelniklaus/mining_legal_arguments_argType | [
"license:apache-2.0",
"arxiv:2208.06178",
"region:us"
]
| 2023-01-02T20:44:27+00:00 | {"license": "apache-2.0"} | 2023-01-02T20:51:23+00:00 |
b451718aa256953c500322bdbacbf3aee2756004 |
Description: The National Health and Nutrition Examination Survey (NHANES) provides data and have considerable potential to study the health and environmental exposure of the non-institutionalized US population. However, as NHANES data are plagued with multiple inconsistencies, processing these data is required before deriving new insights through large-scale analyses. Thus, we developed a set of curated and unified datasets by merging 614 separate files and harmonizing unrestricted data across NHANES III (1988-1994) and Continuous (1999-2018), totaling 135,310 participants and 5,078 variables. The variables convey
1. demographics (281 variables),
2. dietary consumption (324 variables),
3. physiological functions (1,027 variables),
4. occupation (61 variables),
5. questionnaires (1444 variables, e.g., physical activity, medical conditions, diabetes, reproductive health, blood pressure and cholesterol, early childhood),
6. medications (29 variables),
7. mortality information linked from the National Death Index (15 variables),
8. survey weights (857 variables),
9. environmental exposure biomarker measurements (598 variables), and
10. chemical comments indicating which measurements are below or above the lower limit of detection (505 variables).
csv Data Record: The curated NHANES datasets and the data dictionaries includes 23 .csv files and 1 excel file.
- The curated NHANES datasets involves 20 .csv formatted files, two for each module with one as the uncleaned version and the other as the cleaned version. The modules are labeled as the following: 1) mortality, 2) dietary, 3) demographics, 4) response, 5) medications, 6) questionnaire, 7) chemicals, 8) occupation, 9) weights, and 10) comments.
- "dictionary\_nhanes.csv" is a dictionary that lists the variable name, description, module, category, units, CAS Number, comment use, chemical family, chemical family shortened, number of measurements, and cycles available for all 5,078 variables in NHANES.
- "dictionary\_harmonized\_categories.csv" contains the harmonized categories for the categorical variables.
- “dictionary\_drug\_codes.csv” contains the dictionary for descriptors on the drugs codes.
- “nhanes\_inconsistencies\_documentation.xlsx” is an excel file that contains the cleaning documentation, which records all the inconsistencies for all affected variables to help curate each of the NHANES modules.
R Data Record: For researchers who want to conduct their analysis in the R programming language, only cleaned NHANES modules and the data dictionaries can be downloaded as a .zip file which include an .RData file and an .R file.
- “w - nhanes_1988\_2018.RData” contains all the aforementioned datasets as R data objects. We make available all R scripts on customized functions that were written to curate the data.
- “m - nhanes\_1988\_2018.R” shows how we used the customized functions (i.e. our pipeline) to curate the original NHANES data.
Example starter codes: The set of starter code to help users conduct exposome analysis consists of four R markdown files (.Rmd). We recommend going through the tutorials in order.
- “example\_0 - merge\_datasets\_together.Rmd” demonstrates how to merge the curated NHANES datasets together.
- “example\_1 - account\_for\_nhanes_design.Rmd” demonstrates how to conduct a linear regression model, a survey-weighted regression model, a Cox proportional hazard model, and a survey-weighted Cox proportional hazard model.
- “example\_2 - calculate\_summary\_statistics.Rmd” demonstrates how to calculate summary statistics for one variable and multiple variables with and without accounting for the NHANES sampling design.
- “example\_3 - run\_multiple\_regressions.Rmd” demonstrates how run multiple regression models with and without adjusting for the sampling design. | nguyenvy/cleaned_nhanes_1988_2018 | [
"license:cc-by-4.0",
"doi:10.57967/hf/0260",
"region:us"
]
| 2023-01-02T20:50:25+00:00 | {"license": "cc-by-4.0"} | 2023-07-27T15:28:51+00:00 |
314a8abe30c8274c771070e27daffeb00a8ac76a |
# Yor Forger Character Embedding / Textual Inversion
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/yor_forger/resolve/main/yor_forger_showcase.png"/>
## Disclaimer
This is an embedding based on the Anime Character Yor Forger from Spy x Family
## Usage
To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
To use it in a prompt: ```"yor_forger"```
Personally, I would recommend to use my embeddings with a strength of 0.8, like ```"(yor_forger:0.8)"```, but in this case the embedding basically works on almost all strength.
I hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: "Nerfgun3#7508"
## License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | Nerfgun3/yor_forger | [
"language:en",
"license:creativeml-openrail-m",
"stable-diffusion",
"text-to-image",
"image-to-image",
"region:us"
]
| 2023-01-02T21:02:24+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "thumbnail": "https://huggingface.co/datasets/Nerfgun3/yor_forger/resolve/main/yor_forger_showcase.png", "tags": ["stable-diffusion", "text-to-image", "image-to-image"], "inference": false} | 2023-01-02T21:08:45+00:00 |
fb24e6030d3545667be53171fc8c296848bf07da | Source : https://github.com/allisonhorst/palmerpenguins
Data originally published in :
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. https://doi.org/10.1371/journal.pone.0090081 | methodidacte/penguins | [
"license:unknown",
"region:us"
]
| 2023-01-02T21:29:37+00:00 | {"license": "unknown"} | 2023-01-02T21:38:31+00:00 |
a3c393f5d103fd0c516374e4fdff676c8176dcb1 | theatticusproject/cuad | [
"license:cc-by-4.0",
"region:us"
]
| 2023-01-02T21:54:27+00:00 | {"license": "cc-by-4.0"} | 2023-01-02T22:36:46+00:00 |
|
da0e30d826cc12c73c21cded75aecf1e30410d11 |
Dataset of Goya Paintings | BirdL/Goya-Dataset | [
"license:other",
"region:us"
]
| 2023-01-02T22:19:48+00:00 | {"license": "other"} | 2023-01-07T20:48:04+00:00 |
37d5c3b95d18dcd8404cc5ce3fd5069be062392f | theatticusproject/maud | [
"license:cc-by-4.0",
"region:us"
]
| 2023-01-02T22:44:50+00:00 | {"license": "cc-by-4.0"} | 2023-01-02T22:50:04+00:00 |
|
cb454d8fb5ee6d9bc82a836395f85553987f87d5 | # Dataset Card for "t5-small-october-wikipedia-2022-tokenized-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Tristan/t5-small-october-wikipedia-2022-tokenized-512 | [
"region:us"
]
| 2023-01-02T23:03:59+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 30029601900, "num_examples": 9737225}], "download_size": 9411819822, "dataset_size": 30029601900}} | 2023-01-02T23:17:40+00:00 |
9c8ca9cb5f1f6a7454465edd2c1a53dea3eb9298 | # Dataset Card for "bookcorpus_small_compact_1024_n7"
448 samples after explode graphs
`gdown 13QYq8op5XHlhL_qvdQbpYxo-pR5uAwcO` to download the assciated graph pickle
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_small_compact_1024_n7 | [
"region:us"
]
| 2023-01-03T00:07:49+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 81072, "num_examples": 7}], "download_size": 42603, "dataset_size": 81072}} | 2023-01-30T19:12:34+00:00 |
509d0127abfb348abb94175a5cf59bef7199f9b0 | # Dataset Card for "bookcorpus_small_compact_1024_shard0_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_small_compact_1024_n7_meta | [
"region:us"
]
| 2023-01-03T00:21:29+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 795771, "num_examples": 7}], "download_size": 260012, "dataset_size": 795771}} | 2023-01-05T00:54:07+00:00 |
1e8b886a454125e7c7488630971e012264d8fb9d |
# Dataset Card for Bernice Pre-train Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** N/A
- **Repository:** https://github.com/JHU-CLSP/Bernice-Twitter-encoder
- **Paper:** _Bernice: A Multilingual Pre-trained Encoder for Twitter_ at [EMNLP 2022](https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.415)
- **Leaderboard:** N/A
- **Point of Contact:** Alexandra DeLucia aadelucia (at) jhu.edu
### Dataset Summary
Tweet IDs for the 2.5 billion multilingual tweets used to train Bernice, a Twitter encoder.
Read the paper [here](https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.415).
The tweets are from the public 1% Twitter API stream from January 2016 to December 2021.
Twitter-provided language metadata is provided with the tweet ID. The data contains 66 unique languages, as identified by [ISO 639 language codes](https://www.wikiwand.com/en/List_of_ISO_639-1_codes), including `und` for undefined languages.
Tweets need to be re-gathered via the Twitter API. We suggest [Hydrator](https://github.com/DocNow/hydrator) or [tweepy](https://www.tweepy.org/).
To load with HuggingFace:
```python
from datasets import load_dataset
dataset = load_dataset("jhu-clsp/bernice-pretrain-data")
for i, row in enumerate(dataset["train"]):
print(row)
if i > 10:
break
```
If you only want Indic languages, use
```python
dataset = load_dataset("jhu-clsp/bernice-pretrain-data", "indic")
```
### Supported Tasks and Leaderboards
N/A
### Languages
65 languages (ISO 639 codes shown below), plus an `und` (undefined) category.
All language identification provided by Twitter API.
| | | | | | | |
|----|-----|----|----|----|-----|----|
| en | ru | ht | zh | bn | ps | lt |
| es | bo | ur | ta | sr | ckb | km |
| pt | it | sv | ro | bg | si | dv |
| ja | th | ca | no | mr | hy | lo |
| ar | de | el | uk | ml | or | ug |
| in | hi | fi | cy | is | pa | |
| ko | pl | cs | ne | te | am | |
| tr | nl | iw | hu | gu | sd | |
| fr | fa | da | eu | kn | my | |
| tl | et | vi | sl | lv | ka | |
## Dataset Structure
### Data Instances
Data is provided in gzip'd files organized by year and month of tweet origin.
Tweets are one per line, with fields separated by tabs.
### Data Fields
* `tweet ID`: ID of tweet
* `lang`: ISO 639 code of language, provided by Twitter metadata. Accuracy of label is not known.
* `year`: Year tweet was created. Year is also provided in the file names.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
Data was gathered to support the training of Bernice, a multilingual pre-trained Twitter encoder.
### Source Data
#### Initial Data Collection and Normalization
Data was gathered via the Twitter API public 1% stream from January 2016 through December 2021.
Tweets with less than three non-username or URL space-delimited words were removed.
All usernames and URLs were replaced with `@USER` and `HTTPURL`, respectively.
#### Who are the source language producers?
Data was produced by users on Twitter.
### Annotations
N/A
### Personal and Sensitive Information
As per Twitter guidelines, only tweet IDs and not full tweets are shared.
Tweets will only be accessible if user has not removed their account (or been banned), tweets were deleted or removed, or a user changed their account access to private.
## 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
Dataset gathered and processed by Mark Dredze, Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, and Philip Resnik.
### Licensing Information
MIT
### Citation Information
Please cite the Bernice paper if you use this dataset:
> Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, Philip Resnik, and Mark Dredze. 2022. Bernice: A Multilingual Pre-trained Encoder for Twitter. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6191–6205, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
### Contributions
Dataset uploaded by [@AADeLucia](https://github.com/AADeLucia).
| jhu-clsp/bernice-pretrain-data | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1B<n<10B",
"source_datasets:original",
"language:en",
"language:es",
"language:pt",
"language:ja",
"language:ar",
"language:in",
"language:ko",
"language:tr",
"language:fr",
"language:tl",
"language:ru",
"language:it",
"language:th",
"language:de",
"language:hi",
"language:pl",
"language:nl",
"language:fa",
"language:et",
"language:ht",
"language:ur",
"language:sv",
"language:ca",
"language:el",
"language:fi",
"language:cs",
"language:iw",
"language:da",
"language:vi",
"language:zh",
"language:ta",
"language:ro",
"language:no",
"language:uk",
"language:cy",
"language:ne",
"language:hu",
"language:eu",
"language:sl",
"language:lv",
"language:lt",
"language:bn",
"language:sr",
"language:bg",
"language:mr",
"language:ml",
"language:is",
"language:te",
"language:gu",
"language:kn",
"language:ps",
"language:ckb",
"language:si",
"language:hy",
"language:or",
"language:pa",
"language:am",
"language:sd",
"language:my",
"language:ka",
"language:km",
"language:dv",
"language:lo",
"language:ug",
"language:bo",
"license:mit",
"twitter",
"slang",
"code switch",
"social",
"social media",
"region:us"
]
| 2023-01-03T01:48:26+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en", "es", "pt", "ja", "ar", "in", "ko", "tr", "fr", "tl", "ru", "it", "th", "de", "hi", "pl", "nl", "fa", "et", "ht", "ur", "sv", "ca", "el", "fi", "cs", "iw", "da", "vi", "zh", "ta", "ro", false, "uk", "cy", "ne", "hu", "eu", "sl", "lv", "lt", "bn", "sr", "bg", "mr", "ml", "is", "te", "gu", "kn", "ps", "ckb", "si", "hy", "or", "pa", "am", "sd", "my", "ka", "km", "dv", "lo", "ug", "bo"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": ["1B<n<10B"], "source_datasets": ["original"], "task_categories": ["other"], "task_ids": [], "pretty_name": "Bernice Pretrain Data", "tags": ["twitter", "slang", "code switch", "social", "social media"]} | 2023-01-03T21:28:00+00:00 |
6f2d3885aed0fc6f467ce40d00373e0f17ba246b | # Dataset Card for "origin_added_korquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lim4349/origin_added_korquad | [
"region:us"
]
| 2023-01-03T02:24:40+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "answers", "struct": [{"name": "text", "sequence": "string"}, {"name": "answer_start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 83769368, "num_examples": 57923}, {"name": "validation", "num_bytes": 9244735, "num_examples": 6436}], "download_size": 57373216, "dataset_size": 93014103}} | 2023-01-03T02:37:00+00:00 |
821d77a9210bf7f1c5f595f8b900e5dd1b422176 | # Dataset Card for "korquad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lim4349/korquad | [
"region:us"
]
| 2023-01-03T02:38:32+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "answers", "struct": [{"name": "text", "sequence": "string"}, {"name": "answer_start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 75266074, "num_examples": 54366}, {"name": "validation", "num_bytes": 8358264, "num_examples": 6041}], "download_size": 51472501, "dataset_size": 83624338}} | 2023-01-03T02:39:12+00:00 |
72a73be2064bae0109a168fb8355fcf4ca3bfe2e | # Dataset Card for "AToMiC-Texts-Mapped"
## Dataset Description
- **Homepage:** [AToMiC homepage](https://trec-atomic.github.io/)
- **Source:** [WIT](https://github.com/google-research-datasets/wit)
- **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://arxiv.org/abs/2103.01913)
### Languages
This dataset only contains English in Wikipedia (parsed from the 20221101 XML dump).
### Data Instances
Each instance is a section of a Wikipedia page. We also provide its page-level information, and associated information such as categories and media.
The `source_id` can be mapped back to the instance in the original [WIT instance](https://github.com/google-research-datasets/wit/blob/main/DATA.md).
Notice that the WIT dataset is crawled from the earlier version of Wikipedia (2020-08-30).
The WIT dataset is mapped to the new dump by pure BM25 matching with [Anserini](https://github.com/castorini/anserini).
### Intended Usage
1. Text collection for Image-to-Text retrieval
2. Language model pretraining
3. Document classification
### Licensing Information
[CC BY-SA 4.0 international license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
TBA
### Acknowledgement
Thanks to:
[mwparserfromhell](https://github.com/earwig/mwparserfromhell)
[Datasets](https://github.com/huggingface/datasets)
[Anserini](https://github.com/castorini/anserini)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TREC-AToMiC/AToMiC-Texts-v0.2 | [
"size_categories:100M<n<1B",
"license:cc-by-sa-4.0",
"arxiv:2103.01913",
"region:us"
]
| 2023-01-03T04:29:46+00:00 | {"license": "cc-by-sa-4.0", "size_categories": ["100M<n<1B"], "dataset_info": {"features": [{"name": "text_id", "dtype": "string"}, {"name": "page_url", "dtype": "string"}, {"name": "page_title", "dtype": "string"}, {"name": "section_title", "dtype": "string"}, {"name": "context_page_description", "dtype": "string"}, {"name": "context_section_description", "dtype": "string"}, {"name": "media", "sequence": "string"}, {"name": "hierachy", "sequence": "string"}, {"name": "category", "sequence": "string"}, {"name": "source_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14378574060.336058, "num_examples": 10134744}], "download_size": 6408012391, "dataset_size": 14378574060.336058}} | 2023-02-14T21:30:37+00:00 |
2f1a77540906c5230aad77cb8b24f8e510024426 |
AI generated images that have relatively obvious issues
target tag: bad anatomy | trojblue/bad_ai | [
"license:gpl",
"region:us"
]
| 2023-01-03T05:18:06+00:00 | {"license": "gpl"} | 2023-03-13T00:58:12+00:00 |
be60029f2bc1489690db6eb64d92dffa30f7797c | boys <3 | gweg/boys | [
"region:us"
]
| 2023-01-03T06:45:26+00:00 | {"pretty_name": "Game boys genus male "} | 2023-04-14T17:57:18+00:00 |
1a5624ce04940147b55612539e157937b1e577d4 |
# Dataset Card for MNIST
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://yann.lecun.com/exdb/mnist/
- **Repository:**
- **Paper:** MNIST handwritten digit database by Yann LeCun, Corinna Cortes, and CJ Burges
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The MNIST dataset consists of 70,000 28x28 black-and-white images of handwritten digits extracted from two NIST databases. There are 60,000 images in the training dataset and 10,000 images in the validation dataset, one class per digit so a total of 10 classes, with 7,000 images (6,000 train images and 1,000 test images) per class.
Half of the image were drawn by Census Bureau employees and the other half by high school students (this split is evenly distributed in the training and testing sets).
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-mnist).
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its label:
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x276021F6DD8>,
'label': 5
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `label`: an integer between 0 and 9 representing the digit.
### Data Splits
The data is split into training and test set. All the images in the test set were drawn by different individuals than the images in the training set. The training set contains 60,000 images and the test set 10,000 images.
## Dataset Creation
### Curation Rationale
The MNIST database was created to provide a testbed for people wanting to try pattern recognition methods or machine learning algorithms while spending minimal efforts on preprocessing and formatting. Images of the original dataset (NIST) were in two groups, one consisting of images drawn by Census Bureau employees and one consisting of images drawn by high school students. In NIST, the training set was built by grouping all the images of the Census Bureau employees, and the test set was built by grouping the images form the high school students.
The goal in building MNIST was to have a training and test set following the same distributions, so the training set contains 30,000 images drawn by Census Bureau employees and 30,000 images drawn by high school students, and the test set contains 5,000 images of each group. The curators took care to make sure all the images in the test set were drawn by different individuals than the images in the training set.
### Source Data
#### Initial Data Collection and Normalization
The original images from NIST were size normalized to fit a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels (i.e., pixels don't simply have a value of black and white, but a level of greyness from 0 to 255) as a result of the anti-aliasing technique used by the normalization algorithm. The images were then centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.
#### Who are the source language producers?
Half of the source images were drawn by Census Bureau employees, half by high school students. According to the dataset curator, the images from the first group are more easily recognizable.
### Annotations
#### Annotation process
The images were not annotated after their creation: the image creators annotated their images with the corresponding label after drawing them.
#### Who are the annotators?
Same as the source data creators.
### 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
Chris Burges, Corinna Cortes and Yann LeCun
### Licensing Information
MIT Licence
### Citation Information
```
@article{lecun2010mnist,
title={MNIST handwritten digit database},
author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
volume={2},
year={2010}
}
```
### Contributions
Thanks to [@sgugger](https://github.com/sgugger) for adding this dataset. | mqddb/test-dataset | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-nist",
"language:en",
"license:mit",
"region:us"
]
| 2023-01-03T06:54:16+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other-nist"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "mnist", "pretty_name": "MNIST", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6", "7": "7", "8": "8", "9": "9"}}}}], "config_name": "mnist", "splits": [{"name": "train", "num_bytes": 17470848, "num_examples": 60000}, {"name": "test", "num_bytes": 2916440, "num_examples": 10000}], "download_size": 11594722, "dataset_size": 20387288}} | 2023-01-03T07:08:03+00:00 |
e17f3184bbedc139dac614088b263a7ab8d79a0b | abirmunna/BSLWord40 | [
"license:creativeml-openrail-m",
"doi:10.57967/hf/0245",
"region:us"
]
| 2023-01-03T07:16:05+00:00 | {"license": "creativeml-openrail-m", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Aaj", "1": "Basha", "2": "Biyog", "3": "Bondhu", "4": "Darano", "5": "Darao", "6": "Desh", "7": "Ekhane", "8": "Gun", "9": "Kichuta", "10": "Kothay", "11": "Onurodh", "12": "Shahajjo", "13": "She", "14": "Shomoi", "15": "Shundor", "16": "Sir", "17": "Tara", "18": "Tumi", "19": "bagh", "20": "bouddho", "21": "chamra", "22": "girja", "23": "hockey", "24": "jail", "25": "keram", "26": "piano", "27": "puru", "28": "shomajkollan", "29": "shotto"}}}}], "splits": [{"name": "train", "num_bytes": 2192638367.4, "num_examples": 1200}], "download_size": 2042629430, "dataset_size": 2192638367.4}} | 2023-01-03T08:30:39+00:00 |
|
4d6b88706fed4d253c7e73e23d36ec4a3570387f | # Dataset Card for "sv_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hellosimple/sv_corpora_parliament_processed | [
"region:us"
]
| 2023-01-03T09:00:09+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 292351437, "num_examples": 1892723}], "download_size": 161955537, "dataset_size": 292351437}} | 2023-01-03T09:09:32+00:00 |
ac23016f92d5a58164e09b94825242d0422a3018 | # Dataset Card for "sidewalk-imagery2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | chiHang/sidewalk-imagery2 | [
"region:us"
]
| 2023-01-03T09:21:27+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3138386.0, "num_examples": 10}], "download_size": 3139599, "dataset_size": 3138386.0}} | 2023-01-03T09:21:31+00:00 |
7ed3f4eb026a81d35ede261a816de31cb6f7b19c | lolcharacters/luxanna_crownguard | [
"license:wtfpl",
"region:us"
]
| 2023-01-03T09:24:44+00:00 | {"license": "wtfpl"} | 2023-01-03T09:25:18+00:00 |
|
f295a9c04c6c9cfe5f4d51780e5c0b3367b662fb | cagrigungor/cagri-ankara | [
"license:apache-2.0",
"region:us"
]
| 2023-01-03T10:25:56+00:00 | {"license": "apache-2.0"} | 2023-01-03T10:26:22+00:00 |
|
52131b57d46428b144564c8e40f5688f28909831 | arnepeine/medspeech2 | [
"license:other",
"region:us"
]
| 2023-01-03T10:36:27+00:00 | {"license": "other"} | 2023-01-03T10:40:46+00:00 |
|
296c87b74745521851107b27f37bc08585eab51f |
<h1>Afriqa Prebuilt Indices</h1>
Prebuilt Lucene Inverted Indices for preprocessed Afriqa Wikipedia Passages | masakhane/afriqa-prebuilt-sparse-indexes | [
"task_categories:text-retrieval",
"size_categories:100K<n<1M",
"language:en",
"language:fr",
"license:apache-2.0",
"region:us"
]
| 2023-01-03T12:07:03+00:00 | {"language": ["en", "fr"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-retrieval"], "pretty_name": "Afriqa Wikipedia 100 Inverted Indices"} | 2023-03-31T16:29:39+00:00 |
93ed975421b3189ca3af9ee7059d413144d8f694 | # AutoTrain Dataset for project: exact_data
## Dataset Description
This dataset has been automatically processed by AutoTrain for project exact_data.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "What is the maximum vendor id of vendor present in vendor table who has been issued a PO in 2021",
"target": "select max(t1.vendor_id) from RETAILBUYER_POHEADER as t2 inner join RETAILBUYER_VENDOR as t1 on t2.vendor_id = t1.vendor_id where YEAR(t2.po_issuedt) = 2021"
},
{
"text": "What are the product ids, descriptions and sum of quantities ordered for the products in purchase order line items",
"target": "select L.product_id, t2.product_desc, sum(t1.quantity) from RETAILBUYER_PRODUCT_SOURCE as t2 INNER JOIN RETAILBUYER_POLINEITEM as t1 ON t2.PRODUCT_ID = t1.PRODUCT_ID GROUP BY t1.PRODUCT_ID, t2.product_desc"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 25 |
| valid | 7 |
| Aman6917/autotrain-data-exact_data | [
"task_categories:summarization",
"region:us"
]
| 2023-01-03T12:34:52+00:00 | {"task_categories": ["summarization"]} | 2023-01-03T12:42:34+00:00 |
d12b7c878224d4773ecb04596ba9cf0e9a499be8 | # AutoTrain Dataset for project: tm3_model
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tm3_model.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "List all PO headers with a valid vendor record in database",
"target": "select * from RETAILBUYER_POHEADER as t2 inner join RETAILBUYER_VENDOR as t1 on t2.VENDOR_ID = t1.VENDOR_ID"
},
{
"text": "List all details of PO headers which have a vendor in vendor table",
"target": "select * from RETAILBUYER_POHEADER as t2 inner join RETAILBUYER_VENDOR as t1 on t2.VENDOR_ID = t1.VENDOR_ID"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 49 |
| valid | 17 |
| Aman6917/autotrain-data-tm3_model | [
"task_categories:summarization",
"region:us"
]
| 2023-01-03T12:47:41+00:00 | {"task_categories": ["summarization"]} | 2023-01-03T12:52:49+00:00 |
2b34df1759c39e6cd4fd863850df5141b03e8f98 | maickdelaia/image23 | [
"region:us"
]
| 2023-01-03T12:47:51+00:00 | {} | 2023-01-03T13:28:55+00:00 |
|
3aafb46d72915a2378592678035999d8935e3bff | # Dataset Card for "medspeech3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arnepeine/medspeech3 | [
"region:us"
]
| 2023-01-03T13:27:23+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2290519.0, "num_examples": 24}], "download_size": 0, "dataset_size": 2290519.0}} | 2023-01-03T15:07:34+00:00 |
9e561816b83ca5ab0c09843e5c8d7a525067cec3 | epts/kanji-full | [
"license:wtfpl",
"region:us"
]
| 2023-01-03T14:32:49+00:00 | {"license": "wtfpl"} | 2023-01-03T17:44:26+00:00 |
|
378da95ed5c61c9510afe04097d67ae43154d5f3 | W4nkel/dataSet2 | [
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-03T14:43:47+00:00 | {"license": "cc-by-sa-4.0"} | 2023-01-03T18:42:11+00:00 |
|
b9c7b76cbb634a3e7b59e7e055eeada03bf3b8dc | # Dataset Card for "test_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pyakymenko/test_dev | [
"region:us"
]
| 2023-01-03T14:56:11+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57651.0, "num_examples": 2}], "download_size": 51674, "dataset_size": 57651.0}} | 2023-01-04T17:20:42+00:00 |
f926107d762b1fb99e9b7f936541d1281d55d26d | # Dataset Card for "test-github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ppl418/test-github-issues | [
"region:us"
]
| 2023-01-03T15:01:53+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "dtype": "null"}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 410377, "num_examples": 100}], "download_size": 183986, "dataset_size": 410377}} | 2023-01-03T15:01:57+00:00 |
741e0c378ea81209c16803672db9cf5c51d4093a | # Dataset Card for "wikipedia_id_20230101"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/wikipedia_id_20230101 | [
"region:us"
]
| 2023-01-03T16:04:05+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": 1051737365, "num_examples": 634559}], "download_size": 544132473, "dataset_size": 1051737365}} | 2023-01-03T16:04:27+00:00 |
5975141ea711bdf75d6d528989d3169863dc1239 | # Dataset Card for "beautiful_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_data | [
"region:us"
]
| 2023-01-03T16:14:30+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}, {"name": "dataset_identifier", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2130074097.4420476, "num_examples": 2795}, {"name": "test", "num_bytes": 237013611.55795234, "num_examples": 311}], "download_size": 2367106825, "dataset_size": 2367087709.0}} | 2023-01-11T14:33:37+00:00 |
8093a356b493987098623241350cb37e0148dc66 | Sgevreolete/A7 | [
"license:unknown",
"region:us"
]
| 2023-01-03T17:15:33+00:00 | {"license": "unknown"} | 2023-01-03T17:15:33+00:00 |
|
0b1d3e63ee735a36303025f197168de134dc530e | # AutoTrain Dataset for project: copcar
## Dataset Description
This dataset has been automatically processed by AutoTrain for project copcar.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<246x360 RGB PIL image>",
"target": 0
},
{
"image": "<128x128 RGB PIL image>",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['VehiclesNepal1', 'police_car'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 992 |
| valid | 248 |
### Citation Information
```
@misc {leroy_t_brenneman_2023,
author = { {leroy T brenneman} },
title = { autotrain-data-copcar (Revision ebeca60) },
year = 2023,
url = { https://huggingface.co/datasets/gatman666/autotrain-data-copcar },
doi = { 10.57967/hf/0243 },
publisher = { Hugging Face }
}
```
| gatman666/autotrain-data-copcar | [
"task_categories:image-classification",
"doi:10.57967/hf/0243",
"region:us"
]
| 2023-01-03T17:57:15+00:00 | {"task_categories": ["image-classification"]} | 2023-03-01T21:55:26+00:00 |
0ef727f48d5f8bc2133a1b21e522458bd6f9e06f | # Dataset Card for "sample_dataset_ts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | BhavyaMuni/sample_dataset_ts | [
"region:us"
]
| 2023-01-03T18:07:28+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 633903, "num_examples": 3445}], "download_size": 256343, "dataset_size": 633903}} | 2023-01-03T18:07:33+00:00 |
4287dd5e60f84797248b5a0723582b82bae7a5bd | # Dataset Card for "dfl_classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ManuD/dfl_classification | [
"region:us"
]
| 2023-01-03T18:29:13+00:00 | {"dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "video_id", "dtype": "string"}, {"name": "time", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int32"}, {"name": "label_str", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8428385682.6, "num_examples": 244497}], "download_size": 8405174528, "dataset_size": 8428385682.6}} | 2023-01-05T22:21:07+00:00 |
e858c85e98b8eee9ea6bb9a9911c2a4d407e38cb | # Dataset Card for "helloworld"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Taeyoung/helloworld | [
"region:us"
]
| 2023-01-03T18:46:38+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2080649.0, "num_examples": 6}], "download_size": 0, "dataset_size": 2080649.0}} | 2023-01-03T19:16:04+00:00 |
414bed565cc9de0ee5fd8e441dea9a18dfcb473b | Patryk5675/test5 | [
"license:gfdl",
"region:us"
]
| 2023-01-03T18:57:34+00:00 | {"license": "gfdl"} | 2023-01-03T18:58:03+00:00 |
|
b13f7ed769444c5b010034ed8ac5f0f0b6c87af9 |
# Spanish Books
## Dataset Description
- **Total of books:** 87,967
### Dataset Summary
Dataset of books in Spanish crawled from web and torrents.
### Preprocessing
Preprocessing performed by [spanish_nlp](https://github.com/jorgeortizfuentes/spanish_nlp).
### Licensing Information
The dataset is available under the [Creative Commons Attribution-ShareAlike License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
Some books may be subject to copyright. Use for academic purposes only.
### Citation Information
```
@misc{ortiz2022esbooks,
title={Crawled Spanish Books},
author={Jorge Ortiz-Fuentes},
year={2022},
publisher= {Hugging Face}
}
```
| jorgeortizfuentes/spanish_books | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-01-03T20:50:24+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["es"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "SpanishBooks", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40822979419, "num_examples": 87967}], "download_size": 25042031556, "dataset_size": 40822979419}} | 2023-01-03T21:21:44+00:00 |
3a9052f6dba0f50edbe1a1e0d677b83ee52273c7 | DavidVivancos/MindBigData2022_Imagenet_IN | [
"license:odbl",
"region:us"
]
| 2023-01-03T21:12:28+00:00 | {"license": "odbl"} | 2023-01-03T21:16:12+00:00 |
|
d93f31174df641b5d31508e1e3b0708460f18fcb | # Dataset Card for "kratos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | matteopilotto/kratos | [
"region:us"
]
| 2023-01-03T21:33:19+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 10082811.0, "num_examples": 10}], "download_size": 10084661, "dataset_size": 10082811.0}} | 2023-01-04T07:08:38+00:00 |
22679caa9c01eff73bd1f02334c28112d91f4079 | # Dataset Card for "OCT_balanced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MauroLeidi/OCT_balanced | [
"region:us"
]
| 2023-01-03T21:34:57+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "DRUSEN", "1": "NORMAL"}}}}], "splits": [{"name": "train", "num_bytes": 1037539349.736, "num_examples": 17232}, {"name": "test", "num_bytes": 21771538.0, "num_examples": 500}], "download_size": 1080333714, "dataset_size": 1059310887.736}} | 2023-01-03T22:00:22+00:00 |
cc96c26810a89d329fdaabeef7b3ad266f73da3e | # AutoTrain Dataset for project: police-identifier
## Dataset Description
This dataset has been automatically processed by AutoTrain for project police-identifier.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<246x360 RGB PIL image>",
"target": 0
},
{
"image": "<128x128 RGB PIL image>",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['VehiclesNepal1', 'police_car'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 992 |
| valid | 248 |
| gatman666/autotrain-data-police-identifier | [
"task_categories:image-classification",
"region:us"
]
| 2023-01-03T22:20:20+00:00 | {"task_categories": ["image-classification"]} | 2023-01-03T22:48:29+00:00 |
025879d6b66ec442954e5e0e3cd70c04e7293a6e | PP04/Sanskrit-Text-Summary | [
"license:unknown",
"region:us"
]
| 2023-01-04T00:31:05+00:00 | {"license": "unknown"} | 2023-01-04T18:46:51+00:00 |
|
47a44c793eeed725649bcd39cc7a6ec986b4904d | # Dataset Card for "GMTK-Transcripts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Description
Transcripts generated by Whisper's large model of [GMTK's channel](https://www.youtube.com/channel/UCqJ-Xo29CKyLTjn6z2XwYAw) | taesiri/GMTK-Transcripts | [
"region:us"
]
| 2023-01-04T01:56:30+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2490682, "num_examples": 36120}], "download_size": 1595636, "dataset_size": 2490682}} | 2023-01-04T02:00:29+00:00 |
cc8df0f224cb1a9353a92d959b095a1a1c233068 | sdfs | Sushmit/diffMed | [
"region:us"
]
| 2023-01-04T02:26:17+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2346045522.71, "num_examples": 89395}], "download_size": 2318135039, "dataset_size": 2346045522.71}} | 2023-03-13T11:59:35+00:00 |
14d1cd35a3211c4ad2af2c4914ecd9ca73c6be64 | Xylverize/p2m1 | [
"license:other",
"region:us"
]
| 2023-01-04T03:34:51+00:00 | {"license": "other"} | 2023-01-04T03:38:42+00:00 |
|
90e1f870489c35c15d2a1f9a270856cc3089d11d | Xylverize/p1g1 | [
"license:other",
"region:us"
]
| 2023-01-04T03:35:18+00:00 | {"license": "other"} | 2023-01-04T03:39:42+00:00 |
|
09b1267148267957205edc684969ec65a4cc9556 | hasarinduperera/sigiriya-image-dataset | [
"license:openrail",
"region:us"
]
| 2023-01-04T05:11:43+00:00 | {"license": "openrail"} | 2023-01-04T05:14:29+00:00 |
|
d0bb0d5c1f0a257110d49bafa5c8a7946a9a361b | FLIP-dataset/FLIP-80M | [
"license:cc-by-4.0",
"region:us"
]
| 2023-01-04T05:12:28+00:00 | {"license": "cc-by-4.0"} | 2023-01-04T06:20:06+00:00 |
|
585ba19c42a7c3a56d703678d289f449de4e85eb |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Hacker news until 2015 with comments. Collect from Google BigQuery open dataset. We didn't do any pre-processing except remove HTML tags.
### Supported Tasks and Leaderboards
Comment Generation; News analysis with comments; Other comment-based NLP tasks.
### Languages
English
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | Linkseed/hacker_news_with_comments | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:afl-3.0",
"CommentGenerate",
"region:us"
]
| 2023-01-04T06:19:34+00:00 | {"annotations_creators": [], "language_creators": ["found"], "language": ["en"], "license": ["afl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "hacker_news_with_comments ", "tags": ["CommentGenerate"]} | 2023-01-06T05:44:10+00:00 |
ed469c08d41bc8f06be59d054c92f45fa8aba976 | # Dataset Card for "alarm_prediction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hamzagorgulu/alarm_prediction | [
"region:us"
]
| 2023-01-04T06:36:58+00:00 | {"dataset_info": {"features": [{"name": "alarms", "dtype": "string"}, {"name": "sequence_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 590075.731, "num_examples": 1271}, {"name": "validation", "num_bytes": 65925.062, "num_examples": 142}], "download_size": 191168, "dataset_size": 656000.7930000001}} | 2023-01-04T09:56:30+00:00 |
2e8ed5d0c2dbceff3f65d2b0c018ca18f9d9783a | # Dataset Card for "psychiq2-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | derenrich/psychiq2-dataset | [
"region:us"
]
| 2023-01-04T06:46:18+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "P31-Q5", "1": "P31-Q16521", "2": "P31-Q4167410", "3": "P31-Q11424", "4": "P31-Q482994", "5": "P31-Q13406463", "6": "P31-Q532", "7": "P31-Q27020041", "8": "P31-Q486972", "9": "P31-Q22808320", "10": "P31-Q4830453", "11": "P31-Q101352", "12": "P31-Q7725634", "13": "P31-Q134556", "14": "P31-Q215380", "15": "P31-Q55488", "16": "P31-Q17343829", "17": "P31-Q5398426", "18": "P31-Q484170", "19": "P31-Q105543609", "20": "P31-Q4022", "21": "P31-Q43229", "22": "P31-Q18340514", "23": "P31-Q7889", "24": "P31-Q8502", "25": "P31-Q34442", "26": "P31-Q26895936", "27": "P31-Q3558970", "28": "P31-Q16510064", "29": "P31-Q14350", "30": "P31-Q476028", "31": "P31-Q56436498", "32": "P31-Q16970", "33": "P31-Q11173", "34": "P31-Q9826", "35": "P31-Q41176", "36": "P31-Q23038290", "37": "P31-Q11446", "38": "P31-Q46190676", "39": "P31-Q26887310", "40": 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"747": "P31-Q79602", "748": "P31-Q20857085", "749": "P31-Q8366", "750": "P31-Q22713629", "751": "P31-Q3469910", "752": "P31-Q15079786", "753": "P31-Q1261499", "754": "P31-Q105999", "755": "P31-Q14752696", "756": "P31-Q9842", "757": "P31-Q15265344", "758": "P31-Q35054", "759": "P31-Q686822", "760": "P31-Q22674925", "761": "P31-Q3685463", "762": "P31-Q5003624", "763": "P31-Q1620908", "764": "P31-Q8776398", "765": "P31-Q153562", "766": "P31-Q15893266", "767": "P31-Q15620943", "768": "P31-Q39367", "769": "P31-Q12819564", "770": "P31-Q83790536", "771": "P31-Q98775491", "772": "P31-Q55102916", "773": "P31-Q47443726", "774": "P31-Q11167066", "775": "P31-Q75054287", "776": "P31-Q1068842", "777": "P31-Q620471", "778": "P31-Q746549", "779": "P31-Q4117139", "780": "P31-Q640506", "781": "P31-Q158218", "782": "P31-Q15221215", "783": "P31-Q17431399", "784": "P31-Q1484611", "785": "P31-Q2983893", "786": "P31-Q12518", "787": "P31-Q2154459", "788": "P31-Q166118", "789": "P31-Q3117863", "790": "P31-Q968159", "791": "P31-Q11455398", "792": "P31-Q317623", "793": "P31-Q2742167", "794": "P31-Q24869", "795": "P31-Q32880", "796": "P31-Q192078", "797": "P31-Q1756006", "798": "P31-Q55237813", "799": "P31-Q6243", "800": "P31-Q66715753", "801": "P31-Q109607", "802": "P31-Q2996394", "803": "P31-Q167170", "804": "P31-Q2089242", "805": "P31-Q11204", "806": "P31-Q67454740", "807": "P31-Q211748", "808": "P31-Q26214208", "809": "P31-Q2750108", "810": "P31-Q507619", "811": "P31-Q1499623", "812": "P279-Q2990946", "813": "P31-Q15229207", "814": "P31-Q1441305", "815": "P31-Q1060829", "816": "P31-Q7864918", "817": "P31-Q190903", "818": "P31-Q124734", "819": "P31-Q1267632", "820": "P31-Q726870", "821": "P31-Q917146", "822": "P31-Q23039057", "823": "P31-Q2695280", "824": "P31-Q2635894", "825": "P31-Q465299", "826": "P31-Q1799072", "827": "P31-Q1048525", "828": "P31-Q55983715", "829": "P31-Q2022036", "830": "P31-Q4271324", "831": "P31-Q49773", "832": "P31-Q3327874", "833": "P31-Q682943", "834": 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"P31-Q2223653", "878": "P31-Q30129411", "879": "P31-Q383092", "880": "P31-Q15720476", "881": "P31-Q18691599", "882": "P31-Q3497167", "883": "P31-Q17366755", "884": "P31-Q15324", "885": "P31-Q15221242", "886": "P31-Q641066", "887": "P31-Q16887380", "888": "P31-Q381885", "889": "P31-Q38720", "890": "P31-Q158438", "891": "P31-Q829026", "892": "P31-Q55659167", "893": "P31-Q496825", "894": "P31-Q7372078", "895": "P31-Q3950", "896": "P31-Q1785733", "897": "P31-Q18564289", "898": "P31-Q17339814", "899": "P31-Q1311958", "900": "P31-Q46865913", "901": "P31-Q107679", "902": "P31-Q18325436", "903": "P31-Q23847174", "904": "P31-Q23691", "905": "P31-Q3240003", "906": "P31-Q18761864", "907": "P31-Q1595639", "908": "P31-Q1147395", "909": "P31-Q46351685", "910": "P31-Q1070990", "911": "P31-Q17715832", "912": "P31-Q16735822", "913": "P31-Q1047113", "914": "P31-Q13411064", "915": "P31-Q4936952", "916": "P31-Q23983664", "917": "P31-Q936518", "918": "P31-Q850270", "919": "P31-Q16466010", "920": 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"num_examples": 5660845}, {"name": "test", "num_bytes": 297185196, "num_examples": 628983}], "download_size": 987789145, "dataset_size": 2973142673}} | 2023-01-04T06:59:48+00:00 |
81c57d652dfb6a3ca2497cf3ab4ef40f1936c33b | Someman/danphe | [
"license:mit",
"region:us"
]
| 2023-01-04T06:53:59+00:00 | {"license": "mit"} | 2023-01-04T06:55:56+00:00 |
|
04949024777c371d5cc6e85976d9287f94ff71a2 |
# Genshin Datasets for SVS/SVC/TTS
## 仓库地址
| 仓库 | 传送门 |
| :------------: | :-----------------------------------------------: |
| DiffSinger | [点此传送](https://github.com/openvpi/DiffSinger) |
| Fish Diffusion | [点此传送](https://github.com/fishaudio/fish-diffusion) |
| RVC | [点此传送](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI) |
| DDSP-SVC | [点此传送](https://github.com/yxlllc/DDSP-SVC) |
| Vits | [点此传送](https://github.com/CjangCjengh/vits) |
| 44.1KHz声码器 | [点此传送](https://openvpi.github.io/vocoders) |
| 原神语音数据集(溯洄，目前只更新到了3.4) | [点此传送](https://github.com/w4123/GenshinVoice) |
## 介绍
该数据集为训练原神 SVS/SVC/TTS 的数据集，目前提供全数据集 (Full) 和整理好的 (Sorted) 数据集，全数据集由 [溯洄](https://github.com/w4123) 的3.4版本和我自己整理的合并而成，需要自行根据项目进行预处理以及响度匹配等等。后续也会提供其它语言的语音。**该数据集仅可用于二次创作和训练模型，不得进行任何商业用途！该数据集所用的语音数据的所有权均归 [米哈游](https://www.mihoyo.com/) 所有！**
## 下载地址（不定时更新）
| 版本 | 是否已整理 | 语言 | 下载地址 | 备注 |
| :----------------------------------------------------------: | :------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| 3.5 | 已按照角色整理 | Chinese | [点我下载](https://huggingface.co/datasets/Erythrocyte/Genshin_Datasets/resolve/main/Sorted/Chinese/3.5_Sorted.zip) | 包含：全角色+部分NPC+标注 |
| 3.5 | 未整理 | Chinese | 上传中 | 完整的数据集，需要自行按需整理，无标注 |
## 整理脚本
如果想要从完整数据集获取自己想要的角色，可以通过如下脚本整理：
整理脚本：https://huggingface.co/datasets/Erythrocyte/Genshin_Datasets/blob/main/Scripts/genshin_label.py | Erythrocyte/Genshin_Datasets | [
"Genshin",
"Genshin Impact",
"Voice Data",
"Voice Dataset",
"DiffSinger",
"Diff-SVC",
"DiffSVC",
"Vits",
"DDSP-SVC",
"region:us"
]
| 2023-01-04T06:59:36+00:00 | {"tags": ["Genshin", "Genshin Impact", "Voice Data", "Voice Dataset", "DiffSinger", "Diff-SVC", "DiffSVC", "Vits", "DDSP-SVC"]} | 2023-05-02T05:56:26+00:00 |
3481798ee8036a17b6c943c1cea7d15239db6641 | Someman/momo | [
"license:mit",
"region:us"
]
| 2023-01-04T07:29:01+00:00 | {"license": "mit"} | 2023-01-04T08:15:03+00:00 |
|
7d9772484437c411095674310b5c297603a760f2 | # Dataset Card for "beautiful_interesting_spectacular_photo_model_30000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_interesting_spectacular_photo_model_30000 | [
"region:us"
]
| 2023-01-04T07:43:16+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 216048045.0, "num_examples": 314}], "download_size": 216051172, "dataset_size": 216048045.0}} | 2023-01-04T07:43:40+00:00 |
84ab804cfcae53048713ce350017e3dfe6225fde | # Dataset Card for "beautiful_interesting_spectacular_photo_fantasy_30000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_interesting_spectacular_photo_fantasy_30000 | [
"region:us"
]
| 2023-01-04T07:50:05+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 268414188.0, "num_examples": 317}], "download_size": 268419805, "dataset_size": 268414188.0}} | 2023-01-04T07:50:32+00:00 |
3e48b7963475b55341dc09c108b6a7684c3d6d1f | # Dataset Card for "beautiful_interesting_spectacular_photo_dark_fantasy_30000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_interesting_spectacular_photo_dark_fantasy_30000 | [
"region:us"
]
| 2023-01-04T07:57:37+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 533716832.0, "num_examples": 718}], "download_size": 533724773, "dataset_size": 533716832.0}} | 2023-01-04T07:58:27+00:00 |
33354022c52971b0f34e7367b78fd6a37d80d66b | # Dataset Card for "alarm_prediction2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hamzagorgulu/alarm_prediction2 | [
"region:us"
]
| 2023-01-04T07:59:55+00:00 | {"dataset_info": {"features": [{"name": "alarms", "dtype": "string"}, {"name": "sequence_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 5160488.637481801, "num_examples": 10905}, {"name": "validation", "num_bytes": 573545.3671369045, "num_examples": 1212}], "download_size": 1179619, "dataset_size": 5734034.004618706}} | 2023-01-04T08:00:20+00:00 |
ce0774c2ff5385234e78cbd52cf287693b375a5e | DavidVivancos/MindBigData2022_Imagenet_IN_Spct | [
"license:odbl",
"region:us"
]
| 2023-01-04T08:10:14+00:00 | {"license": "odbl"} | 2023-01-04T08:12:38+00:00 |
|
e6a8589f70429398fdf99baab997f7a7a46e4b72 | # Dataset Card for "beautiful_interesting_spectacular_photo_anime_25000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_interesting_spectacular_photo_anime_25000 | [
"region:us"
]
| 2023-01-04T08:12:15+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 773920358.0, "num_examples": 956}], "download_size": 773924888, "dataset_size": 773920358.0}} | 2023-01-04T08:13:20+00:00 |
31920bb67cb196b706cbb23d66d96495b545fc15 | DavidVivancos/MindBigData2022_VisMNIST_MU2 | [
"license:odbl",
"region:us"
]
| 2023-01-04T08:16:21+00:00 | {"license": "odbl"} | 2023-01-04T08:18:34+00:00 |
|
3ee237788da7c69c53188e34f778a7b61b479af7 | # Dataset Card for "online-sweater"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | chiHang/online-sweater | [
"region:us"
]
| 2023-01-04T08:18:14+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1126581.0, "num_examples": 10}], "download_size": 0, "dataset_size": 1126581.0}} | 2023-01-12T07:56:28+00:00 |
773520b8eea5f9f29ba65740853510fcd6ad1b0c | # Dataset Card for "beautiful_interesting_spectacular_photo_futuristic_25000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_interesting_spectacular_photo_futuristic_25000 | [
"region:us"
]
| 2023-01-04T08:22:17+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 406730039.0, "num_examples": 596}], "download_size": 406731237, "dataset_size": 406730039.0}} | 2023-01-04T08:22:52+00:00 |
4e7e1e01382f55746d6eae13b68664cdaf0c5185 | DavidVivancos/MindBigData2022_MNIST_MW | [
"license:odbl",
"region:us"
]
| 2023-01-04T08:25:17+00:00 | {"license": "odbl"} | 2023-01-04T08:26:21+00:00 |
|
10544092a3d76e2eb2bad55e4e5ef40e165a8f71 | # Dataset Card for "beautiful_interesting_spectacular_photo_25000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_interesting_spectacular_photo_25000 | [
"region:us"
]
| 2023-01-04T08:31:50+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 94714209.0, "num_examples": 111}], "download_size": 94717904, "dataset_size": 94714209.0}} | 2023-01-04T08:32:05+00:00 |
37b30417fc61d2017d88b07bb5c9de096182d1b2 | # Dataset Card for "beautiful_interesting_spectacular_photo_HD_25000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_interesting_spectacular_photo_HD_25000 | [
"region:us"
]
| 2023-01-04T08:36:12+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 73481451.0, "num_examples": 94}], "download_size": 73485488, "dataset_size": 73481451.0}} | 2023-01-04T08:36:27+00:00 |
c2fc4c386a6eb488561e194fd9692eac21fe97ae | # Dataset Card for "beautiful_interesting_spectacular_photo_medieval_25000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_interesting_spectacular_photo_medieval_25000 | [
"region:us"
]
| 2023-01-04T08:46:43+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 195631184.0, "num_examples": 198}], "download_size": 195563226, "dataset_size": 195631184.0}} | 2023-01-04T08:47:05+00:00 |
fdc848ab0183208ea7808206c91c724414d0a071 |
# Dataset Card for 🥤SODA
## Dataset Description
- **Repository:** [Code](https://github.com/skywalker023/sodaverse)
- **Paper:** [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://arxiv.org/abs/2212.10465)
- **Point of Contact:** [Hyunwoo Kim](mailto:[email protected])
## Dataset Summary
🥤SODA is the first publicly available, million-scale, high-quality dialogue dataset covering a wide range of social interactions. Dialogues are distilled from a PLM (InstructGPT; Ouyang et al., 2022) by contextualizing social commonsense knowledge from a knowledge graph (Atomic10x; West et al., 2022). Human evaluation shows that dialogues in SODA are more consistent, specific, and (surprisingly) natural than prior human-authored datasets – e.g., DailyDialog (Li et al., 2017), BlendedSkillTalk (Smith et al., 2020). Also, since social commonsense knowledge encompasses emotional reactions (i.e., the xReact `relation`), SODA includes 385K conversations labeled with 1.7K unique emotions along with information about the experiencer and the cause – i.e., `PersonX` and the `head` event in the symbolic commonsense knowledge triple.
## Languages
English
## Dataset Structure
field | type | description
--- | --- | ---
`head` | str | the head event in the symbolic commonsense knowledge triple
`relation` | str | the relationship between `head` and `tail` events
`tail` | str | the tail event in the symbolic commonsense knowledge triple
`literal` | str | the symbolic commonsense knowledge in sentence-form
`narrative` | str | narrative based on the `literal`
`dialogue` | list of str | dialogue grounded in the `narrative`
`speakers` | list of str | the speakers for each turn in the `dialogue`
`PersonX` | str | the assigned name for PersonX in the commonsense knowledge triple
`PersonY` | str\|null | the assigned name for PersonY in the commonsense knowledge triple
`PersonZ` | str\|null | the assigned name for PersonZ in the commonsense knowledge triple
`original_index` | int | the original index from Atomic10x
`split` | str | the split information: {train, valid, test}
`head_answer` | str | the answer for whether the `head` is included in the `narrative`: {Yes, Unknown}
`pmi_head_answer` | str | the answer for whether the `head` is included in the `narrative` with point-wise mutual information applied: {Yes, No, Unknown}
`relation_tail_answer` | str | the answer for whether the `relation`-`tail` is included in the `dialogue`: {Yes, No, Unknown}
`pmi_relation_tail_answer` | str | the answer for whether the `relation`-`tail` is included in the `dialogue` with point-wise mutual information applied: {Yes, No, Unknown}
## Dataset Creation
To create 🥤SODA, we distill dialogues from InstructGPT by contextualizing social commonsense knowledge – i.e., adding context information in multiple steps: (1) Retrieve social commonsense from the symbolic commonsense knowledge graph, (2) convert it into sentence form, (3) generate a narrative from the sentence, (4) infer the speakers from the narrative, and finally (5) derive contentful conversation grounded in the narrative and speakers. Anchoring the PLM in commonsense knowledge for deriving conversations offers two key advantages: (1) minimizing nonsensical conversations and (2) maximizing diversity. For more details, please refer to our [paper](https://arxiv.org/abs/2212.10465).
### Further Details, Social Impacts, and Limitations
Please refer to our [paper](https://arxiv.org/abs/2212.10465).
## Trained Model
Using 🥤SODA, we train 🧑🏻🚀COSMO: a generalizable conversation agent outperforming previous best-performing agents on both in- and out-of-domain datasets. COSMO-3B is available [here](https://huggingface.co/allenai/cosmo-xl)!
## Additional Information
For a brief summary of our paper, please see this [tweet](https://twitter.com/hyunw__kim/status/1605400305126248448).
### Citation
Please cite our work if you find the resources in this repository useful:
```
@article{kim2022soda,
title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization},
author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi},
journal={ArXiv},
year={2022},
volume={abs/2212.10465}
}
``` | allenai/soda | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|Atomic10x",
"language:en",
"license:cc-by-4.0",
"dialogue",
"narrative",
"commonsense",
"arxiv:2212.10465",
"region:us"
]
| 2023-01-04T08:51:53+00:00 | {"language_creators": ["machine-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original", "extended|Atomic10x"], "task_categories": ["conversational"], "task_ids": ["dialogue-generation"], "pretty_name": "SODA", "annotation_creators": ["machine-generated"], "splits": [{"name": "train", "num_examples": 1191582}, {"name": "valid", "num_examples": 146346}, {"name": "test", "num_examples": 148968}], "dataset_size": 1486896, "tags": ["dialogue", "narrative", "commonsense"]} | 2023-01-04T09:24:32+00:00 |
576106af13382526a783d008753c19029329de3a | DavidVivancos/MindBigData2022_VisMNIST_Cap64 | [
"license:odbl",
"region:us"
]
| 2023-01-04T09:02:35+00:00 | {"license": "odbl"} | 2023-01-04T09:04:22+00:00 |
|
688c67b1fef4d625fd3e928ca5638f1d173b4fb5 | # Dataset Card for "alarm_prediction3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hamzagorgulu/alarm_prediction3 | [
"region:us"
]
| 2023-01-04T09:57:11+00:00 | {"dataset_info": {"features": [{"name": "alarms", "dtype": "string"}, {"name": "sequence_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 590075.731, "num_examples": 1271}, {"name": "validation", "num_bytes": 65925.062, "num_examples": 142}], "download_size": 191168, "dataset_size": 656000.7930000001}} | 2023-01-04T10:17:04+00:00 |
4dbfbf954fea228e920d3e0ce6c7c93478705e53 | vikasssss/embedding | [
"license:apache-2.0",
"region:us"
]
| 2023-01-04T10:33:04+00:00 | {"license": "apache-2.0"} | 2023-01-04T10:34:51+00:00 |
|
d1ac575dc099ce61986efd101ed34b25455bd556 |
## starcraft-remastered-melee-maps
This is a dataset containing 1,815 Starcraft:Remastered melee maps, categorized into tilesets.
The dataset is used to train this model: https://huggingface.co/wdcqc/starcraft-platform-terrain-32x32
The dataset is manually downloaded from Battle.net, bounding.net (scmscx.com) and broodwarmaps.com over a long period of time.
To use this dataset, extract the `staredit\\scenario.chk` files from the map files using StormLib, then refer to [Scenario.chk Format](http://www.staredit.net/wiki/index.php/Scenario.chk) to get data like text, terrain or resource placement from the map.
Alternatively download the dataset and put it in `<My Documents>\StarCraft\Maps`. You can play with your friends. | wdcqc/starcraft-remastered-melee-maps | [
"task_categories:feature-extraction",
"task_categories:text-to-image",
"task_categories:image-to-image",
"task_categories:reinforcement-learning",
"task_ids:task-planning",
"size_categories:1K<n<10K",
"language:en",
"language:ko",
"license:unknown",
"starcraft",
"broodwar",
"melee",
"maps",
"region:us"
]
| 2023-01-04T10:38:40+00:00 | {"language": ["en", "ko"], "license": "unknown", "size_categories": "1K<n<10K", "task_categories": ["feature-extraction", "text-to-image", "image-to-image", "reinforcement-learning"], "task_ids": ["task-planning"], "pretty_name": "Starcraft Remastered Melee Maps", "tags": ["starcraft", "broodwar", "melee", "maps"], "splits": [{"name": "ashworld", "num_bytes": "12,598,840", "num_examples": 135}, {"name": "badlands", "num_bytes": "21,067,712", "num_examples": 213}, {"name": "desert", "num_bytes": "19,505,010", "num_examples": 185}, {"name": "ice", "num_bytes": "19,070,217", "num_examples": 179}, {"name": "install", "num_bytes": "28,135", "num_examples": 1}, {"name": "jungle", "num_bytes": "62,374,211", "num_examples": 563}, {"name": "platform", "num_bytes": "23,324,208", "num_examples": 265}, {"name": "twilight", "num_bytes": "28,311,253", "num_examples": 274}]} | 2023-01-06T22:38:36+00:00 |
fb8c3263adf9284583743bd955306187a2d77757 | # Dataset Card for "beautiful_data_with_generated_captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/beautiful_data_with_generated_captions | [
"region:us"
]
| 2023-01-04T11:01:28+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "pclean", "dtype": "float64"}, {"name": "generated_caption", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 256755027.0, "num_examples": 331}, {"name": "train", "num_bytes": 2306158521.402, "num_examples": 2973}], "download_size": 2541913303, "dataset_size": 2562913548.402}} | 2023-01-04T14:24:07+00:00 |
8320c50ad78559c591b0239bb6767b6f605b10e2 | ell-hol/ConceptualCaptionFR | [
"license:apache-2.0",
"region:us"
]
| 2023-01-04T11:28:30+00:00 | {"license": "apache-2.0"} | 2023-01-04T15:23:06+00:00 |
|
88542b32a842267f56d83dbe1fa39d3a38c92c4b | Mayhem50/mayhem-test | [
"license:unknown",
"region:us"
]
| 2023-01-04T11:44:37+00:00 | {"license": "unknown"} | 2023-01-04T11:44:37+00:00 |
|
93061e4f418460af4f1eb8ee81dd472439e44862 | hasarinduperera/bioluminescence-image-dataset | [
"license:openrail",
"region:us"
]
| 2023-01-04T12:59:35+00:00 | {"license": "openrail"} | 2023-01-04T13:02:35+00:00 |
|
1227dd2f87c2c044fb90885ed2ba2dda0f6ff5c6 | 35 dataset images for FloralMarble. Originally created an embedding for statues and busts on a colored background, then mixed that with various other embeddings, resulting in this dataset.
Trained for 500 epochs/steps. 35 images, 4 vectors. Batch size of 7, 5 grad acc steps, learning rate of 0.0025:250,0.001:500.
| spaablauw/FloralMarble_dataset | [
"license:wtfpl",
"region:us"
]
| 2023-01-04T13:22:20+00:00 | {"license": "wtfpl"} | 2023-01-04T13:28:07+00:00 |
a7de0e452152e1deffcb8b95c40ce0da323028dd | # Dataset Card for "sketchy-svgs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kmewhort/sketchy-svgs | [
"region:us"
]
| 2023-01-04T13:46:16+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "airplane", "1": "alarm_clock", "2": "ant", "3": "ape", "4": "apple", "5": "armor", "6": "axe", "7": "banana", "8": "bat", "9": "bear", "10": "bee", "11": "beetle", "12": "bell", "13": "bench", "14": "bicycle", "15": "blimp", "16": "bread", "17": "butterfly", "18": "cabin", "19": "camel", "20": "candle", "21": "cannon", "22": "car_(sedan)", "23": "castle", "24": "cat", "25": "chair", "26": "chicken", "27": "church", "28": "couch", "29": "cow", "30": "crab", "31": "crocodilian", "32": "cup", "33": "deer", "34": "dog", "35": "dolphin", "36": "door", "37": "duck", "38": "elephant", "39": "eyeglasses", "40": "fan", "41": "fish", "42": "flower", "43": "frog", "44": "geyser", "45": "giraffe", "46": "guitar", "47": "hamburger", "48": "hammer", "49": "harp", "50": "hat", "51": "hedgehog", "52": "helicopter", "53": "hermit_crab", "54": "horse", "55": "hot-air_balloon", "56": "hotdog", "57": "hourglass", "58": "jack-o-lantern", "59": "jellyfish", "60": "kangaroo", "61": "knife", "62": "lion", "63": "lizard", "64": "lobster", "65": "motorcycle", "66": "mouse", "67": "mushroom", "68": "owl", "69": "parrot", "70": "pear", "71": "penguin", "72": "piano", "73": "pickup_truck", "74": "pig", "75": "pineapple", "76": "pistol", "77": "pizza", "78": "pretzel", "79": "rabbit", "80": "raccoon", "81": "racket", "82": "ray", "83": "rhinoceros", "84": "rifle", "85": "rocket", "86": "sailboat", "87": "saw", "88": "saxophone", "89": "scissors", "90": "scorpion", "91": "sea_turtle", "92": "seagull", "93": "seal", "94": "shark", "95": "sheep", "96": "shoe", "97": "skyscraper", "98": "snail", "99": "snake", "100": "songbird", "101": "spider", "102": "spoon", "103": "squirrel", "104": "starfish", "105": "strawberry", "106": "swan", "107": "sword", "108": "table", "109": "tank", "110": "teapot", "111": "teddy_bear", "112": "tiger", "113": "tree", "114": "trumpet", "115": "turtle", "116": "umbrella", "117": "violin", "118": "volcano", "119": "wading_bird", "120": "wheelchair", "121": "windmill", "122": "window", "123": "wine_bottle", "124": "zebra"}}}}, {"name": "svg", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3350400132.1348753, "num_examples": 59966}, {"name": "test", "num_bytes": 837627968.8651245, "num_examples": 14992}], "download_size": 2677218539, "dataset_size": 4188028101.0}} | 2023-01-04T16:20:18+00:00 |
896f918ab4d22b0ad24f8e2d01b84acf0742c050 | venetis/disaster_tweets | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:other",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:openrail",
"region:us"
]
| 2023-01-04T14:34:07+00:00 | {"annotations_creators": ["other"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["openrail"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis"], "pretty_name": "Twitter Disaster Tweets", "tags": []} | 2023-01-04T15:15:03+00:00 |
|
6e44270571f150dd0c42722bb6397632f1e65300 | # AutoTrain Dataset for project: breastcancer
## Dataset Description
This dataset has been automatically processed by AutoTrain for project breastcancer.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<512x630 L PIL image>",
"target": 0
},
{
"image": "<512x666 L PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['No_cancer', 'cancer'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 7998 |
| valid | 2000 |
| hatemestinbejaia/autotrain-data-breastcancer | [
"task_categories:image-classification",
"region:us"
]
| 2023-01-04T15:13:32+00:00 | {"task_categories": ["image-classification"]} | 2023-01-04T23:50:49+00:00 |
f896ae9f1e04a0331f85ef8f840e9bd85caa8601 | Someman/Sitar | [
"license:mit",
"region:us"
]
| 2023-01-04T16:09:17+00:00 | {"license": "mit"} | 2023-01-04T16:09:54+00:00 |
|
7d6e162b17547700d39c34c537178f41318f4112 | priyabrat/Happy | [
"license:openrail",
"region:us"
]
| 2023-01-04T16:10:49+00:00 | {"license": "openrail"} | 2023-01-04T16:10:49+00:00 |
|
88f8f927031cd751c19338fa4af321c46e67ab0d |
# Dataset Card for MDK
This dataset was created as part of the [Bertelsmann Foundation's](https://www.bertelsmann-stiftung.de/de/startseite)
[Musterdatenkatalog (MDK)]("https://www.bertelsmann-stiftung.de/de/unsere-projekte/smart-country/musterdatenkatalog") project. The MDK provides an overview of Open Data in municipalities in Germany. It is intended to help municipalities in Germany, as well as data analysts and journalists, to get an overview of the topics and the extent to which cities have already published data sets.
## Dataset Description
### Dataset Summary
The dataset is an annotated corpus of 1258 records based on the metadata of the datasets from [GOVDATA](https://www.govdata.de/). GovData is a data portal that aims to make cities' data available in a standardized way.
The annotation maps the titles of the datasets to a taxonomy containing categories such as 'Verkehr - KFZ - Messung' or 'Abfallwirtschaft - Abfallkalender'. Through the assignment the names of the data sets can be normalized and grouped. In total, the taxonomy consists 250 categories. Each category is divided into two levels:
- Level 1: "Thema" (topic)
- Level 2: "Bezeichnung" (label).
The first dash divides the levels. For example:
You can find an interactive view of the taxonomy with all labels [here](https://huggingface.co/spaces/and-effect/Musterdatenkatalog).
The repository contains a small and a large version of the data. The small version is for testing purposes only. The large data set contains all 1258 entries. The large and small datasets are split into a training and a testing dataset. In addition, the large dataset folder contains of a validation dataset that has been annotated separately. The validation dataset is an additional dataset that we created for the evaluation of the algorithm. It also consists of data from GOVDATA and has the same structure as the test and training data set.
### Languages
The language data is German.
## Dataset Structure
### Data Fields
| dataset | size |
|-----|-----|
| small/train | 18.96 KB |
| small/test | 6.13 KB |
| large/train | 517.77 KB |
| large/test | 118.66 KB |
An example of looks as follows:
```json
{
"doc_id": "a063d3b7-4c09-421e-9849-073dc8939e76",
"title": "Dienstleistungen Alphabetisch sortiert April 2019",
"description": "CSV-Datei mit allen Dienstleistungen der Kreisverwaltung Kleve. Sortiert nach AlphabetStand 01.04.2019",
"labels_name": "Sonstiges - Sonstiges",
"labels": 166
}
```
The data fields are the same among all splits:
- doc_id (uuid): identifier for each document
- title (str): dataset title from GOVDATA
- description (str): description of the dataset
- labels_name (str): annotation with labels from taxonomy
- labels (int): labels indexed from 0 to 250
### Data Splits
| dataset_name | dataset_splits | train_size | test_size | validation_size
|-----|-----|-----|-----|-----|
| dataset_large | train, test, validation | 1009 | 249 | 101
| dataset_small | train, test | 37 | 13 | None
## Dataset Creation
The dataset was created through multiple manual annotation rounds.
### Source Data
The data comes from [GOVDATA](https://www.govdata.de/), an open data portal of Germany. It aims to provide central access to administrative data from the federal, state and local governments. Their aim is to make data available in one place and thus easier to use. The data available is structured in 13 categories ranging from finance, to international topics, health, education and science and technology. [GOVDATA](https://www.govdata.de/) offers a [CKAN API](https://ckan.govdata.de/) to make requests and provides metadata for each data entry.
#### Initial Data Collection and Normalization
Several sources were used for the annotation process. A sample was collected from [GOVDATA](https://www.govdata.de/) with actual datasets. For the sample, 50 records were drawn for each group. Additional samples are from the previous version of the [MDK](https://github.com/bertelsmannstift/Musterdatenkatalog) that contain older data from [GOVDATA](https://www.govdata.de/). Some of the datasets from the old [MDK](https://github.com/bertelsmannstift/Musterdatenkatalog) already contained an annotation, but since the taxonomy is not the same, the data were re-annotated. A sample was drawn from each source (randomly and by manual selection), resulting in a total of 1258 titles.
### Annotations
#### Annotation process
The data was annotated in four rounds and one additional test round. In each round a percentage of the data was allocated to all annotators to caluculate the inter-annotator agreement using Cohens Kappa.
The following table shows the results of the of the annotations:
| | **Cohens Kappa** | **Number of Annotators** | **Number of Documents** |
| ------------------ | :--------------: | ------------------------ | ----------------------- |
| **Test Round** | .77 | 6 | 50 |
| **Round 1** | .41 | 2 | 120 |
| **Round 2** | .76 | 4 | 480 |
| **Round 3** | .71 | 3 | 420 |
| **Round 4** | .87 | 2 | 416 |
| **Validation set** | - | 1 | 177 |
In addition, a validation set was generated by the dataset curators.
#### Who are the annotators?
Annotators are all employees from [&effect data solutions GmbH](https://www.and-effect.com/). The taxonomy as well as rules and problems in the assignment of datasets were discussed and debated in advance of the development of the taxonomy and the annotation in two workshops with experts and representatives of the open data community and local governments as well as with the project members of the [Musterdatenkatalog]("https://www.bertelsmann-stiftung.de/de/unsere-projekte/smart-country/musterdatenkatalog") from the Bertelsmann Foundation. On this basis, the [&effect](https://www.and-effect.com/) employees were instructed in the annotation by the curators of the datasets.
## Considerations for Using the Data
The dataset for the annotation process was generated by sampling from [GOVDATA](https://www.govdata.de/) and data previously collected from GOVDATA. The data on GOVDATA is continuously updated and data can get deleted. Thus, there is no guarantee that data entries included here will still be available.
### Social Impact of Dataset
Since 2017, the German government has been promoting systematic and free access to public administration data with first laws on open data in municipalities. In this way, a contribution is aimed at the development of a [knowledge society] (https://www.verwaltung-innovativ.de/DE/Startseite/startseite_node.html). The categorization of open data of cities in a standardized and detailed taxonomy supports this process of making data of municipalities freely, openly and structured accessible.
### Discussion of Biases (non-ethical)
The data was mainly sampled at random from the categories available on GOVDATA. Although all categories were sampled there is still some imbalance in the data. For example: entries for the concept 'Raumordnung, Raumplanung und Raumentwicklung - Bebauungsplan' make up the majority class. Although manual selection of data was also used for not all previous concepts data entries was found. However, for 95% of concepts at least one data entry is available.
## Additional Information
### Dataset Curators
Friederike Bauer
Rahkakavee Baskaran
### Licensing Information
CC BY 4.0 | and-effect/mdk_gov_data_titles_clf | [
"task_categories:text-classification",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:de",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-04T16:20:31+00:00 | {"annotations_creators": "crowdsourced", "language_creators": "other", "language": "de", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": ["1K<n<10K"], "source_datasets": "extended", "task_categories": ["text-classification"], "pretty_name": "GOVDATA dataset titles labelled"} | 2023-05-25T11:43:42+00:00 |
410cdf9488714b70e20de89b217e95f856c67030 | # Dataset Card for "test-captioned-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Umal-exvc/test-captioned-dataset | [
"region:us"
]
| 2023-01-04T16:23:40+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 111187.0, "num_examples": 5}], "download_size": 111705, "dataset_size": 111187.0}} | 2023-01-04T16:23:44+00:00 |
8a57a3124b980bf171ddeaecb5fb2b7a39374689 | # Dataset Card for "tu-berlin-svgs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kmewhort/tu-berlin-svgs | [
"region:us"
]
| 2023-01-04T16:34:42+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "airplane", "1": "alarm clock", "2": "angel", "3": "ant", "4": "apple", "5": "arm", "6": "armchair", "7": "ashtray", "8": "axe", "9": "backpack", "10": "banana", "11": "barn", "12": "baseball bat", "13": "basket", "14": "bathtub", "15": "bear (animal)", "16": "bed", "17": "bee", "18": "beer-mug", "19": "bell", "20": "bench", "21": "bicycle", "22": "binoculars", "23": "blimp", "24": "book", "25": "bookshelf", "26": "boomerang", "27": "bottle opener", "28": "bowl", "29": "brain", "30": "bread", "31": "bridge", "32": "bulldozer", "33": "bus", "34": "bush", "35": "butterfly", "36": "cabinet", "37": "cactus", "38": "cake", "39": "calculator", "40": "camel", "41": "camera", "42": "candle", "43": "cannon", "44": "canoe", "45": "car (sedan)", "46": "carrot", "47": "castle", "48": "cat", "49": "cell phone", "50": "chair", "51": "chandelier", "52": "church", "53": "cigarette", "54": "cloud", "55": "comb", "56": "computer monitor", "57": "computer-mouse", "58": "couch", "59": "cow", "60": "crab", "61": "crane (machine)", "62": "crocodile", "63": "crown", "64": "cup", "65": "diamond", "66": "dog", "67": "dolphin", "68": "donut", "69": "door", "70": "door handle", "71": "dragon", "72": "duck", "73": "ear", "74": "elephant", "75": "envelope", "76": "eye", "77": "eyeglasses", "78": "face", "79": "fan", "80": "feather", "81": "fire hydrant", "82": "fish", "83": "flashlight", "84": "floor lamp", "85": "flower with stem", "86": "flying bird", "87": "flying saucer", "88": "foot", "89": "fork", "90": "frog", "91": "frying-pan", "92": "giraffe", "93": "grapes", "94": "grenade", "95": "guitar", "96": "hamburger", "97": "hammer", "98": "hand", "99": "harp", "100": "hat", "101": "head", "102": "head-phones", "103": "hedgehog", "104": "helicopter", "105": "helmet", "106": "horse", "107": "hot air balloon", "108": "hot-dog", "109": "hourglass", "110": "house", "111": "human-skeleton", "112": "ice-cream-cone", "113": "ipod", "114": "kangaroo", "115": "key", "116": "keyboard", "117": "knife", "118": "ladder", "119": "laptop", "120": "leaf", "121": "lightbulb", "122": "lighter", "123": "lion", "124": "lobster", "125": "loudspeaker", "126": "mailbox", "127": "megaphone", "128": "mermaid", "129": "microphone", "130": "microscope", "131": "monkey", "132": "moon", "133": "mosquito", "134": "motorbike", "135": "mouse (animal)", "136": "mouth", "137": "mug", "138": "mushroom", "139": "nose", "140": "octopus", "141": "owl", "142": "palm tree", "143": "panda", "144": "paper clip", "145": "parachute", "146": "parking meter", "147": "parrot", "148": "pear", "149": "pen", "150": "penguin", "151": "person sitting", "152": "person walking", "153": "piano", "154": "pickup truck", "155": "pig", "156": "pigeon", "157": "pineapple", "158": "pipe (for smoking)", "159": "pizza", "160": "potted plant", "161": "power outlet", "162": "present", "163": "pretzel", "164": "pumpkin", "165": "purse", "166": "rabbit", "167": "race car", "168": "radio", "169": "rainbow", "170": "revolver", "171": "rifle", "172": "rollerblades", "173": "rooster", "174": "sailboat", "175": "santa claus", "176": "satellite", "177": "satellite dish", "178": "saxophone", "179": "scissors", "180": "scorpion", "181": "screwdriver", "182": "sea turtle", "183": "seagull", "184": "shark", "185": "sheep", "186": "ship", "187": "shoe", "188": "shovel", "189": "skateboard", "190": "skull", "191": "skyscraper", "192": "snail", "193": "snake", "194": "snowboard", "195": "snowman", "196": "socks", "197": "space shuttle", "198": "speed-boat", "199": "spider", "200": "sponge bob", "201": "spoon", "202": "squirrel", "203": "standing bird", "204": "stapler", "205": "strawberry", "206": "streetlight", "207": "submarine", "208": "suitcase", "209": "sun", "210": "suv", "211": "swan", "212": "sword", "213": "syringe", "214": "t-shirt", "215": "table", "216": "tablelamp", "217": "teacup", "218": "teapot", "219": "teddy-bear", "220": "telephone", "221": "tennis-racket", "222": "tent", "223": "tiger", "224": "tire", "225": "toilet", "226": "tomato", "227": "tooth", "228": "toothbrush", "229": "tractor", "230": "traffic light", "231": "train", "232": "tree", "233": "trombone", "234": "trousers", "235": "truck", "236": "trumpet", "237": "tv", "238": "umbrella", "239": "van", "240": "vase", "241": "violin", "242": "walkie talkie", "243": "wheel", "244": "wheelbarrow", "245": "windmill", "246": "wine-bottle", "247": "wineglass", "248": "wrist-watch", "249": "zebra"}}}}, {"name": "svg", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82640829.32506625, "num_examples": 15999}, {"name": "test", "num_bytes": 20661498.674933746, "num_examples": 4000}], "download_size": 65748314, "dataset_size": 103302328.0}} | 2023-01-10T19:20:44+00:00 |
d3ee81f581595ba9a1b08989000c0a4240ac6892 | # Dataset Card for "OxfordPets_facebook_opt_350m_LLM_Description_gpt3_downstream_tasks_ViT_L_14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_facebook_opt_350m_LLM_Description_gpt3_downstream_tasks_ViT_L_14 | [
"region:us"
]
| 2023-01-04T17:07:09+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 119984114.375, "num_examples": 3669}], "download_size": 119029045, "dataset_size": 119984114.375}} | 2023-01-04T17:07:27+00:00 |
973b6bfa3f5aa6fdf4ba70798c5183a46f19610a | Amala/bil | [
"license:unknown",
"region:us"
]
| 2023-01-04T17:21:31+00:00 | {"license": "unknown"} | 2023-01-04T18:01:55+00:00 |
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